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 websitelists 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:

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. How to Write an Executive Summary That Gets the Job Done 2023.pdf

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?

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 $0.84SUMMARY OUTPUT Regression Statistics Multiple R 0.935824078 R Square 0.875766706 Adjusted R Square 80.12% Standard Error 8.30945321 Observations 17 0 ANOVA df SS MS F Significance F 2 Regression 6 4867.380768 811.2301279 11.74895331 0.00049963 3 Residual 10 690.4701265 69.04701265 Total 16 5557.850894 5 On 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 8 Rent (in City Centre) -0.003212852 0.003974813 -0.808302603 0.437722785 -0.012069287 0.005643584 -0.012069287 0.005643584 9 Monthly Pubic Trans Pass 0.299650003 0.076964051 3.89337619 0.002993072 0.128163411 0.471136595 0.128163411 0.471136595 0 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 2 Bottle of Wine (mid-range) 0.889805486 0.740190296 -1.202130709 0.257006081 -2.539052244 0.759441271 -2.539052244 0.759441271 3 Coffee -2.527438053 6.484555358 -0.389762738 0.704884259 -16.97592778 11.92105168 -16.97592778 11.9210516827 RESIDUAL OUTPUT 28 29 Observation Predicted Cost of Living Index Residuals Standard Residuals City 30 34.32607137 -2.586071368 -0.39366613 Mumbai 31 53.21656053 -2.266560525 -0.345028417 Prague 32 49.41436121 -3.964361215 -0.603477056 Warsaw 33 58.63611785 4.42388215 0.673427882 Athens 34 73.08449538 5.105504624 0.777188237 Rome 35 86.50256003 -3.052560026 -0.464677621 Seoul 36 75.89216916 6.307830843 0.960213003 Brussels 37 67.7257781 -0.975778105 -0.148538356 Madrid 38 90.51996071 -16.45996071 -2.50562653 Vancouver 39 10 81.07358731 8.866412685 1.349694525 Paris 40 11 83.80564633 9.134353675 1.390481989 Tokyo 41 12 80.02510391 -8.37510391 -1.274904778 Berlin 42 13 82.41624318 3.483756815 0.530316788 Amsterdam 43 14 97.75654811 2.243451893 0.341510693 New York 44 15 87.73993924 3.040060757 0.462774913 Sydney 45 16 86.81668291 1.11331709 0.169475303 Dublin 46 17 94.36817468 -6.038174677 -0.919164446 London 47 48