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. TheMercer 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:

No calculations, just need to Identify a city to open a second location at and justify based upon the provided results of the Multiple Linear Regression.

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^rdquartile?

\fRegression Statistics Multiple R 0.935824078 R Square 0.875766706 Adjusted R Square 30.12% Standard Error 8.30945321 Observations 17 ANOVA df SS MS F Significance F Regression 6 4867.380768 811.2301279 11.7489533 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 5.63950178 15.41876933 2.311436213 0.04340114 1.284342794 69.9946608 1.284342794 69.9946608 Rent (in City Centre) -0.003212852 0.003974813 0.808302603 0.43772278 -0.012069287 0.00564358 -0.01206929 0.00564358 Monthly Pubic Trans Pass 0.299650003 0.076964051 3.89337619 0.00299307 0.128163411 0.4711366 0.128163411 0.4711366 Loaf of Bread 16.59481787 6.713301249 2.47193106 0.03299559 1.636650533 31.5529852 1.636650533 31.5529852 Milk 2.912081706 1.98941146 1.463790555 0.17396431 -1.520603261 7.34476667 -1.52060326 6 7.3447666 Bottle of Wine (mid-range 0.889805486 0.740190296 -1.202130709 0.25700608 -2.539052244 0.75944127 -2.53905224 0.75944127 Coffee -2.527438053 6.484555358 0.389762738 0.70488426 -16.97592778 11.9210517 -16.9759278 11.9210517 RESIDUAL OUTPUT Observation Predicted Cost of Living Index Residuals Standard Residuals City 34.32607137 -2.586071368 0.39366613 Mumbai 53.21656053 -2.266560525 -0.345028417 Prague 49.41436121 -3.964361215 -0.603477056 Warsaw 58.63611785 4.42388215 0.673427882 Athens 73.08449538 5.105504624 0.777188237 Rome 86.50256003 -3.052560026 -0.464677621 Seoul 7 75.89216916 6.307830843 0.960213003 Brussels 8 67.7257781 -0.975778105 -0.148538356 Madrid 30.51996071 -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