solution
Part i)
the multiple linear regression equation
"\\hat y_i = \\beta_0+\\beta_1x_i+\\beta_2x_i"
From our data,
Using Excel to fit the linear regression model
Step 1. The data is entered in Excel spreadsheet.
Sales are in cells A1:A7
no of promotions are in cells B1:B7
prices are in cells C1:C7
The first row of each column contains the column labels
Step 2. On the Data tab, click on Data Analysis
Step 3. On the pop-up menu, select Regression
step 4. Input Y Range as $A$1:$A$7
step 5. Input X Range as $B$1:$C$7
Step 6. Ensure the Labels is marked
Step 7. on Output Range, enter Cell $A$11
Finally, click OK.
The fitted regression parameters are displayed from cell $A$11
Parameters
"\\beta_0 = 16.0189, \\beta_1= 0.1273\\ and \\ \\beta_2 = -0.4790"
Answer:
The regression equation becomes:
Part ii)
When "Price = 40" and "promotions = 10"
"= -1.869\\ million"
answer: the model suggests there will be a loss of 1,869,000 when price is 40 and the number of promotions are 10
Comments
Thank you very much well received
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