Please use the multiple regression analysis below to answer question a-e. Variab
ID: 450937 • Letter: P
Question
Please use the multiple regression analysis below to answer question a-e.
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Importance: Specific Medical Concerns, number of visits, Age in Years, Importance: General Health and Fitness, Importance: Social Aspects, Importance: Physical Enjoymentb
.
Enter
a. Dependent Variable: Two-year Revenue ($)
b. All requested variables entered.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.440a
.193
.162
370.963
a. Predictors: (Constant), Importance: Specific Medical Concerns, number of visits, Age in Years, Importance: General Health and Fitness, Importance: Social Aspects, Importance: Physical Enjoyment
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
5139508.787
6
856584.798
6.225
.000b
Residual
21467704.210
156
137613.489
Total
26607212.990
162
a. Dependent Variable: Two-year Revenue ($)
b. Predictors: (Constant), Importance: Specific Medical Concerns, number of visits, Age in Years, Importance: General Health and Fitness, Importance: Social Aspects, Importance: Physical Enjoyment
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-235.236
275.130
-.855
.394
number of visits
11.226
4.244
.194
2.645
.009
Age in Years
9.977
2.466
.299
4.046
.000
Importance: General Health and Fitness
11.912
48.235
.019
.247
.805
Importance: Social Aspects
71.350
28.370
.223
2.515
.013
Importance: Physical Enjoyment
-53.173
35.260
-.140
-1.508
.134
Importance: Specific Medical Concerns
-1.387
24.303
-.004
-.057
.955
a. Dependent Variable: Two-year Revenue ($)
Type your answers to parts a-e in the below space.
a. Is the overall model statistically significant?
b. Which predictors are statistically significant?
c. Interpret the unstandardized coefficient associated with age. What does this value mean?
d. What is the coefficient of multiple determination for the analysis? Has adding additional predictors to the model (beyond visit frequency only) improved the ability to explain variance in revenues?
e. Does it appear that multicollinearity might have been a problem in this analysis? Why or why not?
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Importance: Specific Medical Concerns, number of visits, Age in Years, Importance: General Health and Fitness, Importance: Social Aspects, Importance: Physical Enjoymentb
.
Enter
a. Dependent Variable: Two-year Revenue ($)
b. All requested variables entered.
Explanation / Answer
Answer-a Adjusted R-squared value = 0.193 which is less than as compared to other statisticall significant models. Therefore overall model is not statistically significant.
Answer-b P values of number of visits, age in years and importance of social aspects are statistically significant as their p values are less than 0.05
.
Answer-c The unstandardised coeff for the age = 9.977 thsi shows that for a unit change in independent variable there will be 9.97 time change in dependent variable.
Answer-d The coeffitient of determination is R squared which is equal to = 0.193 and shows that the model is not well deterministic.
Answer-e Yes multicolliniarity may be one of the problem associated with less value of R -squared.