[Lynda.com] Excel Data Analysis: Forecasting [2014, ENG]

Содержание

Introduction
Welcome
Who is this course for?
What you should know before watching this course
Using the exercise files
Using the challenges
1. Visually Displaying Your Time-Series Data
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
2. How Good Are Your Forecasts? Errors, Accuracy, and Bias
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
3. Using a Trendline for Forecasting
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
4. Modeling Exponential Growth and Compound Annual Growth Rate (CAGR)
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
5. Seasonality and the Ratio-to-Moving-Average Method
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
6. Forecasting with Multiple Regressions
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
Conclusion
Next steps

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