Evaluation And Grading Of Your Final Project Math 302 Final

Evaluation/Grading of your Final Project Math 302 Final Project will open up Friday morning

In the final project, you are given a data set and a regression output. The concept of a data set should be something that you are familiar with because you collected one during Week 1. There are descriptive statistics that go along with said data set, which should also be familiar because you calculated descriptive statistics during Week 2. The Regression output won’t look familiar to you until Week 7.

Once you go through the Lessons and the Discussion Forum, (particularly your second response post) you should be familiar on how to run a Regression and what a Regression output looks like from the ToolPak. By the end of Week 7, you will have all the information needed to write up the Final Project. There is nothing new that you learn in Week 8 needed for the write up of the final project.

Paper For Above instruction

The final project for Math 302 requires students to analyze a given data set and its regression output, focusing specifically on determining the best city options for opening a second location based on the cost of living factors. The data set, which students would have compiled during Week 1, includes various cities' statistics on costs and expenses. The descriptive statistics related to these data points, such as means, medians, and quartiles, were calculated by students during Week 2.

The regression output, introduced in Week 7, involves analyzing coefficients, significance levels, and regression statistics like R-squared, to determine which predictors significantly affect the cost of living. The analysis involves assessing the significance of predictors using P-values and comparing them to the alpha threshold to determine which are statistically significant. Students are expected to interpret these results, establishing logical connections between significant predictors and practical implications for choosing cities for expansion.

The assignment emphasizes critical thinking supported solely by statistical output, requiring students to justify their city selections based on the combination of significant predictors and descriptive statistics. In particular, students should interpret the descriptive statistics for significant predictors, comparing each city's costs relative to New York City, which serves as a baseline. This comparison involves examining means, medians, quartiles, and the relative costs, considering whether costs are higher or lower than NYC in each city.

Furthermore, the project requires students to recommend at least two cities for opening a second location, providing comprehensive justification rooted in the data. This justification must incorporate both the statistical significance of predictor variables and the detailed descriptive statistics, demonstrating an in-depth understanding of how these factors influence the decision-making process. Analysis should leverage the regression results and descriptive insights to evaluate whether the cost of living differences make a city more or less viable compared to NYC, considering overall affordability and cost structures.

Throughout the project, students are instructed to avoid using outside resources for justifications. Instead, conclusions should be entirely supported and justified with the regression output and descriptive statistics. The entire task emphasizes interpretation and critical analysis of quantitative data, with the goal of making informed, data-driven recommendations for business expansion locations.

References

  • Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (5th ed.). McGraw-Hill Education.
  • Kurzweil, R. (2004). The Age of Spiritual Machines: When Computers Exceed Human Intelligence. Viking.
  • Montgomery, D. C., Peck, J. P., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). Wiley.
  • Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach (5th ed.). Cengage Learning.
  • Damodar N. Gujarati & Dawn C. Porter (2009). Basic Econometrics. McGraw-Hill.
  • Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics (3rd ed.). Pearson.
  • Acock, A. C. (2014). A Gentle Introduction to Structural Equation Modeling. Guilford Publications.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). Sage Publications.
  • Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis (3rd ed.). Wiley.
  • Gujarati, D., & Ported, D. (2003). Basic Econometrics. McGraw-Hill.

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