Applied Regression Analysis

The course covers the basics of simple linear regression: parameter estimation and model fitting; model checking (R-square, residual plot an PP- plot); prediction; inference about parameters; linear correlation and inference about correlation coefficient; The multiple linear regression: model assumptions, model fitting; R-square; partial correlation coefficients;model diagnostics, partitioning sum of squares, ANOVA table construction, test of hypoth esis, prediction, dummy variables; effects of departures from model assumptions; model building strategy, polynomial regressions.