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>Data
| Trident University |
| BUS
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| 0 |
: Business Analytics and Decision Making
| Module
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| 4 |
: SLP Template
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| FILL IN ALL CELLS THAT ARE HIGHLIGHTED IN YELLOW |
| Please remember to save this file with your last name in the file name. For example: BUS
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| 52 |
0 Module 4 SLP Template, Doe
| Name: |
| Annual Amount Spent on Organic Food |
Age |
Annual Income |
Number of People in Household |
Gender (0 = Male;
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| 1 |
= Female)
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| 7 |
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| 3 |
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| 48 |
77 |
109
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| 6 |
88
3 1
| 11
| 59 |
8
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| 47 |
109981 |
5 1
| 92
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| 24 |
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| 23 |
11
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| 21 |
| 39 |
4 1
| 1
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| 29 |
91
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| 38 |
11
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| 34 |
20
5 1
| 1
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| 65 |
| 56 |
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| 58 |
11
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| 41 |
01
5 0
| 11
| 51 |
5
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| 44 |
115100 |
5 0
| 10
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| 46 |
9
34
11
| 63 |
| 30 |
5 0
| 179
| 33 |
|
| 75 |
116339 |
6 0
| 181
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| 73 |
| 32 |
117907 |
7 0
| 12305 |
39
1190
| 71 |
5 1
| 9080 |
65
58
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| 60 |
3
5 1
| 9113 |
48
58
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| 62 |
3
4 1
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| 61 |
85
48
61
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| 57 |
9
2 1
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| 64 |
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| 70 |
49 |
62180 |
2 0
| 6000 |
57
62202 |
5 1
|
| 67 |
60
71
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| 68 |
041
2 0
| 8579 |
47
68
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| 40 |
7
4 1
| 7393 |
47
69618 |
3 1
| 8161 |
|
| 28 |
73079 |
4 0
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| 10800 |
63
75900 |
5 1
| 6160 |
24
77129 |
3 1
10800
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| 66 |
79618 |
6 0
| 8
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| 54 |
3
24
81131 |
4 0
| 17666 |
38
86246 |
6 1
| 1
| 26 |
44
54
89167 |
5 1
| 1
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| 43 |
08
28
89576 |
5 1
| 97
| 37 |
58
92296 |
4 0
| 13301 |
| 27 |
9
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| 36 |
14
5 1
| 18106 |
48
93954 |
6 1
| 11468 |
26
95937 |
5 1
| 9547 |
52
100846 |
4 0
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| 78 |
12
29
103276 |
2 1
| 1
| 55 |
21
75
104112 |
5 1
| 7598 |
| 45 |
105119 |
4 0
| 7783 |
| 74 |
1059
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| 25 |
3 1
| 17737 |
56
106084 |
6 0
| 7824 |
30
108616 |
3 1
| 6552 |
57
| 10903 |
8
2 0
| 11232 |
41
109585 |
5 1
| 6540 |
23
37834 |
5 0
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| 42 |
00
28
38940 |
5 0
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| 72 |
25
23
42145 |
5 0
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| 53 |
70
45
48677 |
5 1
| 4476 |
33
48997 |
4 1
| 2800 |
42
49058 |
1 1
| 7839 |
39
49609 |
4 1
| 3472 |
60
53279 |
1 0
| 8854 |
57
53917 |
5 0
| 8900 |
41
54716 |
5 0
| 12791 |
67
126306 |
5 0
| 12712 |
73
130893 |
5 1
| 13321 |
57
134488 |
5 1
| 8802 |
64
1
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| 35 |
711
4 0
| 14369 |
24
139701 |
5 1
| 7908 |
25
142014 |
4 1
| 17840 |
34
142857 |
6 0
| 15107 |
78
143182 |
5 1
| 12070 |
34
150987 |
5 1
| 6389 |
34
152041 |
2 1
| 6606 |
41
154702 |
3 0
| 6291 |
62
155552 |
1 1
| 7425 |
57
157329 |
3 0
| 11436 |
23
163794 |
5 1
| 7612 |
78
164108 |
4 0
| 7515 |
36
165851 |
4 0
| 13115 |
44
172497 |
5 1
| 11870 |
75
174458 |
5 0
| 8450 |
70
177517 |
4 1
| 16324 |
38
183779 |
5 0
| 9331 |
35
185111 |
4 0
| 9184 |
65
186467 |
4 0
| 16803 |
68
189137 |
6 0
| 10709 |
48
194351 |
5 1
| 14456 |
24
194380 |
5 0
| 16634 |
46
197358 |
5 0
| 12227 |
43
197400 |
5 1
| 13476 |
58
198650 |
5 0
| 14554 |
66
202859 |
5 1
| 9393 |
68
203591 |
4 1
| 14594 |
74
206216 |
5 1
| 6628 |
32
207679 |
2 0
| 11240 |
61
210498 |
5 0
| 13101 |
42
210678 |
5 1
| 14034 |
60
211249 |
5 0
| 17837 |
64
211961 |
6 1
| 7849 |
53
212851 |
2 1
| 10578 |
62
213035 |
5 1
| 11325 |
78
214457 |
5 0
| 7105 |
44
215442 |
2 0
| 16460 |
58
220178 |
5 1
| 8390 |
27
220403 |
3 1
| 14956 |
68
220893 |
5 1
10903 21
| 221223 |
4 1
| 12054 |
70
221498 |
5 1
| 11697 |
38
222618 |
5 1
| 12781 |
25
229072 |
5 1
| 17456 |
30
229685 |
6 1
| 12835 |
70
230228 |
5 1
| 13403 |
37
235617 |
5 0
| 15051 |
40
238087 |
5 0
| 14225 |
29
240768 |
5 0
| 11196 |
54
242529 |
4 0
| 11475 |
52
243765 |
5 1
| 5605 |
65
244625 |
2 0
| 9890 |
72
245208 |
4 1
| 13227 |
40
247648 |
5 0
| 11200 |
36
249805 |
4 1
| 9600 |
43
252033 |
4 0
| 15703 |
38
252812 |
5 1
| 6486 |
73
257143 |
1 1
| 9430 |
41
258167 |
4 1
| 7755 |
35
258640 |
2 1
| 8100 |
21
261020 |
3 1
| 14821 |
59
266223 |
5 1
| 10650 |
56
266269 |
5 1
| 12589 |
42
267565 |
5 1
| 11600 |
46
268380 |
4 1
| 13000 |
34
269431 |
4 1
| 17065 |
70
269839 |
6 0
| 16500 |
55
270441 |
5 0
| 8600 |
38
272795 |
2 0
| 11900 |
51
274846 |
4 1
| 16723 |
66
276250 |
5 0
| 16759 |
43
277231 |
5 0
Question 1
FILL IN ALL CELLS THAT ARE HIGHLIGHTED IN YELLOW
| QUESTION 1: Compare the coefficients of determination (r-squared values) from the three linear regressions: simple linear regression from Module 3 Case, multivariate regression from Module 4 Case, and the second multivariate regression with the logged values from Module 4 Case. Which model had the “best fit?” |
| R-squared from Module 3 Simple Linear Regression: |
| Adjusted R-squared from Module 4 Multivariate Linear Regression: |
| Adjusted R-squared from Module 4 Multivariate Regression with Logged Values: |
| Which model has the “best fit?” Recall: The coefficient of determination indicates how much of variation in the dependent variable we have explained in the model. |
Questions 2-5
FILL IN ALL CELLS THAT ARE HIGHLIGHTED IN YELLOW
| QUESTION 2: Calculate the residual for the first observation from the simple linear regression model. Recall, the Residual = Observed value – Predicted value or e = y – ŷ. |
| Observed value of y for the first observation from the dataset: |
| Predicted value of y for the first observation (Hint: To find this, substitute the actual value of x for the first observation into the regression equation and solve for y): |
| Residual: |
0
| QUESTION 3: What happens to the overall distance between the best fit line and the coordinates in the scatterplot when the residuals shrink? |
| QUESTION 4: What happens to the coefficient of determination when the residuals shrink? |
| QUESTION 5: Consider the r-squared from the linear regression model and the r-squared from the first multivariate regression model. Why did the coefficient of determination change when more variables were added to the model? |
MODULE 4, BUS 520
The primary resource for this module is Introductory Business Statistics, by Alexander, Illowsky, and Dean.
Alexander, H., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. Openstax. Retrieved from
https://openstax.org/details/books/introductory-business-statistics
Include APA citations from this resource
ASSIGNMENT 1
Module 4 – Case
MULTIVARIATE ESTIMATION AND MODEL FIT
Assignment Overview
You are a consultant who works for the Diligent Consulting Group. In this Case, you are engaged on a consulting basis by Loving Organic Foods. In order to get a better idea of what might have motivated customers’ buying habits you are asked to analyze the factors that impact organic food expenditures. You performed a simple linear regression analysis in the Module 3 Case. Now, you are adding a layer of complexity to that analysis and including more independent variables in your model.
(Mod3 Case is attached Case_3_Excel & Case_3_Word)
Case Assignment
Using Excel, generate regression estimates for the following model:
Annual Amount Spent on Organic Food = α + b1Age + b2AnnualIncome
+ b3Number of People in Household + b4Gender
After you have reviewed the results from the estimation, write a report to your boss that interprets the results that you obtained. Please include the following in your report:
1. The regression output you generated in Excel.
2. Your interpretation of the coefficient of determination (r-squared).
3. Your interpretation of the global test for statistical significance (the F-test).
4. Your interpretation of the coefficient estimates for all the independent variables.
5. Your interpretation of the statistical significance of the coefficient estimates for all the independent variables.
6. The regression equation with estimates substituted into the equation. (Note: Once the estimates are substituted into the regression equation, it should take a form similar to this: y = 10 +2×1 +1×2 +4×3 +0.9×4)
7. An estimate of “Annual Amount Spent on Organic Food” for the average consumer. (Note: You will need to substitute the averages for all the independent variables into the regression equation for x, the intercept for α, and solve for y.)
8. A discussion of whether or not the coefficient estimate on the Age variable in this estimation is different than it was in the simple linear regression model from Module 3 Case. Be sure to explain why it did/did not change.
9. You decide you want to generate an elasticity coefficient, so you log the following variables in Excel: Annual Amount Spent on Organic Food, Annual Income.
10. Using Excel, generate regression estimates for the following model:
Log(Annual Amount Spent on Organic Food) = α +b1Age + b2Log(AnnualIncome)
+ b3Number of People in Household + b4Gender
11. Your interpretation of the coefficient estimate for Log(AnnualIncome).
12. Your interpretation of the coefficient of determination (r-squared) for this new model.
Data: Download the Excel-based data file:
BUS520 Module 4 Case
.
(File is attached)
Assignment Expectations
Written Report
Length requirements: 3–4 pages minimum (not including Cover and Reference pages). Note: You must submit 3–4 pages of written discussion and analysis.
Provide a brief introduction to/background of the problem, similar to the introduction/background you provided in Module 1 through 3 Case submissions.
Provide a brief comparison of simple linear regression and multiple linear regression.
Provide a written analysis that addresses each of requirements listed under the “Case Assignment” section.
Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size.
Please use keywords as headings to organize the report.
Avoid redundancy and general statements such as “All organizations exist to make a profit.” Make every sentence count.
Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words.
Upload both your written report and Excel file to the Case 4 Dropbox.
Assume once again that you are a consultant who works for the Diligent Consulting Group. You are continuing to work on the analysis of the customer database from Modules 1 through 3.
ASSIGNMENT 2
SLP Assignment Expectations
Complete the following tasks in the
Module 4 SLP
assignment template:
1. Compare the coefficients of determination (r-squared values) from the three linear regressions: simple linear regression from Module 3 Case, multivariate regression from Module 4 Case, and the second multivariate regression with the logged values from Module 4 Case. Which model had the “best fit”?
2. Calculate the residual for the first observation from the simple linear regression model. Recall, the Residual = Observed value – Predicted value or e = y – ŷ.
3. What happens to the overall distance between the best fit line and the coordinates in the scatterplot when the residuals shrink?
4. What happens to the coefficient of determination when the residuals shrink?
5. Consider the r-squared from the linear regression model and the r-squared from the first multivariate regression model. Why did the coefficient of determination change when more variables were added to the model?
