Multiple linear regression is a widely used statistical method for modelling the relationship between a dependent variable and one or more independent variables. In this analysis, we aim to develop a robust and interpretable linear regression model while addressing common challenges such as multicollinearity, overfitting, and variable selection. Key steps include evaluating the significance of predictors using the F-statistic and p-values, assessing multicollinearity through variance inflation factors, and exploring the impact of reducing the number of variables on model performance.

Flexdashboard - R

Bu dosyada OLC731 dersinin 11. haftasına ilişkin ders notları yer almaktadır.

PPV isolates from wild boars in Russia.