FINAL PROJECT URGENT DUE TODAY
Final Project Assignment Instructions
Scenario 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.
More specifically, if you choose to explore the website further you will find a lot of fun and interesting data. You can explore the website more on your own after the course concludes.
https://mobilityexchange.mercer.com/Insights/ cost-of-living-rankings#rankings
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 3-bedroom 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, in which city should you open a second office in? You must justify your answer. If you want to recommend 2 or 3 different cities and rank them based on the data and your findings, this is fine as well.
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. You do not need to do any calculations, but you do need to pick a city to open a second location at and justify your answer based upon the provided results of the Multiple Linear Regression.
The format of this assignment will be an Executive Summary. Think of this assignment as the first page of a much longer report, known as an Executive Summary, that essentially summarizes your findings briefly and at a high level. This needs to be written up neatly and professionally. This would be something you would present at a board meeting in a corporate environment. 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?
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?
What city or cities fall are below the median?
What city or cities are in the upper 3rd quartile?
MATH302 Final Project Description
Evaluation/Grading of your Final Project
Math 302 Final Project will open up Friday morning of Week 6 in the course. You have 3 full
weekends to review and work on the Final Project.
Content addressed in the Final Project
In the final project, you are given a data set and a regression output. The concept of a data set
should be something that you are familiar with because you collected one during Week 1.
There are descriptive statistics that go along with said data set, which should also be familiar
because you calculated descriptive statistics during Week 2.
The Regression output won’t look familiar to you until Week 7. Once you go through the
Lessons and the Discussion Forum, (particularly your second response post) you should be
familiar on how to run a Regression and what a Regression output looks like from the
ToolPak. By the end of Week 7, you will have all the information needed to write up the Final
Project. There is nothing new that you learn in Week 8 needed for the write up of the final
project.
Final Project Overview
The final project is worth 100 points and no calculations are needed. You will write up an
Executive Summary on what city you chose to open a second location in and justify the results.
Again, no calculations are needed because you will be writing up your own Executive Summary
that will then be submitted through Turnitin. From Turnitin, an originality report will be
generated. No Turnitin report should exceed 20% of originality because you are writing this up
in your own words. If any originality report is over 20%, then further action will need to be
required from your instructor. This can include an automatic failure and 0 for plagiarism. If you
have questions on what Academic Plagiarism is, please contact your instructor.
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?
https://www.surveygizmo.com/resources/blog/how-to-write-executive-summary/
https://www.surveygizmo.com/resources/blog/how-to-write-executive-summary/
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.
> inal MLR
Statistics
2 0 83
67056
2%
s
F .230 79392
Standard Error .4187693276
362129
1.2843427942 69.9946607717 Centre)
-0.0120692871 0.0056435836 0.1281634113 0.4711365954 1.6366505328 31.5529852097 -1.5206032612 7.3447666725 -2.5390522435 0.7594412713 -16.9759277837 11.9210516769 City Monthly Pubic Trans Pass Loaf of Bread Milk Bottle of Wine (mid-range) Coffee $8.24 $1.51 2
$8.24 $1.51 $5.89 $7.06 $1.71 $2.93 $1.47 82.2 $2,354.10 $74.28 $1.37 $4.34 $8.24 $1.71 31.74 $569.12 $7.66 $0.41 $2.68 $5.46 $0.84 100 $5,877.45 $173.81 $2.93 $7.90 $17.75 $2.88 66.75 $1,695.77 $41.20 $1.04 $3.63 $7.06 $1.51 88.33 $2,937.27 $105.93 $1.77 $5.35 $14.12 $1.98 New York 100 $5,877.45 $121.00 $2.93 $3.98 $15.00 $0.84F
SUMMARY OUTPUT
Regression
Multiple R
0.
9
3
5
8
4
7
R Square
0.8757
6
Adjusted R Square
80.
1
Standard Error
8.3094532099
Observation
17
ANOVA
df
SS
MS
Significance F
Regression 6
4867.380767635
8
11
12
11.748953312
0.0004996299
Residual
10
690.4701264826
69.0470126483
Total
16
5557.8508941176
Coefficients
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
35.6395017829
15
2.31
14
0.0434011406
1.2843427942
69.9946607717
Rent (in
City
-0.0032128517
0.0039748
13
-0.8083026028
0.4377227847
-0.0120692871
0.0056435836
Monthly Pubic Trans Pass
0.2996500033
0.0769640509
3.8933761896
0.0029930715
0.1281634113
0.4711365954
Loaf of Bread
16.5948178712
6.7133012492
2.4719310597
0.0329955879
1.6366505328
31.5529852097
Milk
2.9120817057
1.9894114601
1.4637905552
0.1739643111
-1.5206032612
7.3447666725
Bottle of Wine (mid-range)
-0.8898054861
0.7401902965
-1.2021307093
0.2570060814
-2.5390522435
0.7594412713
Coffee
-2.5274380534
6.4845553577
-0.3897627384
0.7048842587
-16.9759277837
11.9210516769
RESIDUAL OUTPUT
Observation
Predicted
Cost of Living Index
Residuals
Standard Residuals
1
34.3260713681
-2.5860713681
-0.3936661298
Mumbai
2
53.2165605253
-2.2665605253
-0.3450284168
Prague
3
49.4143612149
-3.9643612149
-0.6034770563
Warsaw
4
58.6361178497
4.4238821503
0.6734278823
Athens
5
73.0844953758
5.1055046242
0.7771882365
Rome
6
86.5025600265
-3.0525600265
-0.4646776212
Seoul
7
75.8921691573
6.3078308427
0.9602130034
Brussels
8
67.7257781049
-0.9757781049
-0.1485383562
Madrid
9
90.5199607051
-16.4599607051
-2.5056265297
Vancouver
10
81.0735873148
8.8664126852
1.3496945251
Paris
11
83.8056463253
9.1343536747
1.3904819889
Tokyo
12
80.02510391
-8.37510391
-1.2749047778
Berlin
13
82.4162431846
3.4837568154
0.5303167885
Amsterdam
14
97.7565481074
2.2434518926
0.3415106926
New York
15
87.7399392431
3.0400607569
0.4627749131
Sydney
16
86.8166829103
1.1133170897
0.1694753035
Dublin
17
94.3681746768
-6.0381746768
-0.9191644459
London
Data
City Cost of Living Index
Rent (in City Centre)
Mumbai
31.74
$1,642.68
$7.66
$0.41
$2.93
$10.73
$1.63
Prague
50.95
$1,240.48
$25.01
$0.92
$3.14
$5.46
$2.17
Warsaw
45.45
$1,060.06
$30.09
$0.69
$2.68
$6.84
$1.98
Athens
63.06
$569.12
$35.31
$0.80
$5.35
$8.24
$2.88
Rome
78.19
$2,354.10
$41.20
$1.38
$6.82
$7.06
$1.51
Seoul
83.45
$2,370.81
$50.53
$2.44
$7.90
$17.57
$1.79
Brussels
82.2
$1,734.75
$57.68
$1.66
$4.17
Madrid
66.75
$1,795.10
$64.27
$1.04
$3.63
$5.89
$1.58
Vancouver
74.06
$2,937.27
$74.28
$2.28
$7.12
$14.38
$1.47
Paris
89.94
$2,701.61
$
85.9
$1.56
$4.68
Tokyo
92.94
$2,197.03
$88.77
$1.77
$6.46
$17.75
$1.49
Berlin
71.65
$1,695.77
$95.34
$1.24
$3.52
$1.71
Amsterdam 85.9
$2,823.28
$105.93
$1.33
$4.34
New York
100
$5,877.45
$121.00
$3.98
$15.00
$0.84
Sydney
90.78
$3,777.72
$124.55
$1.94
$4.43
$14.01
$2.26
Dublin
87.93
$3,025.83
$144.78
$1.37
$4.31
$14.12
$2.06
London
88.33
$4,069.99
$173.81
$1.23
$4.63
$10.53
$1.90
mean
75.49
$2,463.12
$78.01
$4.71
$10.41
$1.76
median
min
max
Q1
Q3