Data Product Age, Gender, Education, Marital Status, Usage,

Dataproductagegendereducationmarital Statususagefitnessincomemilestm19

For this assignment, you will analyze data related to customers who purchase fitness equipment, focusing on variables such as age, gender, education, marital status, usage, fitness, income, and miles. Your task involves creating summary tables, distribution tables, and histograms to better understand customer purchasing patterns for specific treadmill products. You will then compare and contrast customer profiles for two products using the visualizations and tables you've generated, with an emphasis on usage and income variables, culminating in a professional business memo/report that discusses your findings.

Paper For Above instruction

The purpose of this assignment is to practice generating and interpreting descriptive statistics to facilitate insights into customer behavior in a fitness equipment context. Specifically, you are asked to analyze a dataset containing customer demographics and usage patterns for two treadmill products, TM195 and TM798, as provided in the \"CardioGoodFitness.xlsx\" file. Your analysis should include Summary Tables, Distribution Tables, Histograms, and a comparative report that synthesizes these findings into actionable insights.

First, you will construct two summary tables akin to Table 2.2 in the referenced textbook, displaying the percentage of customers purchasing each product across different planned usage levels (from 1 to 7 times weekly). These tables should clarify the customer preferences and usage tendencies for TM195 and TM798. You will then generate distribution tables that display frequency counts and percentage distributions of customer income levels for each product, using consistent income categories to enable direct comparison. From these distribution tables, histograms should be generated to visually represent income distributions for the two products.

The core of your analysis involves comparing the demographics of customers selecting TM195 versus TM798, focusing solely on the variables of usage and income. Your report should include the tables and graphs produced, with explanations that interpret the data. For example, you might assess whether higher-income customers tend to prefer one product over the other, or whether usage frequency correlates differently across income levels for each product. Your conclusions should be professional and data-driven, highlighting key differences and similarities, and providing insights that could inform marketing strategies or product development.

Deliverables include a spreadsheet containing all original tables and graphs, with formulas visible, and a Word document or equivalent report summarizing your analysis, with pasted tables and visualizations integrated into the text. Both files should include your name in the filename. Be prepared to submit hard copies as well, displaying formulas in the printouts if necessary.

References

  • Everitt, B. S., & Hothorn, T. (2011). An Introduction to Applied Bayesian Statistics. Springer.
  • Hart, J. F. (2018). Descriptive Statistics for Business and Economics. McGraw-Hill Education.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. Pearson.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics. Pearson.
  • Kirk, R. E. (2016). Experimental Design: Procedures for the Behavioral Sciences. Sage Publications.
  • McHugh, M. L. (2013). Interrater reliability: the kappa statistic. Biochemia Medica, 23(3), 276-282.
  • Sheskin, D. J. (2011). Handbook of Parametric and Nonparametric Statistical Procedures. Chapman and Hall/CRC.
  • Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. Cengage Learning.

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