Overview
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Use Excel's data-analysis tools to create accurate and insightful forecasts.
Syllabus
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Introduction
- Welcome
- Who is this course for?
- What you should know before watching this course
- Using the exercise files
- Using the challenges
- What is time-series data?
- Plotting a time series
- Understanding level in a time series
- Understanding trend in a time series
- Understanding seasonality in a time series
- Understanding noise in a time series
- Creating a moving average chart
- Challenge: Analyze time-series data for airline miles
- Solution: Analyze time-series data for airline miles
- Exploring why some forecasts are better than others
- Computing the mean absolute deviation (MAD)
- Computing the mean absolute percentage error (MAPE)
- Calculating the sum of squared errors (SSE)
- Computing forecast bias
- Advanced forecast bias: Determining significance
- Challenge: Compute MAD, MAPE, and SSE for an NFL game
- Solution: Compute MAD, MAPE, and SSE for an NFL game
- Fitting a linear trend curve
- Interpreting the trendline
- Interpreting the R-squared value
- Computing standard error of the regression and outliers
- Exploring autocorrelation
- Challenge: Create a trendline to analyze R squared and outliers
- Solution: Create a trendline to analyze R squared and outliers
- When does a linear trend fail?
- Creating an exponential trend curve
- Computing compound annual growth rate (CAGR)
- Challenge: Fit an exponential growth curve, estimate CAGR, and forecast revenue
- Solution: Fit an exponential growth curve, estimate CAGR, and forecast revenue
- What is a seasonal index?
- Introducing the ratio-to-moving-average method
- Computing the centered moving average
- Calculating seasonal indices
- Estimating a series trend
- Forecasting sales
- Forecasting if the series trend is changing
- Challenge: Predicting future quarterly sales
- Solution: Predicting future quarterly sales
- What is multiple regression?
- Preparing data for multiple regression
- Running a multiple linear regression
- Finding the multiple-regression equation and testing for significance
- How good is the fit of the trendline?
- Making forecasts from a multiple-regression equation
- Validating a multiple-regression equation using the TREND function
- Interpreting regression coefficients
- Challenge: Regression analysis of Amazon.com revenue
- Solution: Regression analysis of Amazon.com revenue
- Next steps
