As a data analyst for the basketball team
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 dThe coach of the team and your management have requested that you come up with regression models that predict the number of wins ithat are included in the data set. These regression models will help make key decisions to improve the performance of the team. You will statistical analyses and then prepare a report of your findings to present for the team’s management. Since the managers are not data andescribe their practical implications.
Note: 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 https://www.kaggle.com/fivethirtyeight/fivet
Directions
For this project, you will submit the Python script Python script you used to make your calculations and a summary report summary report explaining your findings
1. Python Script Python Script: To complete the tasks listed below, open the Project Three Jupyter Notebook link in the Assignment Information Python scripts for your project. In the notebook, you will find step-by-step instructions and code blocks that will help you completeSimple Linear Regression Simple Linear Regression
Create scatterplots scatterplots
Compute the correlation coefficient correlation coefficient
Conduct a linear regression linear regression
Multiple Regression Multiple Regression
Create scatterplots scatterplots
Compute the correlation matrix correlation matrix
Conduct a multiple regression multiple regression analysis
2. Summary Report Summary Report: Once you have completed all the steps in your Python script, you will create a summary report to present yourreport. You must complete each each of the following sections:
Introduction Introduction: Set the context for your scenario and the analyses you will be performing.
Scatterplots and Correlation Scatterplots and Correlation: Discuss relationships between variables using scatterplots and correlation coefficients.
Simple Linear Regression Simple Linear Regression: Create a simple linear regression model to predict the response variable.
Multiple Regression Multiple Regression: Create a multiple regression model to predict the response variable.
Conclusion Conclusion: Summarize your findings and explain their practical implications
Introduction
As a data analyst for the basketball team, I have been tasked with creating regression models to predict the number of wins based on historical data. By leveraging statistical analyses, we aim to provide valuable insights that will inform key decisions to enhance the team's performance and success on the court.
Scatterplots and Correlation
To begin the analysis, I generated scatterplots to visualize the relationships between variables and computed correlation coefficients to quantify the strength and direction of these relationships. By examining these visualizations and numerical indicators, we can gain a better understanding of how different factors may influence the team's number of wins.
Simple Linear Regression
In the simple linear regression analysis, I developed a model to predict the number of wins based on a single independent variable. This model allows us to assess the linear relationship between a specific predictor and the outcome variable, providing insights into how changes in the predictor impact the team's performance.
Multiple Regression
Moving beyond simple linear regression, I conducted a multiple regression analysis to create a more comprehensive model that incorporates multiple predictors to forecast the number of wins. By considering a combination of factors simultaneously, this model offers a more nuanced perspective on the complex interplay of variables that contribute to the team's success.
Conclusion
In conclusion, the regression models developed in this analysis offer valuable predictive capabilities that can inform strategic decisions to improve the team's performance. By leveraging these insights, the coaching staff and management can optimize their strategies, player selection, and training programs to maximize the team's chances of success on the court. The practical implications of these findings extend beyond statistical predictions, empowering the team to make data-driven decisions that drive continuous improvement and competitive advantage in the dynamic landscape of professional basketball.