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DEMO - Excel Data Analysis: Forecasting
DEMO - Excel Data Analysis: ForecastingAnalytics Courses |
| Categories | Most Recent | Top Rated | Popular Courses |
 | Uploader | Date Added | Views | Rating |
| Superadmin | 15.03.16 | 1,036 | No Rating | |
| Description | ||||
| Professor Wayne Winston has taught advanced forecasting techniques to Fortune 500 companies for more than twenty years. In this course, he shows how to use Excel's data-analysis tools—including charts, formulas, and functions—to create accurate and insightful forecasts. Learn how to display time-series data visually; make sure your forecasts are accurate, by computing for errors and bias; use trendlines to identify trends and outlier data; model growth; account for seasonality; and identify unknown variables, with multiple regression analysis. A series of practice challenges along the way helps you test your skills and compare your work to Wayne's solutions. Table of Contents Introduction 3m 3s 1. Visually Displaying Your Time-Series Data 16m 56s What is time-series data? 1m 10s Plotting a time series 1m 45s Understanding level in a time series 2m 26s Understanding trend in a time series 1m 25s Understanding seasonality in a time series 2m 34s Understanding noise in a time series 1m 36s Creating a moving average chart 3m 8s Challenge: Analyze time-series data for airline miles 27s Solution: Analyze time-series data for airline miles 2m 25s 2. How Good Are Your Forecasts? Errors, Accuracy, and Bias 29m 0s Exploring why some forecasts are better than others 4m 31s Computing the mean absolute deviation (MAD) 3m 54s Computing the mean absolute percentage error (MAPE) 5m 29s Calculating the sum of squared errors (SSE) 2m 39s Computing forecast bias 3m 21s Advanced forecast bias: Determining significance 3m 49s Challenge: Compute MAD, MAPE, and SSE for an NFL game 36s Solution: Compute MAD, MAPE, and SSE for an NFL game 4m 41s 3. Using a Trendline for Forecasting 28m 59s Fitting a linear trend curve 2m 55s Interpreting the trendline 1m 51s Interpreting the R-squared value 4m 41s Computing standard error of the regression and outliers 6m 10s Exploring autocorrelation 6m 56s Challenge: Create a trendline to analyze R squared and outliers 37s Solution: Create a trendline to analyze R squared and outliers 5m 49s 4. Modeling Exponential Growth and Compound Annual Growth Rate (CAGR) 17m 25s When does a linear trend fail? 5m 19s Creating an exponential trend curve 5m 31s Computing compound annual growth rate (CAGR) 2m 45s Challenge: Fit an exponential growth curve, estimate CAGR, and forecast revenue 41s Solution: Fit an exponential growth curve, estimate CAGR, and forecast revenue 3m 9s 5. Seasonality and the Ratio-to-Moving-Average Method 28m 22s What is a seasonal index? 4m 23s Introducing the ratio-to-moving-average method 1m 47s Computing the centered moving average 4m 7s Calculating seasonal indices 4m 22s Estimating a series trend 2m 4s Forecasting sales 5m 6s Forecasting if the series trend is changing 3m 8s Challenge: Predicting future quarterly sales 37s Solution: Predicting future quarterly sales 2m 48s 6. Forecasting with Multiple Regressions 1h 1m What is multiple regression? 6m 2s Preparing data for multiple regression 9m 7s Running a multiple linear regression 2m 32s Finding the multiple-regression equation and testing for significance 9m 15s How good is the fit of the trendline? 5m 51s Making forecasts from a multiple-regression equation 4m 17s Validating a multiple-regression equation using the TREND function 9m 24s Interpreting regression coefficients 4m 27s Challenge: Regression analysis of Amazon.com revenue 1m 24s Solution: Regression analysis of Amazon.com revenue 9m 13s Conclusion 1m 48s Next steps 1m 48s | ||||
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