Guide to Using Minitab 14 For Basic Statistical
Guide to Using Minitab 14 For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 8 th Ed. Chapter 16: Analyzing and Forecasting Time-Series Data By Groebner, Shannon, Fry, & Smith Prentice-Hall Publishing Company Copyright, 2011
Chapter 16 Minitab Examples n n Trend Based Forecasting Taft Ice Cream Company Nonlinear Trend Harrison Equipment Company Seasonal Adjustment Big Mountain Ski Resort Single Exponential Smoothing Dawson Graphic Designs More Examples
Chapter 16 Minitab Examples n Double Exponential Smoothing Billingsley Insurance Company
Trend Based Forecasting Taft Ice Cream Company Issue: The owners of Taft Ice Cream Company considering expanding their manufacturing facilities. The bank requires a forecast of future sales. Objective: Use Minitab to build a forecasting model based on 10 years of data. Data file is Taft. MTW
Trend Based Forecasting – Taft Ice Cream Company Open File Taft. MTW
Trend Based Forecasting – Taft Ice Cream Company First click on Graph, then Time Series Plot.
Trend Based Forecasting – Taft Ice Cream Company Select Simple
Trend Based Forecasting – Taft Ice Cream Company Enter data column to be graphed. Then click Time/Scale
Trend Based Forecasting – Taft Ice Cream Company Click on Calendar and specify Year – then determine starting Year (1997)
Trend Based Forecasting – Taft Ice Cream Company A linear trend is evident in this time series plot.
Trend Based Forecasting – Taft Ice Cream Company To develop the trend line, click on Stat, then Regression and Regression again
Trend Based Forecasting – Taft Ice Cream Company Identify the columns containing the Time Series and also specify the dependent variable (t)
Trend Based Forecasting – Taft Ice Cream Company Linear Trend Model
Trend Based Forecasting – Taft Ice Cream Company A second method - select Stat – Time Series – Trend Analysis
Trend Based Forecasting – Taft Ice Cream Company Specify time series variable and select Linear model type
Trend Based Forecasting – Taft Ice Cream Company Measures of forecast accuracy
Nonlinear Trend – Harrison Equipment Company Issue: Harrison Equipment is interested in forecasting future repair costs for a crawler tractor it leases to contractors. Objective: Use Minitab to develop a nonlinear forecasting model. Data file is Harrison. MTW
Nonlinear Trend – Harrison Equipment Company Open File Harrison. MTW
Nonlinear Trend – Harrison Equipment Company Select Graph – then Time Series Plot
Nonlinear Trend – Harrison Equipment Company Select Simple
Nonlinear Trend – Harrison Equipment Company Define Time Series variable (Repair Costs) and then select Time/Scale
Nonlinear Trend – Harrison Equipment Company Select Calendar and pick Quarter Year option Specify starting Quarter and Year
Nonlinear Trend – Harrison Equipment Company
Nonlinear Trend – Harrison Equipment Company To develop a linear model, click on Stat, then Time Series and finally Trend Analysis.
Nonlinear Trend – Harrison Equipment Company Specify time series variable (Repair Costs) and select Linear
Nonlinear Trend – Harrison Equipment Company Linear Model Results
Nonlinear Trend – Harrison Equipment Company To develop a model with time squared as the variable Click on Calc, then Calculator.
Nonlinear Trend – Harrison Equipment Company Identify column for new variable, in Expressions box enter form of new variable. Click OK
Nonlinear Trend – Harrison Equipment Company Click on Stat, then Regression and Regression again.
Nonlinear Trend – Harrison Equipment Company Define the Response variable (Repair Costs) Predictors (Qtr 2) then click Storage.
Nonlinear Trend – Harrison Equipment Company Under Diagnostic Measures select Residuals, under Characterist ics select Fits. Click OK twice.
Nonlinear Trend – Harrison Equipment Company The Minitab output shows the regression model.
Seasonal Adjustment Big Mountain Ski Resort Issue: The resort wants to build a forecasting model from data that has a definite seasonal component. Objective: Use Minitab to develop a forecasting model adjusting for seasonal data. Data file is Big Mountain. MTW
Seasonal Adjustment – Big Mountain Ski Resort Open File Big Mountain. MTW
Seasonal Adjustment – Big Mountain Ski Resort Click on Stat, then Time Series and then select Decomposition.
Seasonal Adjustment – Big Mountain Ski Resort Define the Variable, the Model Type, the Seasonal length and the Model Components.
Seasonal Adjustment – Big Mountain Ski Resort The graph shows the actual and predicted values.
Seasonal Adjustment – Big Mountain Ski Resort This output shows the original data and other graphs.
Seasonal Adjustment – Big Mountain Ski Resort The Trend Line Equation, the Seasonal Indices and MAPE, MAD and MSD are also given.
Single Exponential Smoothing Dawson Graphic Design Issue: The company needs to develop a forecasting model to forecast incoming customer calls so they are able to make informed future staffing decisions. Because the time series appears to be relatively stable, a relatively small smoothing constant will be used. Objective: Use Minitab to develop a single exponential smoothing forecasting model. Data file is Dawson. MTW
Single Exponential Smoothing – Dawson Graphic Design Open File Dawson. MTW
Single Exponential Smoothing – Dawson Graphic Design Click on Stat, then Time Series and finally Single Exponential Smoothing.
Single Exponential Smoothing – Dawson Graphic Design Identify the Time Series Variable. Either specify alpha or ask Minitab to optimize the forecasting model. Select Storage
Single Exponential Smoothing – Dawson Graphic Design Select Fits
Single Exponential Smoothing – Dawson Graphic Design The graph shows the actual and forecast values. The accuracy measures are also given.
Single Exponential Smoothing – Dawson Graphic Design To determine optimal alpha, Identify the Time Series Variable. Ask Minitab to optimize the forecasting model.
Single Exponential Smoothing – Dawson Graphic Design The graph shows the actual and forecast values. The accuracy measures and the optimum alpha are also given.
Double Exponential Smoothing Billingsley Insurance Issue: The claims manager has data for 12 months and wants to forecast claims for month 13. But the time series contains a strong upward trend Objective: Use Minitab to develop a double exponential smoothing model. Data file is Billingsley. MTW
Double Exponential Smoothing – Billingsley Insurance Open file Billingsley. MTW
Double Exponential Smoothing – Billingsley Insurance Click on Stat then Time Series and finally Double Exponential Smoothing.
Double Exponential Smoothing – Billingsley Insurance Identify the Time Series Variable. Either specify alpha and beta or ask Minitab to optimize the forecasting model.
Double Exponential Smoothing – Billingsley Insurance The graph shows the actual and forecast values. The accuracy measures are also given.
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