Machine Learning & AI Foundations: Linear Regression
Posted by Superadmin on May 14 2023 06:23:54

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


01_001.Welcome

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


01_002.What you should know

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


01_003.Using the exercise files

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


02_001 Building effective scatter plots in Chart Builder

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


02_002 Adding labels and spikes to a scatter plot

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


02_003 Create a 3D scatter plot

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


02_004 Bubble chart with GPL

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


02_005 Residuals and R2

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


02_006 Calculating and interpreting regression coefficients

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_001 Challenges and assumptions of multiple regression

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_002 Checking assumptions visually

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_003 Checking assumptions with Explore

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_004 Checking assumptions Durbin-Watson

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_005 Checking assumptions Levines test

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_006 Checking assumptions Correlation matrix

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_007 Checking assumptions Residuals plot

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


03_008 Checking assumptions Summary

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


04_001 Creating dummy codes

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


04_002 Dummy coding with the R extension

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


04_003 Detecting variable interactions

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


04_004 Creating and testing interaction terms

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_001 Three regression strategies and when to use them

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_002 Understanding partial correlations

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_003 Understanding part correlations

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_004 Visualizing part and partial correlations

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_005 Simultaneous regression Setting up the analysis

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_006 Simultaneous regression Interpreting the output

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_007 Hierarchical regression Setting up the analysis

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_008 Hierarchical regression Interpreting the output

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_009 Creating a train-test partition in SPSS

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_010 Stepwise regression Setting up the analysis

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


05_011 Stepwise regression Interpreting the output

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


06_001 Collinearity diagnostics

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


06_002 Dealing with multicollinearity Factor analysisPCA

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


06_003 Dealing with multicollinearity Manually combine IVs

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


06_004 Diagnosing outliers and influential points

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


06_005 Dealing with outliers Studentized deleted residuals

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


06_006 Dealing with outliers Should cases be removed

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


06_007 Detecting curvilinearity

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_001 Regression options

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_002 Automatic linear modeling

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_003 Regression trees

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_004 Time series forecasting

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_005 Categorical regression with optimal scaling

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_006 Comparing regression to Neural Nets

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_007 Logistic regression

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


07_008 SEM

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


08_001 Whats next

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


Ex_Files_MachineLearning_AI_Linear_Regression.zip

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression