Statistics Tutorials and Data Analysis Guides

A verified 649-student guide to random-intercept modeling, fixed effects, variance components, ICC, clustered prediction and Python, R, SPSS and Excel diagnostics.

A verified 649-case R Squared guide covering sums of squares, adjusted fit, nested changes, predictor comparisons, diagnostics, and matched Python, R, and SPSS evidence.

Binary probabilities estimated with the standard normal cumulative distribution Probit Regression: Formula, Interpretation, Python, R, SPSS and Excel Guide Probit Regression models a binary outcome by…

PCA dimension reduction, validated component selection and regression Principal Component Regression: Formula, Interpretation, Python, R, SPSS and Excel Guide Principal Component Regression turns correlated predictors into…

Curved relationships estimated with linear-model machinery Polynomial Regression: Formula, Interpretation, Python, R, SPSS and Excel Guide Polynomial Regression extends a linear model by adding squared, cubic,…

Supervised dimension reduction for correlated and high-dimensional predictors Partial Least Squares Regression: Tutorial, Interpretation, Python, R, SPSS and Excel Guide Partial Least Squares Regression builds outcome-focused…

Repeated observations, within-unit change, and between-unit differences Panel Regression: Formula, Interpretation, Fixed Effects, Python, R, SPSS and Excel Guide Panel Regression analyses repeated measurements from the…

Continuous-outcome modelling, prediction, and adjusted effects Ordinary Least Squares Regression: Formula, Interpretation, SPSS, Python, R and Excel Guide Ordinary Least Squares Regression estimates the relationship between…

Ordered-category modeling with cumulative logits Ordinal Logistic Regression: Formula, Interpretation, SPSS, Python, R and Excel Guide Ordinal Logistic Regression models an outcome whose categories have a…