I studied the Breusch-Pagan test in detail and learnt how it is used to assess the presence of heteroscedasticity in a regression model. Heteroscedasticity refers to the situation where the variance of the error terms in a regression model is not constant across all levels of the independent variables, violating one of the key assumptions of linear regression. The Breusch-Pagan test evaluates whether there is a significant relationship between the squared residuals of a regression model and the independent variables. If the test indicates a significant relationship, it suggests the presence of heteroscedasticity, and adjustments or transformations may be necessary to address this issue in the regression analysis.
The major idea is to find collinearity between the data points inactivity and obesity by plotting linear models of the same in order to predict trends in diabetes. Here, as suggested by the professor we are using a top-down approach where we would be backtracking the conclusion to the cause. This kind of a backward mapping method is very beneficial in the data science world today as it is the trends and predictions of data that people are mostly captivated by.