MATH302 Final Project Description Evaluation/Grading Of You ✓ Solved

MATH302 Final Project Description Evaluation/Grading of you

The final project involves analyzing a given data set and regression output. You should be familiar with the data set you collected in Week 1 and the descriptive statistics calculated in Week 2. The Regression output will become relevant to you during Week 7 after you engage with the Lessons and Discussion Forum.

You will write an Executive Summary on a city chosen for opening a second location, justifying your decision based on the project requirements. No calculations are needed for this task. The Executive Summary must be original, with a Turnitin report not exceeding 20% similarity. Reports over this threshold may result in penalties, including a possible failure for plagiarism.

The grading criteria include:

1) Executive Summary (up to 10%) with a cover page.

2) Grammar (up to 10%) with correct punctuation and complete sentences.

3) Significant Predictors (up to 25%) must be stated clearly, with a comparison to alpha to justify your results.

4) Descriptive Statistics of the significant predictors (up to 25%) must include mean, median, min, max, Q1, and Q3 values, as well as relative comparisons to NYC.

5) Recommendations for at least two cities to open a second location (up to 30%), requiring justification based on both Significant Predictors and Descriptive Statistics.

Use the output provided and analyze all relevant data without external resources for justification.

Paper For Above Instructions

The MATH302 final project provides an exceptional opportunity to apply analytical skills and knowledge accrued throughout the course. This project entails an in-depth analysis of cost-of-living data across different cities, focusing on significant predictors that influence expenses. The goal is to determine the most suitable locations for establishing a second outlet based on statistical evidence.

For this analysis, the data set comprises various cities’ cost of living indices, housing expenses, and prices of consumer goods. Notably, the cities include Mumbai, Prague, Warsaw, Athens, Rome, Seoul, Brussels, Madrid, Vancouver, Paris, Tokyo, Berlin, Amsterdam, New York, Sydney, Dublin, and London. The analysis centers on establishing which cities are the best candidates for opening a second location to maximize profitability.

The first crucial component of the project's requirements is identifying significant predictors affecting the cost of living. From the regression output provided, variables such as Rent, Monthly Public Transportation Pass, Loaf of Bread, Milk, Bottle of Wine, and Coffee are essential indicators. Understanding which predictors significantly affect the cost of living will guide decision-making in choosing the city for expansion.

Following the regression analysis results, one important significant predictor is Rent in City Centres. The regression coefficient shows a negative sign, indicating that as rent decreases, the cost of living index tends to be lower, which is an essential factor for businesses considering location. Given a significant p-value, this predictor significantly impacts the cost of living in the examined cities and should be taken into account when selecting a new location.

Additionally, examining the average costs of essential goods enables us to gauge the general affordability in potential relocation cities. If we consider the price of a loaf of bread, for example, cities with prices significantly lower than New York City, such as Mumbai at $0.41 or even Warsaw at $0.69, are attractive candidates for expansion. Comparatively, Rent and other living costs in such cities would permit a lower operating cost leading to higher profitability for a new outlet.

In conjunction with significant predictors, descriptive statistics will strengthen our analysis. Conducting an overview of the data provides valuable insights. The mean cost of living index across the cities is 75.49, while the median stands at 82.2. This means that half the cities have higher costs than the average, enabling a comparison of affordability. Cities like Mumbai and Prague, which fall significantly below both these statistical markers, represent potentially lucrative opportunities. Furthermore, when comparing the statistics for the first and third quartiles (Q1 and Q3), we observe that many cities lie well beneath the third quartile of the data and present unique potential for market entry.

Moreover, to fully satisfy the project requirements, it is equally important to justify recommendations based on both significant predictors and descriptive statistics. After analyzing the data thoroughly, it is recommended that the second location be opened in either Mumbai or Warsaw. Both cities demonstrate affordability combined with substantial market potential. For instance, Mumbai, with its cost of living index of 31.74, shows that it is significantly less expensive than New York City, with a living index of 100. Not only does this align with consumer affordability, but as observed, Rent in the city centre is also markedly lower, corroborating the potential desirability of such an expansion. Meanwhile, Warsaw's cost of living index of 45.45, alongside similar favorable statistics in other areas of living costs, makes it another strong candidate where market opportunities could thrive.

The insights from data analysis reveal that while cities like London may present a balanced lifestyle, their higher costs relative to New York and other cities position them as less desirable for establishment when profit margins are taken into account. The statistical outputs clearly show that cities with lower living costs and reasonable expenses for rent provide a competitive advantage for opening new locations. This type of analytical thinking is critical for strategic planning in business operations.

In conclusion, utilizing both significant predictors and descriptive statistics, the recommendations are anchored in solid evidence. The decision to explore Mumbai and Warsaw for the second location capitalizes on cost advantages, potential consumer base affordability, and favorable market conditions. Understanding and analyzing these data elements provide a comprehensive framework to ensure informed decision-making in business location strategy.

References

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• Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2004). Applied Linear Statistical Models. McGraw-Hill/Irwin.
• Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics. McGraw-Hill/Irwin.
• Greene, W. H. (2012). Econometric Analysis. Pearson Education.
• Wooldridge, J. M. (2020). Introductory Econometrics: A Modern Approach. Cengage Learning.
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