Regression Analysis

Paper Code: 
STT 222
Credits: 
5
Contact Hours: 
75.00
Max. Marks: 
100.00
Objective: 

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 be able to

Course

Learning outcomes (at course level

Learning and teaching strategies

Assessment Strategies

 

Paper Code

Paper Title

STT-222

Regression Analysis

CO 36: Learn how to Compute and interpret the results of Bivariate and Multivariate Regression and Correlation Analysis for forecasting.

 

CO 37: Develop a deeper understanding of the linear regression model and its limitations.

 

CO 38: Determine whether a regression model is significant.

 

CO 39: Recognize regression analysis applications for purposes of description and prediction.

 

CO 40: 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

 

15.00

Unit-I                                                                                                        

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.

15.00

Unit –II                                                                                                               

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

15.00

Unit-III                                                                                                                

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

15.00

Unit-IV                                                                                                                 

Linear model: fixed, random and mixed effects models. Analysis of variance, multiple comparisons test: Tukey, Scheffe and Student-Newmann-Kuel,Duncan

15.00

Unit-V                                                                                                                   

Regression diagnostic, normal probability plot, Goldfeld-Quandt test, Park test, Breusch- godfrey, Logistic regression.

Essential Readings: 
  • Arnold, B.C., Balakrishnan, N. & Nagaraja, H.N. (1992): A First Course in Order Statistics. John Wiley.
  • David, H.A. & Nagaraja, H.N. (2003): Order Statistics. 3rd Ed. John Wiley.
  • 3.Goon, Gupta & Das Gupta. (2003): Outline of Statistical Theory. Vol. I, World Press,14th edition.
  • Hogg, R.V. and Craig, A.T.(2009): Introduction to Mathematical Statistics, McMillan.
  • Johnson, S. and Kotz. (1972): Distribution in Statistics, Vol.I, II. And III, Houghton and Muffin.

 

 

References: 

SUGGESTED READINGS:

 

  • Kendall, M.G.and Stuart. (1996): An Advanced Theory of Statistics, Vol. I,II. Charls Griffin.
  • Mood,A.M., Graybill, F.A. and Boes, D.C.(1974): Introduction to the Theory of Statistics, McGraw Hill,third edition.
  • Mukhopadhyay, P. (1996): Mathematical Statistics, New Central Book Agency (P) Ltd.
  • Draper, N.R. & Smith, H. (1998): Applied Regression Analysis, 3rd Ed. JohnWiley.
  • Ezekiel, M. (1963): Methods of Correlation and Regression Analysis, JohnWiley.
  • Kutner, M.H., Nachtsheim, C.J. & Neter, J. (2004): Applied Linear Regression Models, 4th Ed. With Student, CD. McGraw Hill.
  • Rohatgi, V.K. (2009): An Introduction to Probability Theory and Mathematical Statistics, Wiley Eastern.
  • C.R. Rao.(2001): Linear Models & Generalization
  • Kailath (2016): Linear Estimation

 

e-RESOURCES:

 

 

JOURNALS:

 

  • Sankhya The Indian Journal of Statistics, Indian Statistical Institute
  • Aligarh Journal of Statistics, Department of Statistics and Operations Research, Aligarh Muslim University
  • Afrika Statistika, Saint-Louis Senega University
  • International Journal of Statistics and Reliability Engineering, Indian Association for Reliability and Statistic
  • Journal of the Indian Society for Probability and Statistics, Indian Society for Probability and Statistics
  • Journal of the Indian Statistical Association, Indian Statistical Association
  • Statistica, Department of Statistical Sciences Paolo Fortunato, University of Bologna
  • Statistics and Applications, Society of Statistics, Computer and Applications
  • Stochastic Modeling and Applications, MUK Publications and Distributions

 

Academic Year: 
2022-2023
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