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STAT 3503

Regression Analysis

0.5 credit · Undergraduate

Review of simple and multiple regression with matrices, Gauss-Markov theorem, polynomial regression, indicator variables, residual analysis, weighted least squares, variable selection techniques, nonlinear regression, correlation analysis and autocorrelation. Computer packages are used for statistical analyses.

2022

Topics

  • Sec 1.1-1.8 Simple Linear Regression .
  • Sec 2.1-2.11 Inferences in Regression and Correlation Analysis.
  • Sec 3.1-3.9 Diagnostics and Remedial Measures
  • Sec 4.1-4.7, 5.1-5.13 Simultaneous Inferences and Matrix Approach to SLR.
  • Sec 6.1-6.9, 7.1-7.3 Multiple Regression.
  • Sec 7.4-7.6, Review Multiple Regression.
  • Sec 8.1-8.2 Regression Models for Quantitative and Qualitative Predictors.
  • Sec 8.3-8.7 Regression Models for Quantitative and Qualitative Predictors.
  • Sec 9.1-9.6, 10.1-10.5 Model Selection and Validation, Diagnostics.
  • Sec 11.1-11.5, 12.1-12.5 Remedial Measure , Autocorrelation in Time Series Data.
  • Sec 14.1-14.10 Logistic Regression, Poisson Regression and Generalized Linear
  • 11-14.12 Review Logistic Regression, Poisson Regression and Generalized Linear

Readings

  • Applied Linear Statistical models , (5th Ed., McGraw Hill 2005) by Kutner, Nachtsheim, Neter, and Li. [ Student Solution Manual & Exercise Data Sets].

Prerequisites & course information

Precludes additional credit for STAT 3553 . Prerequisite(s): i) DATA 1519 or STAT 2509 or STAT 2602 or STAT 2607 or ECON 2202 or ECON 2220 or equivalent; and ii) MATH 1152 or MATH 1107 or MATH 1119 or equivalent; or permission of the School. Lectures three hours a week and one hour laboratory.

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