01 - Introduction
02 - 1. Visually Displaying Your Time-Series Data
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01 - What is time-series data (1:10)
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02 - Plotting a time series (1:45)
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03 - Understanding level in a time series (2:26)
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04 - Understanding trend in a time series (1:25)
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05 - Understanding seasonality in a time series (2:35)
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06 - Understanding noise in a time series (1:36)
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07 - Creating a moving average chart (3:09)
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08 - Challenge Analyze time-series data for airline miles (0:27)
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09 - Solution Analyze time-series data for airline miles (2:25)
03 - 2. How Good Are Your Forecasts Errors, Accuracy, and Bias
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02 - Computing the mean absolute deviation (MAD) (3:54)
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01 - Exploring why some forecasts are better than others (4:31)
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03 - Computing the mean absolute percentage error (MAPE) (5:30)
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04 - Calculating the sum of squared errors (SSE) (2:39)
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05 - Computing forecast bias (3:21)
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06 - Advanced forecast bias Determining significance (3:49)
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07 - Challenge Compute MAD, MAPE, and SSE for an NFL game (0:36)
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08 - Solution Compute MAD, MAPE, and SSE for an NFL game (4:41)
04 - 3. Using a Trendline for Forecasting
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02 - Interpreting the trendline (1:51)
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01 - Fitting a linear trend curve (2:55)
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03 - Interpreting the R-squared value (4:41)
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04 - Computing standard error of the regression and outliers (6:10)
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05 - Exploring autocorrelation (6:56)
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06 - Challenge Create a trendline to analyze R squared and outliers (0:37)
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07 - Solution Create a trendline to analyze R squared and outliers (5:49)
05 - 4. Modeling Exponential Growth and Compound Annual Growth Rate (CAGR)
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02 - Creating an exponential trend curve (5:31)
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01 - When does a linear trend fail (5:19)
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03 - Computing compound annual growth rate (CAGR) (2:45)
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04 - Challenge Fit an exponential growth curve, estimate CAGR, and forecast revenue (0:41)
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05 - Solution Fit an exponential growth curve, estimate CAGR, and forecast revenue (3:09)
06 - 5. Seasonality and the Ratio-to-Moving-Average Method
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01 - What is a seasonal index (4:23)
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02 - Introducing the ratio-to-moving-average method (1:47)
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03 - Computing the centered moving average (4:07)
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04 - Calculating seasonal indices (4:22)
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05 - Estimating a series trend (2:04)
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06 - Forecasting sales (5:06)
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07 - Forecasting if the series trend is changing (3:08)
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08 - Challenge Predicting future quarterly sales (0:37)
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09 - Solution Predicting future quarterly sales (2:48)
07 - 6. Forecasting with Multiple Regressions
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01 - What is multiple regression (6:02)
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02 - Preparing data for multiple regression (9:07)
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03 - Running a multiple linear regression (2:32)
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04 - Finding the multiple-regression equation and testing for significance (9:15)
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05 - How good is the fit of the trendline (5:52)
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06 - Making forecasts from a multiple-regression equation (4:17)
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07 - Validating a multiple-regression equation using the TREND function (9:24)
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08 - Interpreting regression coefficients (4:27)
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09 - Challenge Regression analysis of Amazon.com revenue (1:24)
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10 - Solution Regression analysis of Amazon.com revenue (9:13)
08 - Conclusion
Ex_Files_ExcelDataAnalysis_Forecst
