Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
1 Conditional expected value, the concept of regression and model building Source reading
2 The creation of a simple linear regression model, the least squares estimators for the parameters, centered model Source reading
3 Properties of least squares estimators of parameters Source reading
4 Estimation error variance and examination of the properties of the fitted regression model Source reading
5 Maximum likelihood estimation of error variance and regression parameters Source reading
6 Tests of hypotheses about the parameters, test for significance of regression Source reading
7 Preparation and explanation of how to use the ANOVA table, examination of the coefficient of determination Source reading
8 Midterm exam Review the topics discussed in the lecture notes and sources
9 Interval estimation of parameters, the interval estimation of the mean response, prediction of new observations Source reading
10 Regression through the origin, examination of the assumptions of the model (residual analysis), investigation of heteroskedasticity, normal probability graphics Source reading
11 Introduction to outliers and influential observations and examination of their effects on the the least squares estimators Source reading
12 Fitting multiple regression model, matrix notation and estimation of the regression parameters Source reading
13 Examining the distributional properties of least squares estimators of regression parameters, and the error variance Source reading
14 The creation of multiple regression ANOVA table and tests of hypotheses about the parameters of the regression Source reading
15 Determination of the influential observations in multiple regression Source reading
16-17 Final exam Review the topics discussed in the lecture notes and sources

Recommended or Required Reading
Additional Resources
Montgomery, D. C., Peck, E. A., Vining, G. G. (2001), Introduction to Linear Regression Analysis, 3rd edition, John Wiely & Sons Inc.