A marketing company based out of New York City is doing well andis looking to expand internationally. The CEO and VP of Operationsdecide 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 ResourceConsulting website (www.mercer.com) lists prices of certain itemsin selected cities around the world. They also report an overallcost-of-living index for each city compared to the costs ofhundreds of items in New York City (NYC). For example, London at88.33 is 11.67% less expensive than NYC.
More specifically, if you choose to explore the website furtheryou will find a lot of fun and interesting data. You can explorethe 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 citiesin the data set Cost of Living. Included are the 2018 cost ofliving index, cost of a 3-bedroom apartment (per month), price ofmonthly 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 andprice for a 12 oz. cup of black coffee. All prices are in U.S.dollars.
You use this information to run a Multiple LinearRegression to predict Cost of living, along with calculatingvarious descriptive statistics. This is given in the Excel output(that is, the MLR has already been calculated. Your task is tointerpret the data). Based on this information, in which cityshould you open a second office in? You must justify your answer.If you want to recommend 2 or 3 different cities and rank thembased on the data and your findings, this is fine as well. Thisshould 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 anycalculations, but you do need to pick a city to open a secondlocation at and justify your answer based upon the provided resultsof the Multiple Linear Regression. Think of this assignment as thefirst page of a much longer report, known as an Executive Summary,that essentially summarizes your findings briefly and at a highlevel. This needs to be written up neatly and professionally. Thiswould be something you would present at a board meeting in acorporate environment.
City Cost of Living Index Rent (in City Centre) Monthly PubicTrans 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 2.3114362130.043401141 1.284342794 69.99466077 1.284342794 69.99466077
Rent (in City Centre) -0.003212852 0.003974813 -0.8083026030.437722785 -0.012069287 0.005643584 -0.012069287 0.005643584
Monthly Pubic Trans Pass 0.299650003 0.076964051 3.893376190.002993072 0.128163411 0.471136595 0.128163411 0.471136595
Loaf of Bread 16.59481787 6.713301249 2.47193106 0.0329955881.636650533 31.55298521 1.636650533 31.55298521
Milk 2.912081706 1.98941146 1.463790555 0.173964311 -1.5206032617.344766672 -1.520603261 7.344766672
Bottle of Wine (mid-range) -0.889805486 0.740190296 -1.2021307090.257006081 -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 StandardResiduals 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
