Math302 Week 8 Final Project, Assignments of Mathematics

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Final Project Student Name MATH Instructor: Kevin Lawson Due Date

During the search and gathering on the data of the 17 cites provided for us, the set cost of living, I have made my conclusion. The city I found most appropriate to set up a new location (internationally) would be the city of Mumbai. With all the gathered data in the excel sheet, I was able to transfer in the data into two tabs. On one of the tabs, it mentions the cities along with cost, and distinct amenities. While the other tab shows multiple linear regression or in this case MLR. We used the cost of living for a 3-bedroom apartment (monthly), price of monthly transportation pass, price of mid-range bottle of wine, price of a loaf of bread (1lb), the price of a gallon of milk, and the price for a 12 oz cup of black coffee. With these items being used daily, some people consider these as a necessity. The change of a geographical location will have little effect on the change of people’s habits and is why its best to use these variables in the chart. After running the regression, the common significance level I used was 0.05. The purpose was to focus on the p-values of each and with that, we can determine the values and significance of each variable in the different cities. Now, if the p-value is higher than the significance of 0.05, that would mean that the city would not be the best candidate for a further expansion. With the data included, we can analyze the different city cost and values to see in which city would be best suited for the expansion of an international company. We have concluded three variables that run lower than our p-value. With the significance level of 0.05, the three variables are lower in the cost of living (monthly), transportation, and the cost of a loaf of bread (1lbs). The variables are lower than 0.05 and are part of the daily necessities. So, with this information we will use them to determine our new location. One of the most expensive cities in the United States, would be New York City, so we used this city to compare the data. Focusing on the 1st^ quartile range of 66.75, New York’s numbers where cost of living is at 100, that being the max. Translating the 66.75, this would mean that most cities have a cost of living above the 66.75 range. Any number that falls below this has a change of being outlier. Firstly, with the cost-of-living numbers, it gave an average was 75.49, the median was 82.20, the minimum was 31.74, out of 100 with the 1st^ quartile being 66.75 and 3rd quartile being 88.33. Secondly, for the cost of public transportation, the average was 78.01, the median was 74.28, the minimum was 7.66 with the max of 173.81, the 1 st^ quartile 41.20 and 3 rd quartile 105.93. Lastly, the cost of a loaf of bread (1lb), the average was 1.47, the median was 1.37, the minimum was 0.41 with the max of 2.93, 1st^ quartile 1.04 and 3rd quartile 1.77. With the information given, the objective was to compare the data in determining if there were any values lower than the 1st^ quartile. With the standard residuals for the predicted cost of living from the regression test we ran, there were no standard residuals that were less or greater than 3. Having this information is important because whether the residuals were less or greater than 3, the outliners are usually greater than 3 standard deviations away from the mean. In this case, the highest standard residual was 1.39 and the lowest standard residual was -2.51. Having this data, this would mean that none of the cites were considered outlier. It should be clear, that with all the information provided above, why I chose Mumbai as the best international city to expand the company. It is cheaper to live in Mumbai than living in New York City. The cost of living in Mumbai is 31.74, with a public transportation cost of 7.66, and the cost of a loaf of bread being 0.41. Living in a cheaper cost of living city would mean, cheaper mortgages, more people willing to be employed, more residents, and more consumers. All in all, the international expansion would best be suited in Mumbai, the expenses would be way less, and the company would profit.