A statistical analysis of an artificial fitness database

This RPubs report presents an analysis of hypothesis testing in multiple linear regression using the BRFSS 2020 dataset. The study examines factors associated with the number of mentally unhealthy days reported by U.S. adults. The analysis fits regression models including predictors such as physically unhealthy days, sleep hours, age, income, sex, and exercise. Different hypothesis testing methods are used to determine whether these variables significantly contribute to the model. The report demonstrates the use of the overall F-test, Type I and Type III sums of squares, partial F-tests, t-tests for regression coefficients, and chunk tests to evaluate the importance of individual variables and groups of variables in explaining mental health outcomes.

Model creation using multiple linear regression from EDA to predictions

Problems 9, 10, 11.

In class notes and homework on the bottom