UKCustomPapers | A marketing company based out of New York City is doing well and is looking to expand internationally

A marketing company based out of New York City is doing well and is looking to expand internationally

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. The 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.

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.

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 3rd quartile?

Sample Answer

Analyzing the Cost of Living Data for International Expansion

Understanding the Data

We’re tasked with identifying the best city for a second office based on cost-of-living data. We’ll use a multiple linear regression (MLR) model to predict the cost of living index based on several factors.

Interpreting the MLR Output

Significant Predictors: By examining the p-values associated with each predictor variable, we can determine their statistical significance. Variables with p-values less than 0.05 are generally considered statistically significant.

Full Answer Section

Analyzing Significant Predictors: For the significant predictors, we should compare their mean, median, min, max, Q1, and Q3 values to New York City’s values. This will help us identify cities with lower costs in these areas.

3-Bedroom Apartment: A lower cost of a 3-bedroom apartment would be beneficial for employee housing.
Monthly Transportation Pass: A lower cost of transportation would reduce employee expenses.
Price of a Loaf of Bread: This can be a basic indicator of food costs.
Price of a Gallon of Milk: Another indicator of food costs.

Identifying Potential Cities

Cities Below the Median: These cities might offer lower overall costs of living.
Cities in the Upper 3rd Quartile: These cities might have higher costs of living but could offer other advantages, such as a higher quality of life or a stronger economy.

Making a Recommendation

Based on the analysis of the significant predictors and the overall cost of living index, we can recommend a city or cities for the second office.

Potential Recommendation:

City X: If City X has a significantly lower cost of living index, lower costs for significant predictors, and offers a good quality of life, it could be a strong candidate.
City Y and City Z: If these cities have a lower cost of living index and lower costs for some significant predictors, they could be considered as well.

Additional Considerations:

Cultural Factors: Consider the cultural differences and language barriers.
Business Environment: Evaluate the business climate, tax rates, and regulatory environment.
Talent Pool: Assess the availability of skilled workers in the target city.
Infrastructure: Consider the quality of infrastructure, such as transportation and utilities.

By carefully considering these factors and analyzing the data, we can make an informed decision about the best city for the second office.

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