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The questions is based on a data set that i attached to this also. it involves using SPSS inputFallB2020
M6 Assignment (35 points)
Instruction:
1. Review the Assignment Guides, which contains tutorials on how to show the math/process work
2. Use this Word document to fill in the answers to the questions. You must type out a clear answer
to each question, even if the answer is also contained in the SPSS output.
3. Download the Excel file for this assignment and use that data set to answer all the questions in
this assignment. Import the data into SPSS if instructed to do so.
4. Use  = .05 for all hypothesis testing unless otherwise specified.
Q1. Perform and Interpret a Bivariate Linear Regression (9 points total)
Q1Q2 data set contains cognitive function scores and demographics from a group of older adults. The
focus of this question set is the executive function (Executive). Create an SPSS data file by importing the
Q1Q2 data. Configure the “measure” for each variable.
A . Perform exploratory bivariate correlations in SPSS.
1. Perform bivariate (Pearson’s) correlations among all the variables in the data set. Paste the
correlation matrix (table) here. Hint: Enter all the variables in the same correlation analysis. (1 point for
the table)
2. Report the correlation result in APA format (including r and p) for each of the following pairs of
variables: (1.5 points: .5 for each correlation, both r and p must be correct to earn .5 point)
Education and Executive:
MMSE and Executive:
Age and Executive:
B. Perform a bivariate (simple) linear regression.
The regression model should contain the following:
Outcome variable – Executive
Predictor – Variable with the strongest correlation with Executive (regardless of direction)
1. Create a scatter plot between the predictor variable (X axis) and outcome variable (Y axis). Make sure
the scatter plot has labels for the X and Y axes. Paste the scatter plot here.
(1 point: Deduct .5 for each error up to a total of 1.)
2. Perform the bivariate regression analysis in SPSS. Report the omnibus test result in APA style on the
regression model, including F, p, and adjusted R2. Be sure to paste the relevant output tables (Model
Summary and ANOVA tables) here to support your answer. (1.5 points: .5 for each error in value or format
up to 1.5 total. No credit is earned if no table is pasted.)
FallB2020
3. Discuss the regression result. Is the null hypothesis rejected? What does the result mean?
Hint: Think about the null hypothesis being tested here and form your answer based on whether the null hypothesis
is rejected or not.
(1 point: .5 for each answer)
4. Report the coefficient test on the predictor variable in APA format, including , t and p. Be sure to
paste the relevant output table (Coefficients table) if they have not been pasted above.
(1 point: .5 for any error in value or format, up to 1 total. No point is earned if the table is not included here)
5. Explain the coefficient test result. Is the null hypothesis rejected? What does that mean?
(1 point: .5 for each answer)
6. How much of the variance in the outcome variable can be predicted by the predictor variable? (1
point)
Q2. Perform and Interpret a Multiple Linear Regression (8 points total)
In Q1 above, we examined the relationship between executive function and one predictor variable. Here
we are interested in how multiple predictors may be combined to predict executive function even
better. Specifically, we would like to build a regression model for executive function with two predictor
variables that have the highest and second highest correlations with Executive.
A. Perform a multiple linear regression with Executive as the outcome variable and the two variables
with the highest and second highest correlations with Executive as the predictors in the regression
model. Use “ENTER” (the default in SPSS) as the method of adding the predictor variables to the
regression model.
1. Report the omnibus F test result for the regression model, in APA format, including F, p, and adjusted
R2. Be sure to paste the relevant tables (Model Summary and ANOVA tables) here to support your
answers. (1.5 points total: .5 for any error in value or format up to 1.5 total. No credit is earned if no relevant
table is pasted.)
2. Interpret the test result by answer the following questions: (2 points total: .5 for each question)
a. What was the null hypothesis (in words) tested by this multiple regression analysis?
b. What was the hypothesis test result? (Do you reject or fail to reject the null hypothesis?)
c. What is the effect size of this regression model?
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d. What does the effect size mean? The answer should clearly indicate the predictor variables and the
outcome variable. Hint: Think about the extent that the predictors can collectively predict the outcome
variable.
3. Report the statistics on each predictor variable in APA format, including , t, and p. Paste the relevant
(2 points total. .5 for each error in value or format, up to 2 total. No point is earned if the relevant table is not
pasted here.)
4. Discuss the relative contributions of the predictors in the model. (1.5 points total: .5 for each question)
a. Which is the strongest predictor?
b. How did you know which predictor is the strongest?
c. Is the strongest predictor statistically significant?
B. Compare this current multiple regression model with the bivariate (simple) regression model in Q1.
Does the model with two predictors predict the outcome variable better than the model with only one
predictor? How do you know? (1 point. .5 for each answer.)
Q3. Perform and Interpret a Bivariate Linear Regression (9.5 points total)
This analysis will be performed on a data set collected by a national company on their 27 chain stores.
The marketing department would like to know how various factors contribute to the net sales (Netsales)
amount for a chain store. Create an SPSS data file by importing the Q3Q4 data. Configure the “measure”
for each variable.
A . Perform exploratory bivariate correlations in SPSS.
1. Perform bivariate (Pearson’s) correlations among all the variables in the data set. Paste the
correlation matrix (table) here. Hint: Enter all the variables in the same correlation analysis. (1 point for
the table)
2. Report the correlation result in APA format (including r and p) for each of the following pairs of
variables: (2 points: .5 for each correlation, both r and p must be correct to earn .5 point)
Storesize and Netsales:
FallB2020
Area and Netsales:
Competitor and Netsales:
B. Perform a bivariate (simple) linear regression.
The regression model should contain the following:
Outcome variable – Netsales
Predictor – Variable with the strongest correlation with Netsales (regardless of direction)
1. Create a scatter plot between the predictor variable (X axis) and outcome variable (Y axis). Make sure
the scatter plot has labels for the X and Y axes. Paste the scatter plot here.
(1 point: Deduct .5 for each error up to a total of 1.)
2. Perform the bivariate regression analysis in SPSS. Report the omnibus test result in APA style on the
regression model, including F, p, and adjusted R2. Be sure to paste the relevant output tables (Model
Summary and ANOVA tables) here to support your answer. (1.5 points: .5 for each statistic, both value and
APA format must be correct to earn the credit for each statistic. No credit is earned if no table is pasted.)
3. Discuss the regression result. Is the null hypothesis rejected? What does the result mean?
Hint: Think about the null hypothesis being tested here and form your answer based on whether the null hypothesis
is rejected or not.
(1 point: .5 for each answer)
4. Report the coefficient test on the predictor variable in APA format, including t and p. Be sure to paste
the relevant output table (Coefficients table) if they have not been pasted above.
(1 point: .5 for each statistic, both value and APA format must be correct to earn the credit for each statistic)
5. Explain the coefficient test result. Is the null hypothesis rejected? What does that mean?
(1 point: .5 for each answer)
6. How much of the variance in the outcome variable can be predicted by the predictor variable? (1
point)
Q4. Perform and Interpret a Multiple Linear Regression (8.5 points total)
In Q3 above, we examined the relationship between Netsales and one predictor variable. Here we are
interested in how multiple predictors may be combined to predict Netsales even better. Specifically, we
FallB2020
would like to build a regression model for Netsales with four predictor variables: Storesize, Adcost, Area,
Competitor.
A. Perform a 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.
1. Report the omnibus F test result for the regression model, in APA format, including F, p, and adjusted
R2. Be sure to paste the relevant tables (Module Summary and ANOVA tables) here to support your
(1.5 points total. .5 for each statistic. Both value and format must be correct to earn the point. No credit is earned
if no relevant table is pasted.)
2. Interpret the test result by answer the following questions: (2 points total: .5 for each question)
a. What was the null hypothesis (in words) tested by this multiple regression analysis?
b. What was the hypothesis test result? (Do you reject or fail to reject the null hypothesis?)
c. What is the effect size of this regression model?
d. What does the effect size mean? The answer should clearly indicate the predictor variables and the
outcome variable. Hint: Think about the extent that the predictors can collectively predict the outcome
variable.
3. Report the statistics on each predictor variable in APA format, including , t, and p. Paste the relevant
(2 points total. .5 for each error in value or format, up to 2 total. No point is earned if the relevant table is not
pasted here.)
4. Discuss the relative contributions of the predictors in the model. (2 points total: .5 for each question)
a. Which is the strongest predictor?
b. Which is the weakest predictor?
c. How do you determine the strength of each predictor?
d. Which variables are significant?
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B. Compare this current multiple regression model with the bivariate regression model in Q3. Does the
model with four predictors predict the outcome variable better than the model with only one predictor?
How do you know? (1 point. .5 for each answer.)
Sub ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Education MMSE
Age
16
28
12
28
13
29
8
29
12
28
12
29
16
30
13
28
12
26
14
26
13
29
17
30
12
29
16
28
14
26
20
28
15
28
16
30
16
29
18
29
12
29
19
28
14
30
16
30
19
28
13
28
18
30
16
28
18
27
16
30
20
30
16
30
20
29
18
30
16
30
12
29
16
30
18
30
16
30
14
29
65.8
66.9
76.9
79.9
84.1
72.6
75.6
79.6
78.0
66.3
65.1
67.2
69.0
78.9
69.4
73.6
78.4
69.6
65.8
69.5
75.4
66.7
65.5
72.8
76.5
72.9
69.7
68.2
65.7
73.4
65.8
66.0
71.4
75.8
68.1
67.5
67.8
73.0
71.9
68.3
Executive
84.44
79.88
80.11
100.42
97.59
89.75
108.95
100.00
100.05
97.63
95.63
98.52
103.15
89.73
91.22
93.71
96.87
101.35
97.42
110.68
91.38
105.13
102.57
102.72
107.08
96.14
95.35
104.15
104.76
106.80
100.89
110.68
111.41
114.54
117.27
123.48
116.20
108.14
117.73
93.19
Store ID
Netsales
1
2
3
40
4
5
6
28
30
7
8
29
31
9
32
10
33
11
34
12
35
13
37
14
36
15
39
38
16
17
18
19
20
21
22
23
24
25
26
27
Storesize
10
15
20
20
50
65
68
69
96
98
99
99
155
156
160
161
192
195
230
231
288
299
330
341
342
347
377
387
397
398
400
428
437
464
487
497
507
519
528
570
0.5
0.6
1.2
0.8
1.1
1.2
0.6
0.5
0.9
1.6
0.8
1.5
2.3
2.2
2.5
2.6
2.7
2.5
3.5
3
3.2
3.1
3.4
3.5
3.3
3.6
4.1
3.9
3.8
4.3
8.6
4.2
4.4
4.7
4.8
5.3
5.1
5.5
5.6
5.4
Area
3
2.5
3.3
3
3.1
4.7
4.9
4.5
2.9
4.6
2.8
4.4
6.2
6.9
7
7.2
7.5
7.7
8.1
8.2
8.1
8.1
9.6
9.8
9.6
9.6
7.5
10.1
10.4
5.5
7
10.5
10.6
11.3
11.8
11.5
12
12
12.3
17.4
Competitor
9
8
11
6.3
8.1
3.3
4.7
5.2
6.6
2.7
6.5
3.1
4.3
4.1
6.5
6.3
8.1
8.4
5.4
8.2
10
7.7
12
11.5
11.2
8.1
8.4
8.5
8.2
10
12
14
8.8
11
10
16.3
15.7
16.1
16
12.3
1
8
5
0
13
8
5
3
9
10
12
8
2
0
12
11
5
4
2
0
6
4
13
14
12
14
10
11
11
10
15
7
1
7
3
12
15
1
12
6
Q1 Data Set
Variable Name
Education
Description
Number of years of schooling
Values
Number of years
MMSE
Mini Mental Status Exam
Test score
Age
Age of participant
Number of years
Executive
Executive function
Test score
Variable
Netsales
Description
annual net sale amount (in thousands)
Value
dollar amount in thousands
Storesize
square footage of the store
# of square feet
dollar amount in thousands
Area
size of the area served by the store
# of square miles
Competitor
number of competitors in the area served by #the
of store
stores
Q2 Data Set

attachment

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