SUMMARY OUTPUT 0.945 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.881 30 AN

[answerhappygod]

Summary Output 0 945 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0 881 30 An 1 (51.42 KiB) Viewed 31 times

Summary Output 0 945 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0 881 30 An 2 (37.13 KiB) Viewed 31 times

Summary Output 0 945 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0 881 30 An 3 (34.62 KiB) Viewed 31 times

Summary Output 0 945 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0 881 30 An 4 (34.72 KiB) Viewed 31 times

Summary Output 0 945 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0 881 30 An 5 (74.46 KiB) Viewed 31 times

SUMMARY OUTPUT 0.945 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.881 30 ANOVA df SS MS Significance F Regression Residual Total 1.432 13.459 Stat 3.104 P-value 0.005 Intercept Price IndPrice AdvExp Coefficients 7.589 -2.358 1.612 0.501 Standard Error 2.445 0.638 0.295 0.126 5.459 3.981 0.000 0.000

Question 3 Please use three decimal places. What is the F test statistic? O 72.890 O 4.009 O 0.055

Question 2 Please use three decimal points. 1. What is the SS for Regression? [Select ] 2. What is MS for Regression? [Select ] 3. What is MS for Residual? (Select ]

< 1. What is the df for total? [Select] > 2. What is the df for Regression? [Select ] > 3. What is the df for Residual? (Select]

The following is the partial printout of problem "The Fresh Detergent Case": Enterprise Industries produces Fresh, a brand of liquid laundry detergent. In order to manage its inventory more effectively and make revenue projections, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 30 sales periods (each sales period is defined to be a four- week period). Here are the variables that will be in the model: Y= the demand for the large size bottle of Fresh (in hundreds of thousands of bottles) in the sales period. X1=the price (in dollars) of Fresh as offered by Enterprise Industries in the Sales period. X2-the average industry price in dollars) of competitors' similar detergents in the sales period. X3=Enterprise Industries' advertising expenditure (in hundreds of thousands of dollars) to promote Fresh in the sales periods. The figure below provides the output of the multiple regression model from the Excel: