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.

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?

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?

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 Center)	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.

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