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B. Stat. – 202 |
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| Aims of this Course | ||||||||||||||||||
| The aim of this course is twofold: to provide you with an overview of the most common techniques used to quantify regression analysis, and to enlighten how to establish relationship and model building techniques. | ||||||||||||||||||
| Objectives of this Course | ||||||||||||||||||
| After completing this course, the students should | ||||||||||||||||||
| » | understand all the features of regression analysis; | |||||||||||||||||
| » | apply and fit appropriate regression model according to the nature of data; | |||||||||||||||||
| » | critically analyzing results obtained from fitting regression model; | |||||||||||||||||
| » | develop the capability of model building strategies; | |||||||||||||||||
| Learning outcomes of this course | ||||||||||||||||||
| At the end of the course, the students will be able to | ||||||||||||||||||
| » | know when it is appropriate to use a regression model; | |||||||||||||||||
| » | know what a regression analysis allows you to do; | |||||||||||||||||
| » | know what is the appropriate techniques of formulating, analyzing and forecasting of bivariate/multivariate data; | |||||||||||||||||
| » | know the interpretation the result of the model fitting | |||||||||||||||||
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| Course Contents | ||||||||||||||||||
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Multiple Linear Regression: Estimation of parameters with their properties and Residual analysis for multiple and restricted linear regression through OLS, GLS and WLS methods. Inverse regression.
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Estimation Problems in Linear Regression Model: Identification, test and possible solutions for the problems of Mutlicollinearity, Heteroscedasticity, Autocorrelation, Errors in Variables and Errors in Equations.
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Model Building and Adequacy: Variable selection and model building through stepwise regression procedure, Cp criterion, Akaike information criteria, Schwartz criteria, detection of outlier, influential observations, and high leverage points. Test for significance of the model and model parameters, contribution, Lack of fit and pure error. Model adequacy.
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Dummy Variable Regression: Introduction, uses and applications in regression analysis as independent variable(s). Dummy variable trap. Uses and applications in regression analysis as dependent variable such as: linear probability, logit, probit and tobit models. Logistic regression with its uses, interpretations and test for goodness of fit.
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| Main Books Recommended: | ||||||||||||||||||
| 1) | Draper, N. R. and H. Smith (2003). Applied Linear Regression, 3rd ed., Wiley, N.Y. | |||||||||||||||||
| 2) | Montgomery, D. C., E. A. Peck, & G. G. Vining (2012). Introduction to linear regression analysis (Vol. 821). John Wiley & Sons. [Solution] | |||||||||||||||||
| 3) | Weisberg, S. (2014). Applied linear regression. John Wiley & Sons. | |||||||||||||||||
| References: | ||||||||||||||||||
| 4) | Birkes, D., & Y. Dodge (2011). Alternative methods of regression (Vol. 190). John Wiley & Sons. | |||||||||||||||||
| 5) | Crawford, S. L. (2006). Correlation and regression. [Fox, Rawlings]. | |||||||||||||||||
| 6) | Dobson, A. J. (2001). An introduction to generalized linear models. CRC press. | |||||||||||||||||
| 7) | Washington, S. P., M. G. Karlaftis & F. L. Mannering (2010). Statistical and econometric methods for transportation data analysis. CRC press. | |||||||||||||||||