Fitting of multiple linear regression equation.
The revenues gained from sales of books from an e-Book supplier, the book price and advertising development costs were recorded for a randomly selected week. Treating sales revenue as the dependent variable with price (X1) and advertising development costs (X2) as the predictor variables, a multiple regression model as fitted to these data. An interaction term between X1 and X2 was also included.
a) Expalin why do you believe that states be treated as the dependent variable, with price and advertising development costs as predictor variables.
b) Expalin what the interaction term (X1 * X2) represents in this model.
c) The output from regression sales revenue onto price (X1), advertising development costs (X2) and an interaction term (X1 * X2) are given in table. The values for the t-statistic for the intercept term (t1), the standard error for price (SE1), P-value for advertising developmemt costs (P2) and net regression coefficient for the interaction term (β1*2) are absent. Calculate these.
d) Provide a list of the model assumptions for a regression model.
e) Assuming that all model assumptions are satisfied. Provide your model for this regression based on the P-values present in table. You must explain what each term represents.
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Predictors
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Regression
Coefficient
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Standard error
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t-Statictic
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P - value
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Intercept
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87.5
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175.9
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t1
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0.630
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X1
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5.534
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SE1
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3.09
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0.011
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X2
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5.479
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1.606
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3.41
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P2
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X1 * X2
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β1*2
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0.0158
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0.19
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0.852
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