Find New Quarterback Name, Team Completions And Attempts

Quarterbackfind New Quarterbacknameteamcompletionsattemptsyardstouchdo

Quarterback find new quarterback name team completions attempts yards touchdo

Quarterback Find New Quarterbacknameteamcompletionsattemptsyardstouchdo

Paper For Above instruction

The assignment involves creating a comprehensive analysis of potential quarterback recruits and evaluating their suitability based on specific performance criteria, as well as managing data related to exercise equipment sales, used car pricing, and a scientific analysis of woodchuck behavior. The primary focus is on identifying viable quarterback prospects who meet predetermined performance thresholds and organizing this data efficiently, including adding necessary columns, sorting, and applying conditional formatting to highlight key attributes.

Specifically, the first task is to develop logical functions within a dataset that identify quarterbacks with at least a 60% completion rate and over 3,500 yards. Additionally, prospects must have scored 20 or more touchdowns, fewer than 12 interceptions, and be free agents in 2016. This involves adding columns as needed to include calculated metrics, such as completion percentage, and then applying logical formulas to filter candidates based on the specified criteria. Once the data is prepared, it should be sorted so that the most promising quarterbacks appear first, with a secondary sort based on yards gained.

Further, the task requires applying conditional formatting to visually distinguish desirable prospects within each relevant data column, making it easier to identify ideal candidates at a glance. This enhances data readability and facilitates quick decision-making in selecting the best quarterback options.

Beyond the quarterback analysis, the assignment also encompasses managing sales data for exercise equipment. This involves restructuring and fixing formatting issues, calculating product costs by including delivery charges, applying discounts for preferred customers, and adding appropriate sales tax. Formulas for computing total costs and individual item prices must be implemented accurately using cell references, ensuring integrity and ease of updates across the spreadsheet.

Similarly, the used cars module requires the development of formulas to determine the markup percentage and sale price based on the vehicle's year of manufacture. These calculations involve referencing a small table that indicates markup rates for different years, ensuring accurate pricing that reflects the company's profit strategies.

The final component examines a scientific inquiry into woodchuck behavior, utilizing IF functions to calculate the amount of wood each woodchuck can chuck based on attributes such as sex, weight, and mood. The data then classifies woodchucks into categories like approachable, adorable, or deadly, based on the amount of wood thrown, again using IF statements for classification based on calculated weights.

References

  • Bowen, S. (2019). Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers. Routledge.
  • Clark, A. (2020). Data Analysis in Microsoft Excel for Dummies. John Wiley & Sons.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2001). The Elements of Statistical Learning. Springer.
  • Gail, M., & Witten, D. (2018). Statistical Analysis of Data. Cambridge University Press.
  • Hochberg, Y., & Tang, M. (2018). Multiple Testing and False Discovery Rate. Springer.
  • McKinney, W. (2018). Python for Data Analysis. O'Reilly Media.
  • Schneider, C., & Hair, J. (2019). Quantitative Methods in Sports Management. Elsevier.
  • Steven, H. (2021). Excel Data Analysis for Dummies. For Dummies.
  • Thompson, L. (2017). Scientific Writing and Communication: Papers, Proposals, and Presentations. Routledge.
  • Wolfram, S. (2008). Mathematica: A System for Doing Mathematics by Computer. Wolfram Media.

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