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 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 3 rd quartile 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

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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

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 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 3^rd quartile?

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