Coming soon
Regression Analysis
The students would be exposed to the concepts of correlation and regression. Emphasis will be laid on diagnostic measures such as autocorrelation, multicollinearity and heteroscedasticity. This course would prepare students to handle their data for analysis and Interpretation.
Students will able to
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Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
Paper Title |
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STT-222 |
Regression Analysis |
CO 33: Compute and interpret the results of Bivariate and Multivariate Regression and Correlation Analysis for forecasting
CO 34: Develop a deeper understanding of the linear regression model and its limitations
CO 35: Determine whether a regression model is significant.
CO 36: Recognize regression analysis applications for purposes of description and prediction.
CO 37: Recognize some potential problems if regression analysis is used incorrectly |
Approach in teaching: Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students: Assignments Seminar Presentation Subject based Activities |
Classroom Quiz Assignments Class Test Individual Presentation |
Introduction to correlation and its types, Measures of correlation coefficient, multiple and partial correlation, intra class correlation and correlation ratio. Problem of correlated errors: Autocorrelation , Durbin Watson Statistics, Removal of auto Correlation by transformation. Analysis of collinear data, Detection and correction of multicollinearity, Coefficient of determination.
Linear regression analysis, method of least square for regression curve fitting, regression coefficient and properties. Multiple and partial regression, examine the multiple regression equation, concept of weighted least square, regression equation on grouped data, various methods of selecting the best regression equation.
Linear estimation, Gauss-Markoff's theorem. Estimable functions, error and estimate space, normal equation and least square estimators, estimation of error variance, estimation with correlated observations, properties of least square estimators, generalized inverse of matrix and solution of normal equations, variance and covariance of least square estimators.
Linear model: fixed, random and mixed effects models. Analysis of variance, multiple comparisons test: Tukey, Scheffe and Student-Newmann-Kuel,Duncan.
Regression diagnostic, normal probability plot, Goldfeld-Quandt test, Park test, Breusch- godfrey, Logistic regression.
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