Regression

Regression: Linear Models in Statistics introduces the fundamentals of regression - a core element of statistical analysis that models a dependent variable through a linear combination of one or more predictors, accompanied by random error. The text establishes a bridge between basic statistical principles and more advanced theory, ensuring that readers gain both a solid grounding in one-dimensional statistics and the essential tools for tackling problems in higher dimensions. The book guides students through a spectrum of topics, beginning with simple linear regression and analysis of variance (ANOVA), and expanding into more complex areas such as multiple linear regression and analysis of covariance (ANCOVA). Additional chapters cover special topics including non-parametric regression, mixed models, time series analysis, spatial processes, and experimental design. Written with 2nd and 3rd year undergraduates in mind, Regression: Linear Models in Statistics offers numerous worked examples and fully solved exercises, providing a comprehensive resource for anyone looking to deepen their understanding of statistical modeling techniques.