Summary
In this chapter, we explored the powerful world of linear and logistic regression using Excel data. Linear regression, a fundamental statistical technique, allows us to model relationships between dependent and independent variables. We discussed its assumptions and applications, and walked through the entire process of loading data from Excel, preparing it for analysis, and fitting linear regression models using both R (using base R and tidymodels) and Python (with the scikit-learn and statsmodels libraries).
Through comprehensive code examples, you learned how to perform regression analysis, assess model accuracy, and generate valuable statistics and metrics to interpret model results. We gained insights into creating diagnostic plots, such as residual plots and Q-Q plots, which aid in identifying issues such as heteroscedasticity and outliers.
Additionally, we delved into logistic regression, a powerful tool for class probability prediction and binary classification...
