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B. Stat. – 304 |
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| Aim of the course | ||||||||||||||||||
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The aim of this course is to provide you with an overview of the most common techniques used to quantify logistic regression, quantile regression and robust regression analysis and to handle binary variables, outliers etc.
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| Objectives of the course | ||||||||||||||||||
| After completing this course, the student should | ||||||||||||||||||
| ⇒ | understand the feathers of various logistic regressions | |||||||||||||||||
| ⇒ | apply and fit appropriate regression model according to the nature of the data | |||||||||||||||||
| ⇒ | analyzing results with unusual observations | |||||||||||||||||
| Learning Outcomes | ||||||||||||||||||
| At the end of the course, the student will be able to | ||||||||||||||||||
| ⇒ | know when it is appropriate to use a regression model. | |||||||||||||||||
| ⇒ | how to interpret results from logistic regression models | |||||||||||||||||
| ⇒ | how to present results from regression models in publication-quality tables. | |||||||||||||||||
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think about how to interpret and evaluate the regression-based research of others, and how to produce their own unique research based on regression.
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| Course Contents | ||||||||||||||||||
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Non-linear Regression: Introduction. Different types, Polynomial and orthogonal regression models.
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Models with one or more than one explanatory variables, Transformation of the models, Analytical methods for selecting a transformation, Box and Cox transformation.
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Binary Variables and Logistic Regression: Generalized linear models, Dose response models, General logistic regression model, Goodness of fit statistics, Residuals.
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Nominal and Ordinal Logistic Regression: Introduction, Multinomial distribution, Normal logistic regression, Ordinal logistic regression, General comments.
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Count Data, Poisson Regression and Log-linear Models: Introduction. Poisson regression. Examples of contingency tables. Probability models for Contingency tables. Log-linear models. Inference for log-linear models.
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Quantile Regression: Introduction, Methods–quantile, Inter-quantile, simultaneous quantile and bootstrapped quantile regressions, Influential observations, Outlier, High leverage points.
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Robust Regression: Group deletion, Masking and swamping, Breakdown point and Robust estimators, Least median of squares technique, Reweighted least squares residuals, Detection of multiple outliers.
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Stochastic Regression: Introduction, Asymptotic properties of OLS estimators, Errors in variable and Errors in equation, Specification error, Instrumental variable in regression analysis.
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| Main Books Recommended: | ||||||||||||||||||
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1)
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Montgomery, D. C., E. A., Peck & G. G. Vining (2012). Introduction to linear regression analysis (Vol. 821). John Wiley & Sons. [Solution]
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2)
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Ryan, T. P. (2008). Modern regression methods (Vol. 655). John Wiley & Sons. | |||||||||||||||||
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3)
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Weisberg, S. (2014). Applied linear regression. John Wiley & Sons. | |||||||||||||||||
| References: | ||||||||||||||||||
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4)
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Birkes, D. & Y. Dodge (2011). Alternative methods of regression (Vol. 190). John Wiley & Sons. | |||||||||||||||||
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5)
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Chatterjee, S., & A. S. Hadi (2009). Sensitivity analysis in linear regression. John Wiley & Sons.
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6)
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Cook, R.D. and S. Weisberg (1982). Residuals and Influence in Regression. Chapman and Hall, London. | |||||||||||||||||
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7)
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Draper, N. R. and H. Smith (2003). Applied Linear Regression, Wiley, N.Y. | |||||||||||||||||
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8)
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Gujarati, D. N. (2012). Basic econometrics. Tata McGraw-Hill Education. | |||||||||||||||||
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9)
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Johnston, J. (1997). Econometric Methods, 4th ed., McGraw‑Hill, N.Y. | |||||||||||||||||
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10)
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Rousseeuw, P. J., & A. M. Leroy (2005). Robust regression and outlier detection. John Wiley & Sons.
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11)
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Seber, G. A. F. and Wild (1989). Nonlinear Regression, Wiley, N.Y. | |||||||||||||||||
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