24 Sep Week 4 – Excel Problems
Complete the following problem in your textbook:
From Chapter 8: Problems: 15, 18, and 29
From Chapter 9: Problems: 4, 13, 21 and 23
All work should be submitted in Excel with one (1) problem per tab in a single workbook. Formulas should be used as opposed to outside or manual calculations. Use of Excel add-ins is encouraged.
Data files can be downloaded from
Chapter 8:
15. “The Excel file Concert Sales provides data on sales dollars and the number of radio, TV, and newspaper ads promoting the concerts for a group of cities. Develop simple linear regression models for predicting sales as a function of the number of each type of ad. Compare these results to a multiple linear regression model using both independent variables. Examine the residuals of the best model for regression assumptions and possible outliers.”
18. “The Excel file Salary Data provides information on current salary, beginning salary, previous experience (in months) when hired, and total years of education for a sample of 100 employees in a firm.
a.) Develop a multiple regression model for predicting current salary as a function of the other variables.
b.) Find the best model for predicting current salary using the t-value criterion.”
29. See Attachment
Chapter 9:
4. “The Excel file Unemployment Rates provides data on monthly rates for 4 years. Compare 3- and 12-month moving average forecasts using the MAD criterion. Explain why the 3-month model yields better results.”
13. “Use the Holt-Winters no-trend model to find the best model to forecast the next year of electricity usage in the Excel file Gas & Electric.”
21. “Choose an appropriate forecasting technique for the data in the Excel file Prime Rate and find the best forecasting model. Explain how you would use the model to forecast and how far into the future it would be appropriate to forecast.”
23. “Data in the Excel File Microprocessor Data shows the demand for one type of chip used in industrial equipment from a small manufacturer.
a.) Construct a chart of the data. What appears to happen when a new chip is introduced?
b.) Develop a causal regression model to forecast demand that includes both time and the introduction of a new chip as explanatory variables.
c.)What would the forecast be for the next month if a new chip is introduced? What would it be if a new chip is not introduced?”
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