MATH 303 American Military University Wk7 Significant Predictors Executive Summary

Background:
A marketing company based out of New York City is doing well and is looking
to expand internationally. The CEO and VP of Operations decide to enlist the
help of a consulting firm that you work for, to help collect data and analyze
market trends.
You work for Mercer Human Resources. The Mercer Human Resource
Consulting website lists prices of certain items in selected cities around the
world. They also report an overall cost-of-living index for each city compared
to the costs of hundreds of items in New York City (NYC). For example,
London at 88.33 is 11.67% less expensive than NYC.
Assignment Guidance:
In the Excel document, you will find the 2018 data for 17 cities in the data
set Cost of Living. Included are the 2018 cost of living index, cost of a 3bedroom apartment (per month), price of monthly transportation pass, price
of a mid-range bottle of wine, price of a loaf of bread (1 lb.), the price of a
gallon of milk and price for a 12 oz. cup of black coffee. All prices are in U.S.
dollars.
You use this information to run a Multiple Linear Regression to predict Cost
of living, along with calculating various descriptive statistics. This is given in
the Excel output (that is, the MLR has already been calculated. Your task is
to interpret the data).
Based on this information, recommend 2 different cities and rank
them based on the data and your findings.
Deliverable Requirements:
This should be ¾ to 1 page, no more than 1 single-spaced page in length,
using 12-point Times New Roman font. Justify your answer based upon the
provided results of the Multiple Linear Regression.
The format of this assignment will be an Executive Summary. Essentially
summarizes your findings briefly and at a high level. This needs to be
written up neatly and professionally. If you are unsure of an Executive
Summary, this resource can help with an overview. What is an Executive
Summary?
Things to Consider:
To help you make this decision here are some things to consider:
•
•
•
Based on the MLR output, what variable(s) is/are significant?
From the significant predictors, review the mean, median, min, max,
Q1 and Q3 values?
o It might be a good idea to compare these values to what the
New York value is for that variable. Remember New York is the
baseline as that is where headquarters are located.
Based on the descriptive statistics, for the significant predictors, what
city has the best potential?
o What city or cities fall are below the median?
o What city or cities are in the upper 3rd quartile?
Content addressed
You are given a data set and a regression output. (See excel)
Overview
No calculations are needed. Write up an Executive Summary on what city you chose to open a second
location in and justify the results. Turnitin report should not exceed 20% of originality
Grading Breakdown:
1) Executive Summary – up to 10%
a. Please review what an Executive Summary looks like:
▪ What is an Executive Summary?
b. Must have cover page.
2) Grammar – up to 10%
a. Spell and grammar check your work.
b. Make sure you have correct punctuation and complete sentences.
3) State significant predictors – up to 25%
a. Must state which predictors are significant at predicting Cost of Living and how
do you know.
b. Show the comparison to alpha to state your results and conclusion.
c. Do these significant predictors make sense, if you want to relocate?
4) Discuss descriptive statistics for the significant predictors – up to 25%
a. From the significant predictors, review the mean, median, min, max, Q1 and Q3
values.
b. What city or cities fall above or below the median and/or the mean?
c. What city or cities are in the upper 3rd quartile? Or the bottom quartile?
d. How do these predictors compare to the baseline of NYC? What cost more or
less money than NYC?
5) Recommend at least 2 cities to open a second location in – up to 30%
a. You must justify your answer for full credit.
b. You need to use the Significant Predictors AND Descriptive Statistics in your
justification.
c. Justification without the use of Significant Predictors WILL NOT get full credit.
d. Justification without the use of Descriptive Statistics WILL NOT get full credit.
You need to use both.
e. For example, let’s look back at London. London at 88.33, is 11.67% less
expensive than NYC. But that doesn’t mean London is a good place to open a
second location once you discuss the significant predictors and how it relates
back to each city.
f. Use what you have learned in the course and analyze all the data not just what
you see on the surface.
g. You must use the numbers and the output to justify your answers. Do not use
any outside resources to justify your answer. Only use Significant Predictors
AND Descriptive Statistics
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.935824078
0.875766706
80.12%
8.30945321
17
ANOVA
df
Regression
Residual
Total
SS
4867.380768
690.4701265
5557.850894
MS
811.2301279
69.04701265
Coefficients
Standard Error
35.63950178
15.41876933
-0.003212852
0.003974813
0.299650003
0.076964051
16.59481787
6.713301249
2.912081706
1.98941146
-0.889805486
0.740190296
-2.527438053
6.484555358
t Stat
2.311436213
-0.808302603
3.89337619
2.47193106
1.463790555
-1.202130709
-0.389762738
6
10
16
Intercept
Rent (in City Centre)
Monthly Pubic Trans Pass
Loaf of Bread
Milk
Bottle of Wine (mid-range)
Coffee
RESIDUAL OUTPUT
Observation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Predicted Cost of Living Index
34.32607137
53.21656053
49.41436121
58.63611785
73.08449538
86.50256003
75.89216916
67.7257781
90.51996071
81.07358731
83.80564633
80.02510391
82.41624318
97.75654811
87.73993924
86.81668291
94.36817468
Residuals
-2.586071368
-2.266560525
-3.964361215
4.42388215
5.105504624
-3.052560026
6.307830843
-0.975778105
-16.45996071
8.866412685
9.134353675
-8.37510391
3.483756815
2.243451893
3.040060757
1.11331709
-6.038174677
Standard Residuals
-0.39366613
-0.345028417
-0.603477056
0.673427882
0.777188237
-0.464677621
0.960213003
-0.148538356
-2.50562653
1.349694525
1.390481989
-1.274904778
0.530316788
0.341510693
0.462774913
0.169475303
-0.919164446
F
Significance F
11.74895331
0.00049963
P-value
0.043401141
0.437722785
0.002993072
0.032995588
0.173964311
0.257006081
0.704884259
City
Mumbai
Prague
Warsaw
Athens
Rome
Seoul
Brussels
Madrid
Vancouver
Paris
Tokyo
Berlin
Amsterdam
New York
Sydney
Dublin
London
Lower 95%
1.284342794
-0.012069287
0.128163411
1.636650533
-1.520603261
-2.539052244
-16.97592778
Upper 95%
69.99466077
0.005643584
0.471136595
31.55298521
7.344766672
0.759441271
11.92105168
Lower 95.0% Upper 95.0%
1.284342794 69.99466077
-0.012069287 0.005643584
0.128163411 0.471136595
1.636650533 31.55298521
-1.520603261 7.344766672
-2.539052244 0.759441271
-16.97592778 11.92105168
City
Mumbai
Prague
Warsaw
Athens
Rome
Seoul
Brussels
Madrid
Vancouver
Paris
Tokyo
Berlin
Amsterdam
New York
Sydney
Dublin
London
mean
median
min
max
Q1
Q3
New York
Cost of Living Index
31.74
50.95
45.45
63.06
78.19
83.45
82.2
66.75
74.06
89.94
92.94
71.65
85.9
100
90.78
87.93
88.33
75.49
82.2
31.74
100
66.75
88.33
100
Rent (in City Centre)
$1,642.68
$1,240.48
$1,060.06
$569.12
$2,354.10
$2,370.81
$1,734.75
$1,795.10
$2,937.27
$2,701.61
$2,197.03
$1,695.77
$2,823.28
$5,877.45
$3,777.72
$3,025.83
$4,069.99
$2,463.12
$2,354.10
$569.12
$5,877.45
$1,695.77
$2,937.27
$5,877.45
Monthly Pubic Trans Pass
$7.66
$25.01
$30.09
$35.31
$41.20
$50.53
$57.68
$64.27
$74.28
$85.92
$88.77
$95.34
$105.93
$121.00
$124.55
$144.78
$173.81
$78.01
$74.28
$7.66
$173.81
$41.20
$105.93
$121.00
Loaf of Bread
$0.41
$0.92
$0.69
$0.80
$1.38
$2.44
$1.66
$1.04
$2.28
$1.56
$1.77
$1.24
$1.33
$2.93
$1.94
$1.37
$1.23
$1.47
$1.37
$0.41
$2.93
$1.04
$1.77
$2.93
Milk
$2.93
$3.14
$2.68
$5.35
$6.82
$7.90
$4.17
$3.63
$7.12
$4.68
$6.46
$3.52
$4.34
$3.98
$4.43
$4.31
$4.63
$4.71
$4.34
$2.68
$7.90
$3.63
$5.35
$3.98
Bottle of Wine (mid-range)
$10.73
$5.46
$6.84
$8.24
$7.06
$17.57
$8.24
$5.89
$14.38
$8.24
$17.75
$5.89
$7.06
$15.00
$14.01
$14.12
$10.53
$10.41
$8.24
$5.46
$17.75
$7.06
$14.12
$15.00
Coffee
$1.63
$2.17
$1.98
$2.88
$1.51
$1.79
$1.51
$1.58
$1.47
$1.51
$1.49
$1.71
$1.71
$0.84
$2.26
$2.06
$1.90
$1.76
$1.71
$0.84
$2.88
$1.51
$1.98
$0.84