You are an owner of Carrefour supermarket. You have made feature advertisings fo
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Question
You are an owner of Carrefour supermarket. You have made feature advertisings for last three years. You want to know the effectiveness of this feature advertising on store traffic(numbers of shoppers) in different week. In data set, you have: average numbers of shoppers, average numbers of feature advertising, and average price each week.
With the tables bellow it was done a REGRESSION model in Excel and you should interpret the results obtained from the equation based on the questions.
Q1. Consider a regression model (Model I) that has feature advertising as a single independent variable with intercept. Estimate your model and interpret your estimation results.
Q2. Update above regression model (Model II) by adding an additional independent variable. average price in order to capture the effect of price promotion activities such as coupon during week. Estimate your model and interpret your estimation results. Do you think which model makes more sense between Model I and Model II? Why?
Regression Statistics Multiple R 0,53969388 R Square 0,29126948 Adjusted R Square 0,28666733 Standard Error 205,827509 Observations 156 ANOVA df SS MS F Significance F Regression 1 2681275,289 2681275,3 63,28992304 3,59619E-13 Residual 154 6524204,403 42364,964 Total 155 9205479,692 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0% Intercept 609,046483 19,25202773 31,635446 2,91178E-69 571,0143324 647,0786342 571,0143324 647,0786342 feature 9,48205612 1,191887424 7,9554964 3,59619E-13 7,127496744 11,83661549 7,127496744 11,83661549Explanation / Answer
You are the owner of Carrefour supermarket.You have made feature advertising for last three years.you want to know the effectiveness of this feature advertising on store traffic(number of shoppers) in different weeks.
Let, Response variable y is average number of shoppers and independent variables are average number of feature advertising and average price each week.
Q1)
In question 1 Response variable y is average number of shoppers and independent variables are average number of feature advertising(x1).
The estimated regression model is,
Average number of shoppers(y^) = 609.046483 + 9.48205612 * feature advertising(x1)
Interpritation of Slope - If the average number of feature advertising(x1) increase by 1 unit then we predict that average number of shoppers will increase by approximately 9.48205612 number of shoppers.
Interpritation of Intercept - If the average number of feature advertising(x1) is 0 then we predict average number of shoppers are 609.046483
Interpritation - Average number of shoppers(y^) are linearly depend on the intercept(609.046483) of the model and slope(9.48205612) that is for any average number of feature advertising(x1) we predict Average number of shoppers using this model.
Q2)
In question 2 response variable y is average number of shoppers and independent variables are average number of feature advertising(x1) and average price each week(x2)
The updated estimated regression model is,
Average number of shoppers(y^) = 960.3924697 + 7.806303253 * feature advertising(x1) - 65.10617381 * average price each week(x2)
Interpritation of Slope - If the average number of feature advertising(x1) increase by 1 unit and average price each week(x2)is keeping constant then we predict that average number of shoppers will increase by approximately 7.806303253 number of shoppers and if the average number of feature advertising(x1) is keeps constant and average price each week(x2)is increases by one unit then we predict that average number of shoppers will decreases by approximately 65.10617381 number of shoppers.
Interpritation of Intercept - If the average number of feature advertising(x1) is 0 and average price each week(x2) is 0 then we predict average number of shoppers are 960.3924697
Interpritation - Average number of shoppers(y^) are linearly depend on the intercept(960.3924697) of the model and slope1(7.806303253) and slope2(- 65.10617381) that is for any average number of feature advertising(x1) and average price each week(x2) we predict Average number of shoppers using this model.
R Square for the model 1 is 0.29126948 and R Square for the model 2 is 0.300143756. R square for model 2 is quite larger than model 1 therefore Model II makes slightly more sense as compare to model I .
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