Unit goals — main aim?
Framework for statistical analyses using GLM.
Assessment tasks — components?
Data-analysis task, practical project, final exam.
Regression — definition?
Predicts a numeric outcome y from predictors x.
Residual — what?
Difference between observed y and predicted $\hat{y}\
R2 — meaning?
Proportion of y variance explained by the model.
Simple linear regression — equation?
$\hat{y} = ext{intercept} + ext{slope} imes x$.
Model fit — goal?
Minimize residual variation, maximize explained variation.
Regression assumptions — key?
Linearity, independence, normal residuals, homoscedasticity.
Multiple regression — workflow step?
Univariate summaries, bivariate plots, fit full then reduced model.
Data screening — purpose?
Check distributions, outliers, collinearity.
Collinearity — indicator?
High correlation ($|r| > 0.7$ or $>0.8$) among predictors.
VIF — what?
Variance inflation factor; measures multicollinearity.
Residual independence — check?
Residuals should show no systematic pattern over observations.
Normal residuals — check?
Normal probability plot and Shapiro-Wilk test.
Homoscedasticity — meaning?
Constant residual spread across predicted values.
Linearity — assessment?
Residuals vs predicted plot and residuals vs each predictor.
Sample size rule — minimum?
n > 5p; preferably n > 10p.
Collinearity heuristics — when?
$|r| > 0.7$ possible, $|r| > 0.8$ definite.
Tolerance — relation?
Tolerance = 1 / VIF; <0.1 indicates concern.
Regression diagnostics — purpose?
Check residual independence, normality, homoscedasticity, linearity.
Answer 20 questions on Regression Analysis Fundamentals.
1. What is the main goal of the unit's overall approach to statistical analysis?
2. Which statement best describes how the unit's lectures are delivered and accessed later?
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