Dataset Link: (https://www.kaggle.com/zelphy/nba-playerdata-for-20182019/data)
Our .py files: Regression File T-Test File Histogram & Scatterplot File
Our project is investigating how a players age in the NBA is important towards their usage rate. To be more precise, usage rate is an estimate of the percentage of team plays used by a player while he was on the floor. Our two variables Age and Usage Rate have correlation as they both depend on eachother. Players who are older are more dependent and used more on a court rather than being much younger or older.
With our t-test test, we want to check if our hypothesis was correct as it is a testing tool avaliable for us. Our regression test allows us to examine the relationship between two or more variables.
Null hypothesis: Coefficient is not significantly different from zero. Alternate hypothesis: Coefficient is significantly different from zero.
Since the p-value is less than the alpha level (0.05), then we reject the null hypothesis that the coefficient is not equal to zero. In other words, age is a significant predictor of usage rate in the NBA. We have regression graphs, scatterplots, and histograms to prove how age is significant towards usage rate. © 2019 GitHub, Inc.