This article explains the principles of one‑way, two‑way and multi‑factor ANOVA, demonstrates how to perform them in R using aov(), lm() and anova(), and shows post‑hoc comparisons with TukeyHSD as well as correlation testing with cor() and cor.test().
This article explains the simple linear regression model, its six key assumptions, how to evaluate the fit using ANOVA and the coefficient of determination, and covers hypothesis testing and confidence intervals for regression coefficients with practical examples.
This article explains the fundamentals of multiple linear regression, covering model formulation, least‑squares estimation, ANOVA tables, adjusted R‑squared, standard error, hypothesis testing for coefficients, and the use of dummy variables for categorical predictors.
This article explains the concept of one‑factor (one‑way) ANOVA, walks through a lamp‑life example with four manufacturing processes, derives the within‑ and between‑group sum‑of‑squares formulas, and shows how to execute the test in Python using statsmodels.
This article explains the concept of one‑way ANOVA, walks through a real‑world example comparing four manufacturing processes, and demonstrates how to conduct the analysis in Python using statsmodels, interpreting the resulting F‑statistic and p‑value to assess significance.
