Regression analysis: simple linear regression, measures of model adequacy, residual analysis, transformations, inference for slope and intercept, confidence and prediction intervals for future responses. Multiple linear regression analysis: estimation of model parameters, inference regarding model parameters and predictions, analysis of variance, regression diagnostics, variable selection and model building. Non-parametric methods: sign test, signed-rank test, rand-sum test, runs test. Kruskal Wallace test, rank correlation coefficient checking distributions: Q-Q plots and Kilmogorov Smirnov Test. -- Course Website
Prerequisites: 310532 (v.1)<br/> Statistical Data Analysis 102<br/> <br/> or any previous version
