Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
Explores Multi-factor ANOVA, analyzing variance explained by multiple factors and their interactions, providing practical hints on decomposing total sum of squares.
Covers linear regression basics, focusing on minimizing error using the principle of least squares and includes an ANOVA table and practical example in R.
Explores linear regression fundamentals, non-linear regression issues, and R-squared goodness of fit, with examples like Anscombe's quartet and the Datasaurus dataset.
