This project has to be done using ‘R’. 2 files for the report, the rmd, and pdf.

This project has to be done using ‘R’. 2 files for the report, the rmd, and pdf. The pdf has to come from the knit that R has. Also, you have to share the data in the excel file (.csv). Attached you will find examples for different project.
Also I need the notebook with the code from rstudio and the data set excel(.csv)
Project: NBA season spanning from 2019 to 2022
Data set description:
We will collect and analyze data for the NBA season spanning from 2019 to 2022 to explore factors influencing team performance in terms of regular season wins. The dataset will include information on various variables for each NBA team during this period. Here are the variables for the proposed dataset:
Team: The NBA team’s name.
Year: The year of the NBA season (2019, 2020, 2021, 2022).
Games Played (G): The total number of games played by the team in the regular season.
Points Scored (PTS): The total number of points scored by the team in the regular season.
Points Allowed (PTA): The total number of points the team’s defense allows in the regular season.
Wins (W): The total number of regular seasons wins achieved by the team.
Losses (L): The total number of regular-season losses.
Winning Percentage (WP): The ratio of wins to games played, calculated as W/G.
Field Goal Percentage (FG%): The team’s field goal shooting percentage.
Three-Point Percentage (3P%): The team’s three-point shooting percentage.
Free Throw Percentage (FT%): The team’s free throw shooting percentage.
Assists (AST): The total number of assists made by the team.
Rebounds (REB): The total number of rebounds collected by the team.
Turnovers (TOV): The total number of turnovers committed by the team.
Steals (STL): The total number of steals made by the team.
Blocks (BLK): The total number of blocks recorded by the team.
Playoffs (binary): A binary variable indicating whether the team made it to the playoffs (1) or not (0).
Grading
Rubric for Report
Abstract (approximately 1/4 page)
Introduction (including motivation) section
Analysis – Descriptive analysis of the data or after the regression
modeling. Examples – Correlation matrix, scatter plots, bar charts,
outlier testing, supplemental modeling.
Modeling – Specify a parsimonious model that contains no more than 8
predictor variables
Bullet 1
Bullet 2
Diagnostics – Provide residual plot(s) and plot(s) discussing
normality/linear regression assumptions.
Conclusion section
Neatness (no credit will be given for reports with R code or data printed
inside the report)
Group member assessment