MBA 521 – Spring II 2022 – WebsterCase Analysis – 250 Points
**Before beginning, ensure you have joined a Team using the ‘Sign-Up’ link. You will work in teams
of 3.**
This project will give you the opportunity to use real data to answer a real macroeconomic
question. For example, you may decide to investigate the impact of the unemployment rate on
inflation or the cost of oil on the wage growth of manufacturing workers. Alternatively, you may
decide to assess the likelihood of an increase in interest rates to trigger a higher personal savings
rate.
You will be responsible for selecting one dependent variable (the Y) and four or five
independent variables (the X’s) for a specific industry (e.g., manufacturing, financial, retail,
etc.). You can find industry ideas at the North American Industry Classification System website:
https://www.naics.com/search/.
Steps for completing the data analysis
1. Download the FRED plugin into Excel from here: https://research.stlouisfed.org/fredaddin/. I will provide you with a tutorial on how to use the plugin to draw data directly into
Excel. Note that the FRED plugin is not currently supported by some versions of Excel. If
you cannot get the plugin to work, you will have to obtain data directly from the FRED
website instead.
2. Select a data series from FRED to serve as your dependent variable. Select four or five data
series from FRED to serve as your independent variables. Ensure the units of measurement
make sense and the frequencies of the data series match.
3. State the thesis and hypothesis tests (H0 and H1). What is being tested? What are the
expected findings?
4. Create a summary statistics table consisting of mean, median, standard deviation, and the
number of observations for each of your data series.
5. Run univariate regressions for each of your X’s independently. Create a table for each,
listing the beta-one coefficient, the p-value, and the overall ANOVA values.
6. Run a multivariate regression for all of your X’s concurrently. Create a single table listing
all the beta coefficients, their p-values, and the overall ANOVA values.
MBA 521 – Spring II 2022 – Webster
Case write-up instructions
In a 2–3-page document (double-spaced), discuss the findings of the statistics table (step 4) and
regression models (steps 5 and 6). Tie the models back to the original thesis and hypothesis tests
(step 3) and explain whether there is statistically significant evidence to support or reject the
hypotheses. What can be concluded from the results? What, if anything, could be changed about
the model or the thesis to create a more powerful experiment?
Include output from steps 4-6 in an appendix. Number each figure and/or table clearly, reference
them correctly in the written portion, and present them professionally (i.e., do not take screenshots
of Excel workbooks). Bonus points will be assigned to those appropriately implementing data
visualization techniques from Chapter 3 (not covered in the curriculum, but worth knowing) to
help explain the study and datasets. Any of these data visualizations should also be included in
an appendix and numbered and referenced appropriately.
Submission Instructions
Only one submission of the written analysis for each team should be made. Each team needs to
elect a representative to submit the finished product. The representative from each team will submit
by 11:59 PM on the Sunday of the 6th week by upload through the Case Study assignment link on
Blackboard. Each written submission needs to include all three members’ names on the cover
sheet and the name of the representative must be highlighted in BOLD to facilitate
crosschecking the figures presented in the representative’s cases with the case write-up.
Point Breakdown (250 possible)
Thesis and Hypothesis Tests: 25 points
Summary Statistics:
25 points
Univariate Regressions:
25 points
Multivariate Regression:
25 points
Conclusions:
150 points
World Health Organization Data Analysis:
Submitter: Trevor Hale
Hypothesis:
In this analysis I am going to be examining the relationship between life expectancy and 2 other
explanatory variables.
My initial hypotheses are as follows:
1. I believe that variable X1 has a negative/positive/no relationship with life expectancy.
2. I believe that variable X2 has a negative/positive/no relationship with life expectancy.
3. I believe that either X1 and X2 has a negative/positive/no relationship with life expectancy.
Summary Statistics:
Referring to figure 1 in our appendix:
•
•
•
Variable Y1:
o Include a sentence about the mean/median, then include a sentence about the
relationship between mean and median.
o Include a sentence about standard deviation.
o Include a sentence about the number of observations (if meaning ful).
Variable X1:
o Include a sentence about the mean/median, then include a sentence about the
relationship between mean and median.
o Include a sentence about standard deviation.
o Include a sentence about the number of observations (if meaning ful).
Variable X2:
o Include a sentence about the mean/median, then include a sentence about the
relationship between mean and median.
o Include a sentence about standard deviation.
o Include a sentence about the number of observations (if meaning ful).
Correlation Analysis:
Describe the overall findings from the bullet points below in a few sentences, specifically mention
what the implications are for your regression analyses. Refer to the appropriate figure in your
appendix, this should be labeled Figure 2.
•
•
•
Describe relationship between Y1 and X1 in a sentence.
Describe relationship between Y1 and X2 in a sentence.
Describe relationship between X1 and X2 in a sentence.
Univariate Analyses:
Describe the overall findings in a few sentences, you will need to run two univariate regression
analysis for this. Refer to the appropriate figure(s) these should be labeled Figure 3 and 4 respectively.
•
•
•
Describe the model(s) explanatory power in 2 sentences.
Describe the regression analysis between Y1 and X1 in a sentence.
Describe the regression analysis bet ween Y1 and X2 in a sentence.
Multivariate Analysis:
Describe the overall findings in a few sentences, you will need to run a multivariate regression
analysis for this. Refer to the appropriate figure this should be labeled Figure 5.
•
•
•
•
Describe the models explanatory power in a sentence
Describe the regression analysis between Y1 and X1 in a sentence.
Describe the regression analysis bet ween Y1 and X2 in a sentence.
Decribe the F test and it’s significance.
Conclusion:
Here you will describe what the World Health Organization can infer from your analysis. Is there a
meaningful relationship between the variables studied? If not that is also meaningful information that
should be shared. This should be no more than 3 sentences.
Appendix:
Figure 1:
Life
expectancy
69.3023
0.216629
71.7
73
8.796834
infant deaths
Alcohol
32.55306246
2.975954113
3
0
120.8471905
4.533196
0.099222
3.79
0.01
4.029189
77.38429
0.040321
-0.62876
45
44
89
114279.5
1649
14604.04345
85.31996879
8.477369412
1600
0
1600
53680
1649
16.23436
-0.5911
0.662518
17.86
0.01
17.87
7475.24
1649
Life expectancy
1
infant deaths
-0.169073804
1
0.402718322
-0.106216917
Mean
Standard Error
Median
Mode
Standard
Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Figure 2:
Life
expectancy
infant
deaths
Alcohol
Alcohol
1
Figure 3:
SUMMARY
OUTPUT
Regression Statistics
Multiple R
0.169074
R Square
0.028586
Adjusted R
0.027996
Square
Standard
8.672821
Error
Observations
1649
ANOVA
Regression
Residual
Total
Intercept
infant deaths
df
SS
1
1647
1648
3645.547
123883.8
127529.3
Coefficients
Standard
Error
0.221192
0.001768
69.70295
-0.01231
MS
F
Significance
F
3645.547 48.46652 0.00
75.21783
t Stat
P-value
315.1236 0.00
-6.96179 0.00
Lower 95%
69.2691
-0.01577
Upper
95%
70.1368
0.00884
Lower
95.0%
69.2691
0.01577
Upper
95.0%
70.1368
0.00884
Figure 4:
Regression Statistics
Multiple R
0.402718
R Square
0.162182
Adjusted R
0.161673
Square
Standard
8.054397
Error
Observations
1649
ANOVA
Regression
Residual
Total
Intercept
Alcohol
df
SS
1
1647
1648
20682.96
106846.3
127529.3
Coefficients
Standard
Error
0.298614
0.049242
65.31651
0.879245
MS
F
Significance
F
20682.96 318.8208 0.00
64.87331
t Stat
P-value
218.7326 0.00
17.85556 0.00
Lower 95%
64.73081
0.782662
Upper
95%
65.90221
0.975829
Lower
95.0%
64.73081
0.782662
Upper
95.0%
65.90221
0.975829
Figure 5:
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.422274
R Square
0.178315
Adjusted R
0.177317
Square
Standard
7.978894
Error
Observations 1649
ANOVA
df
Regression
Residual
Total
2
1646
1648
Coefficients
Intercept
infant
deaths
Alcohol
SS
MS
F
Significance
F
22740.43 11370.22 178.6008 0.00
104788.9 63.66275
127529.3
Lower 95%
65.7535
-0.0093
Standard t Stat
P-value
Error
0.305638 215.1351 0.00
0.001636 -5.68491 0.00
65.15401
-0.01251
Upper
95%
66.35298
-0.00609
Lower
95.0%
65.15401
-0.01251
0.8496
0.049058 17.31872 0.00
0.7534
0.945845 0.7534
Upper
95.0%
66.35298
-0.00609
0.945845
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