project 3

 In this project, you will demonstrate your mastery of the following competency: Perform regression analysis to address an authentic problem Scenario You are a data analyst for a basketball team (you will use the same team for all three projects) and have access to a large set of historical data that you can use to analyze performance patterns. The coach of the team and your management have requested that you come up with regression models that predict the number of wins in a regular game based on the performance metrics that are included in the data set. These regression models will help make key decisions to improve the performance of the team. You will use the Python programming language to perform the statistical analyses and then prepare a report of your findings to present for the team’s management. Since the managers are not data analysts, you will need to interpret your findings and describe their practical implications. 

Note: This data set has been “cleaned” for the purposes of this assignment. 

 Reference FiveThirtyEight. (April 26, 2019). FiveThirtyEight NBA Elo dataset. Kaggle. Retrieved from 

Directions For this project, you will submit the Python script you used to make your calculations and a summary report explaining your findings. 

 Python Script: 

To complete the tasks listed below, open the Project Three Jupyter Notebook link in the Assignment Information module. This notebook contains your data set and the Python scripts for your project. In the notebook, you will find step-by-step instructions and code blocks that will help you complete the following tasks: Simple Linear Regression Create scatterplots Compute the correlation coefficient Conduct a linear regression Multiple Regression Create scatterplots Compute the correlation matrix Conduct a multiple regression analysis 

Summary Report: Once you have completed all the steps in your Python script, you will create a summary report to present your findings. Use the provided template to create your report. You must complete each of the following sections: 

Introduction: Set the context for your scenario and the analyses you will be performing. 

Scatterplots and Correlation: Discuss relationships between variables using scatterplots and correlation coefficients. 

Simple Linear Regression: Create a simple linear regression model to predict the response variable. Multiple 

Regression: Create a multiple regression model to predict the response variable. Conclusion: Summarize your findings and explain their practical implications.

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