Residuals
In statistical analysis, residuals are the differences between observed values and the corresponding values predicted by a statistical model. These differences are fundamental for understanding how well a statistical model fits the observed data and for diagnosing the appropriateness of the model assumptions.

Breusch-Pagan test for heteroskedasticity
The Breusch-Pagan test is a statistical test used to determine whether the variance of the errors in a regression model is constant or varies with respect to the predictor variables. In regression analysis, heteroscedasticity refers to the unequal scatter of residuals. Specifically, it refers to the case where there is a systematic change in the spread of the residuals over the range of measured values.

Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that the residuals come from a population that has homoscedasticity, which means constant variance. When heteroscedasticity is present in a regression analysis, the results of the analysis become hard to trust. One way to determine if heteroscedasticity is present in a regression analysis is to use a Breusch -Pagan Test.

Multi Linear Regression for Obesity, Inactivity, and Diabetics is a generalization of simple linear regression, in the sense that this approach makes it possible to evaluate the linear relationships between a response variable and several explanatory variables.