The number of auto accidents in Athens, Ohio is related to the regional number of regisgtered automobiles in thousands (X1) alcholic beverage sales in $10,000 (X2) and rainfall in inches (X3). Furthermore the regression formula has been calculated as: y=a+b1X1+b2x2+b3X3 where y= number of automobile accidents a= 8.0 b1= 3.5 b2=5.0 b3= 2.4 For the given values of X1,X2, and X3, the expected number of accidents will be ( round your responses to one decimal place) X1 X2 X3 # of accidents 6.0 7.0 0.0 [__]

The number of auto accidents in Athens, Ohio is related to the regional number of regisgtered automobiles in thousands (X1) alcholic beverage sales in $10,000 (X2) and rainfall in inches (X3). Furthermore the regression formula has been calculated as: y=a+b1X1+b2x2+b3X3 where y= number of automobile accidents a= 8.0 b1= 3.5 b2=5.0 b3= 2.4 For the given values of X1,X2, and X3, the expected number of accidents will be ( round your responses to one decimal place) X1 X2 X3 # of accidents 6.0 7.0 0.0 [__]

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.4: Multiple Regression Models

Problem 17P: The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He...

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The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
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