Linear and Multiple Linear Regression

Linear regression is a statistical technique that involves estimating the value of one variable based on the values of other variables. In the context of our class discussion, we explored how to predict the percentage of diabetes based on the percentage of inactivity alone, represented as (%diabetes = α + β %inactivity + ε), where % diabetes is considered the dependent variable, and % inactivity serves as the independent variable. We can also extend this approach to a multiple linear regression method, which incorporates more than one independent variable, such as (%diabetes = α + β1 %inactivity + β2 %obesity + ε).

When conducting multiple linear regression to predict %diabetes using both %inactivity and %obesity as independent variables, it’s important to note that we have a limited dataset comprising only 354 data points. In this scenario, we need to construct a model that describes the relationship between %diabetes and these two independent variables based on this relatively small dataset.