Teh marketing director collects 36 weekly observations on S,A, and R to estimate the following multiple regression equation.
S = a + bA + cR
where S, A, and R. are measured in dollars per week.
How well do you follow directions?
Your school subject is not Ashford University.
I doubt if your first name is alberta long.
Perhaps if you read your text more carefully, you'll figure out the answer to your question.
Cheat here:
http://www.justanswer.com/questions/1bvwy-director-marketing-vanguard
Now ask yourself, is getting the A on the assignment (and probably the course) worth anything?
To estimate the multiple regression equation, the marketing director collected 36 weekly observations on the variables S, A, and R. The objective is to find the equation that relates the dependent variable S (sales) to the independent variables A (advertising) and R (other relevant factors).
To estimate the equation, the director needs to perform the following steps:
1. Collect data: The director should gather data on the variables S, A, and R for the 36 weekly observations. Each observation should include the corresponding values for S (sales), A (advertising), and R (other relevant factors).
2. Calculate the regression coefficients: The director needs to estimate the regression coefficients a, b, and c. These coefficients represent the intercept (a) and the slope (b and c) of the regression line that relates S to A and R, respectively.
3. Estimate the equation: Once the regression coefficients are calculated, the director can estimate the multiple regression equation. The equation will be of the form: S = a + bA + cR, where S represents the dependent variable (sales) and A and R represent the independent variables (advertising and other factors).
4. Interpret the coefficients: After estimating the equation, the director should interpret the coefficients a, b, and c to understand their impact on sales. The intercept (a) represents the baseline sales when both A and R are zero. The coefficients b and c reflect the change in sales for every one-unit increase in A and R, respectively.
5. Assess the model's fit: Finally, it is important to assess the overall fit of the multiple regression model. This involves evaluating the statistical significance of the regression coefficients, checking for assumptions (such as linearity and homoscedasticity), and examining measures of goodness-of-fit (e.g., R-squared or adjusted R-squared).
By following these steps, the marketing director can estimate the multiple regression equation and gain insights into the relationship between sales (S) and advertising (A) and other relevant factors (R).