The linear regression model suggests that we can describe one variable (the dependent variable) based on the values of another variable (the independent variable). In our dataset, we have identified three key parameters: diabetes, inactivity, and obesity. Consequently, the variables of interest are represented as percentages of diabetes, inactivity, and obesity. As covered during the class, when determining the percentage of diabetes, we employed the percentage of inactivity as the independent variable, resulting in the equation % diabetes = α + β % inactivity + ε. Similarly, we can extend this approach to multiple linear regression by incorporating two independent variables: the percentage of inactivity and the percentage of obesity. The equation for this extended model would be % diabetes = α + β1 % inactivity + β2 % obesity + ε.
If the Kurtosis is positive then the data exhibits more outliers, whereas it is negative then it exhibits less outliers than the normal distribution. The heteroscedastic occurs when the variance of the data varies widely with more number of outlires.
Hi, this is a comment.
To get started with moderating, editing, and deleting comments, please visit the Comments screen in the dashboard.
Commenter avatars come from Gravatar.