Executive Summary – Cost of Living
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).
This should 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 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.
Executive Summary – Cost of Living
According to the final MRL and summary output, the regression is 11.74895, which is equivalent to a p-value of 0.0004996. Using a significance level of 0.05, the results show a 5% significance, since 0.0004996 is less than 0.05. In that case, the model fits the data value presented at the defined significance level. The overall results of this regression show that when estimating and predicting NYC’s cost of living in the future, data value presented closely relates to the model. When evaluating independent variables of the cost of living, independent variables without a great significance in the determination of cost of living in New York City include rent, milk, a bottle of wine, and coffee, whereby they all have a p-value higher than 0.05. As seen in the output summary, rent has a p-value of 0.44, milk 0.17, a bottle of wine 0.26; and coffee has 0.70. Values higher than 0.05 indicate that these independent variables have no substantial effect on cost of living in New York City.
Independent variables with a significant influence on the cost of living include bread and public transport. Whereby the p-value of bread is 0.03, and that of public transport is 0.003, concluding that they are both less than 0.05 significance level. Secondly, the p-value of R Square, according to summary outputs, is 0.87, which shows that the model explains approximately 87% of the data value. The data value presented on the cost of living in NYC closely fits the regression line by 87%, making the model a good fit. According to the p-value and regression results, the null hypothesis that predicts NY’s cost of living index as 97.7% is firmly rejected, hence, a better cost of living for the company in the future.
