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.

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.

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

Requirements: 1200

Final Project Samples Walmart Sales: Walmart, being a multinational retail giant, collects copious amounts of data. Use this data to help Walmart answer the research questions below by fitting a parsimonious regression model. Also do 2 out of the 3 bullets listed below (your choice): ¥ Interpret the coefficients in the final model. ¥ Your boss claims holiday sales will be 10% more than on a regular day. Is this claim correct? Conduct a hypothesis test to test the claim. Hint: You may want to generate a confidence interval for the holiday sales coefficient (IsHoliday). ¥ It is believed that sales are significantly different across the different store types. Can you refute this claim using a statistical test? The data set consists of several variables including: 1. Store – The store ID 2. Date – Start date of the week 3. Temperature – average temperature in the region 4. Fuel_Price – cost of fuel in the region 5. CPI – the consumer price index in the region for the date specified 6. IsHoliday – a dummy variable indicating if the day is a holiday (1) or not (0) 7. Unemployment – the unemployment rate in the region for the date specified 8. Size – the size of the store in square feet 9. StoreType – The store is one of three types: either A, B, or C 10. Sales – Weekly sales in USD

Moneyball: The movie Moneyball is based on real events. The data set provided is the data set used by Billy Beane and Paul DePodesta. Fit a linear regression model to predict the number of wins. You will want to exclude the Rank for the season since it is almost synonymous with the number of wins. Also, exclude the playoff information from the primary model. Return a parsimonious model. Do 2 out of the 3 bullets listed below (your choice): ¥ Interpret the coefficients from the primary model. ¥ Create a data subset consisting of only the teams that made it to the playoffs (secondary or playoff model). Then, predict the rank in the playoffs. Use the variables that you think are relevant. Interpret the coefficients after returning a parsimonious model. ¥ It is believed that the leagues are significantly different in terms of the number of wins. Can you refute this claim using a statistical test? The variables are as follows: 1. Team 2. League 3. Year 4. Runs Scored (RS) 5. Runs Allowed (RA) 6. Wins (W) 7. On-Base Percentage (OBP) 8. Slugging Percentage (SLG) 9. Batting Average (BA) 10. Playoffs (binary) 11. RankSeason 12. RankPlayoffs 13. Games Played (G) 14. Opponent On-Base Percentage (OOBP) 15. Opponent Slugging Percentage (OSLG)

Vehicle Cost Prediction (1985): Managers wants to know how exactly the prices vary with different variables. For example, an automobile company wants to figure out the factors affecting the pricing of cars in market. A good pricing strategy will help company reduce potential costs, and design vehicles which possess desirable property for customers. Fit a parsimonious model to predict car price. Do 2 out of the 3 bullets listed below (your choice): ¥ Interpret the coefficients from the model. ¥ Your friend would like to buy a Porshe Boxter convertible (Car_ID = 129). The dealer is pricing the car at $38,000. Based on your findings should she buy this car? Perform a statistical test to see if a car with the characteristics falls within the 95% prediction interval. ¥ The company believes that a customer will pay 2000 more dollars if horsepower is 20 more. Can you refute the claim? The data set consists of several variables including: 1. Car_ID – Unique id of each observation (Interger) 2. Symboling – Its assigned insurance risk rating, A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.(Categorical) 3. CarCompany – Name of car company (Categorical) 4. Fueltype – Car fuel type i.e gas or diesel (Categorical) 5. Aspiration – Aspiration used in a car (Categorical) 6. Doornumber – Beneficiary’s residential area in the US, northeast, southeast, southwest, northwest. 7. Carbody – Individual medical costs billed by health insurance 8. Drivewheel – type of drive wheel (Categorical) 9. Enginelocation – Location of car engine (Categorical) 10. Wheelbase – Weelbase of car (Numeric) 11. Carlength – Length of car (Numeric) 12. Carwidth – Width of car (Numeric) 13. Carheight – height of car (Numeric) 14. Curbweight – The weight of a car without occupants or baggage. (Numeric) 15. Enginetype – Type of engine. (Categorical) 16. Cylindernumber – cylinder placed in the car (Categorical) 17. Enginesize – Size of car (Numeric) 18. Fuelsystem – Fuel system of car (Categorical) 19. Boreratio – Boreratio of car (Numeric) 20. Stroke – Stroke or volume inside the engine (Numeric) 21. Compressionratio – compression ratio of car (Numeric) 22. Horsepower – Horsepower (Numeric) 23. Peakrpm – car peak rpm (Numeric) 24. Citympg – Mileage in city (Numeric) 25. Highwaympg – Mileage on highway (Numeric)

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