# Multiple Linear Regression Analysis

### 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:

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. If you are unsure of an Executive Summary, this resource can help with an overview. What is an Executive Summary?

### 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 3rd quartile?
• 1) Executive Summary – up to 10% a. Please review what an Executive Summary looks like: ▪ What is an Executive Summary? b. Must have cover page.
• 2) Grammar – up to 10% a. Spell and grammar check your work. b. Make sure you have correct punctuation and complete sentences.
• 3) State significant predictors – up to 25%
• a. Must state which predictors are significant at predicting Cost of Living and how do you know.
• b. Show the comparison to alpha to state your results and conclusion.
• c. Do these significant predictors make sense, if you want to relocate?
• 4) Discuss descriptive statistics for the significant predictors – up to 25%
• a. From the significant predictors, review the mean, median, min, max, Q1 and Q3 values.
• b. What city or cities fall above or below the median and/or the mean?
• c. What city or cities are in the upper 3rd quartile? Or the bottom quartile?
• d. How do these predictors compare to the baseline of NYC? What cost more or less money than NYC?
• 5) Recommend at least 2 cities to open a second location in – up to 30%
• b. You need to use the Significant Predictors AND Descriptive Statistics in your justification.
• c. Justification without the use of Significant Predictors WILL NOT get full credit.
• d. Justification without the use of Descriptive Statistics WILL NOT get full credit. You need to use both.
• e. For example, let’s look back at London. London at 88.33, is 11.67% less expensive than NYC. But that doesn’t mean London is a good place to open a second location once you discuss the significant predictors and how it relates back to each city. f. Use what you have learned in the course and analyze all the data not just what you see on the surface. g. You must use the numbers and the output to justify your answers. Do not use any outside resources to justify your answer. Only use Significant Predictors AND Descriptive Statistics.