Datasets for the book - Applied Multivariate Statistics with Python

The book, Applied Multivariate Statistics with Python, offers a practical introduction to modern statistical analysis. It combines clear explanations with hands-on applications. It begins with core topics such as hypothesis testing and key probability distributions, then moves into experimental design and ANOVA techniques. Readers learn how to implement these methods using Python, making the material directly applicable.

The book also covers multiple regression, including model building and common challenges like multicollinearity and heteroscedasticity. Advanced topics such as MANOVA, discriminant analysis, cluster analysis, and logistic regression are presented in an accessible way. It concludes with machine learning and neural networks for predicting both cross-sectional and time-series data.

The datasets used in the book are posted here for easy access.