Question

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 (www.mercer.com) 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

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. 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. 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.

City Cost of Living Index Rent (in City Centre) Monthly Pubic Trans Pass Loaf of Bread Milk Bottle of Wine (mid-range) Coffee

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 $8.24 $1.51

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 $85.92 $1.56 $4.68 $8.24 $1.51

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 $5.89 $1.71

Amsterdam 85.9 $2,823.28 $105.93 $1.33 $4.34 $7.06 $1.71

New York 100 $5,877.45 $121.00 $2.93 $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 $1.47 $4.71 $10.41 $1.76

median 82.2 $2,354.10 $74.28 $1.37 $4.34 $8.24 $1.71

min 31.74 $569.12 $7.66 $0.41 $2.68 $5.46 $0.84

max 100 $5,877.45 $173.81 $2.93 $7.90 $17.75 $2.88

Q1 66.75 $1,695.77 $41.20 $1.04 $3.63 $7.06 $1.51

Q3 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 $0.84

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.935824078

R Square 0.875766706

Adjusted R Square 80.12%

Standard Error 8.30945321

Observations 17

ANOVA

df SS MS F Significance F

Regression 6 4867.380768 811.2301279 11.74895331 0.00049963

Residual 10 690.4701265 69.04701265

Total 16 5557.850894

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept  35.63950178 15.41876933 2.311436213 0.043401141 1.284342794 69.99466077 1.284342794 69.99466077

Rent (in City Centre) -0.003212852 0.003974813 -0.808302603 0.437722785 -0.012069287 0.005643584 -0.012069287 0.005643584

Monthly Pubic Trans Pass 0.299650003 0.076964051 3.89337619 0.002993072 0.128163411 0.471136595 0.128163411 0.471136595

Loaf of Bread 16.59481787 6.713301249 2.47193106 0.032995588 1.636650533 31.55298521 1.636650533 31.55298521

Milk 2.912081706 1.98941146 1.463790555 0.173964311 -1.520603261 7.344766672 -1.520603261 7.344766672

Bottle of Wine (mid-range) -0.889805486 0.740190296 -1.202130709 0.257006081 -2.539052244 0.759441271 -2.539052244 0.759441271

Coffee -2.527438053 6.484555358 -0.389762738 0.704884259 -16.97592778 11.92105168 -16.97592778 11.92105168

RESIDUAL OUTPUT

Observation Predicted Cost of Living Index Residuals Standard Residuals City

1 34.32607137 -2.586071368 -0.39366613 Mumbai

2 53.21656053 -2.266560525 -0.345028417 Prague

3 49.41436121 -3.964361215 -0.603477056 Warsaw

4 58.63611785 4.42388215 0.673427882 Athens

5 73.08449538 5.105504624 0.777188237 Rome

6 86.50256003 -3.052560026 -0.464677621 Seoul

7 75.89216916 6.307830843 0.960213003 Brussels

8 67.7257781 -0.975778105 -0.148538356 Madrid

9 90.51996071 -16.45996071 -2.50562653 Vancouver

10 81.07358731 8.866412685 1.349694525 Paris

11 83.80564633 9.134353675 1.390481989 Tokyo

12 80.02510391 -8.37510391 -1.274904778 Berlin

13 82.41624318 3.483756815 0.530316788 Amsterdam

14 97.75654811 2.243451893 0.341510693 New York

15 87.73993924 3.040060757 0.462774913 Sydney

16 86.81668291 1.11331709 0.169475303 Dublin

17 94.36817468 -6.038174677 -0.919164446 London

Answer 1

Based on the regression output summary,

Let the significance level = 0.05

The results can be interpreted in the following points,

Overall Significance

F	P-value
Regression	11.74895	0.0004996	<	0.05	Significant

The p-value is 0.0.0004996 which is less than 0.05 at 5% significance level which means the model fits the data value at the predefined significance level. Hence we can conclude that independent variables fit the model significantly.

Significance of Independent variables

From, the result summary,

Independent variable	P-value
Rent (in City Centre)	0.437722785	>	0.05	Not significant
Monthly Pubic Trans Pass	0.002993072	<	0.05	Significant
Loaf of Bread	0.032995588	<	0.05	Significant
Milk	0.173964311	>	0.05	Not significant
Bottle of Wine (mid-range)	0.257006081	>	0.05	Not significant
Coffee	0.704884259	>	0.05	Not significant

The P-value for the independent variables Monthly Pubic Trans Pass and Loaf of Bread is less than 0.05 at 5% significance level hence we can conclude these two independent variables are significant in the model. While the remaining variables are not significant in the model.

R-Square value

From, the result summary,

R Square

0.875766706

The R-square value tells, how well the regression model fits the data values. The R-square value of the model is 0.875766706 which means, the model explains approximately 87.58% of the variance of the data value. Based on this evidence we can conclude the model is a good fit.