Sales Sales from last year in thousands of squares Expenditures Promotional expe
ID: 3132991 • Letter: S
Question
Sales
Sales from last year in thousands of squares
Expenditures
Promotional expenditures in thousands of dollars
Accounts
Number of active accounts
Competing brands
Number of competing brands producing equivalent or similar products
District potential
A coded indicator of the potential of the district
(higher score = better potential)
Which of the four explanatory variables seems to be the least significant in the model?
The data referred to in this question were collected from several sales districts across the country. The data represent sales for a maker of asphalt roofing shingles. Information on the following variables is available.Sales
Sales from last year in thousands of squares
Expenditures
Promotional expenditures in thousands of dollars
Accounts
Number of active accounts
Competing brands
Number of competing brands producing equivalent or similar products
District potential
A coded indicator of the potential of the district
(higher score = better potential)
Partial SPSS regression output of a multiple regression model with sales as the response variable and the other four variables as predictor variables are given below.
In an attempt to increase sales, the company can only directly influence some of these variables. It cannot change the number of competitors; it cannot change the district potential. The only two variables they can actively change are the number of active accounts and the promotional expenditures. Suppose they have $5000 to spend on new commercials (that is, promotional expenditures). By how much are sales expected to increase?
Which of the four explanatory variables seems to be the least significant in the model?
Accounts Competing brands Expenditures District potentialExplanation / Answer
a) Since B (unstandardised value) for promotional affairs is 1.807
spending amount is $5000
Return amount we get is 5000*1.807
=9035
Option D
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b) District potential has least significance importance
because of less coeffcients compared to others.
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