Q4. Perform and Interpret a Multiple Linear Regression

Here we are interested in how multiple predictors may be combined to predict Netsales even better. Specifically, we would like to build a regression model for Netsales with all the variables that are significantly correlated with Netsales.

Q4A.

Perform multiple linear regression according to the research question above and answer the following questions. Use "ENTER" (the default in SPSS) as the method of adding the predictor variables to the regression model. Report the omnibus F test result for the regression model, in APA format, including F, p, and adjusted R².

F( , ) = , p = , Adjusted R² =

Q4B. Based solely on the omnibus regression result reported above (Q4A), what is the most appropriate conclusion?

Group of answer choices

(a) All the predictors significantly predict the outcome variable.

(b) The model significantly predicts the outcome variable.

(c) At least one of the predictors significantly predicts the outcome variable.

(d) The model can account for all the variance in the outcome variable.

Q4C. Which variables are significant predictors in this regression module? (Select all the significant predictors.)

Group of answer choices

Area

Storesize

Competitor

Adcost

Q4D. Which statistic allows us to compare the simple regression and the multiple regression and see which model accounts for a higher percentage of variance in the outcome variable?

Group of answer choices

p

R

Adjusted R squared

Beta

Data:

Netsales	Storesize	Adcost	Area	Competitor
10.00	0.50	3.00	4.30	1
15.00	0.60	2.50	2.50	8
20.00	1.20	3.30	2.10	5
50.00	1.10	3.10	1.60	13
65.00	1.20	4.70	3.30	8
68.00	0.60	4.90	4.70	5
98.00	1.60	4.60	2.70	10
99.00	0.80	2.80	6.50	12
156.00	2.20	6.90	4.10	0
161.00	2.60	7.20	6.30	11
195.00	2.50	7.70	8.40	4
231.00	3.00	8.20	8.20	0
299.00	3.10	8.10	10.10	4
341.00	3.50	9.80	11.50	14
347.00	3.60	9.60	11.30	14
397.00	3.80	10.40	13.90	11
398.00	4.30	5.50	16.00	10
400.00	8.60	7.00	12.00	15
428.00	4.20	10.50	14.00	7
437.00	4.40	10.60	14.10	1
464.00	4.70	11.30	15.00	7
487.00	4.80	11.80	12.70	3
497.00	5.30	11.50	16.30	12
507.00	5.10	12.00	15.70	15
519.00	5.50	12.00	16.10	1
528.00	5.60	12.30	16.00	12
570.00	5.40	17.40	12.30	6