Queens University at Kingston



Table of Contents

  1. Introduction to Multiple Regression
  2. Definition of the Multiple Regression Model
  3. Derivation of OLS Estimates for the Multiple Regression Model
  4. Examples of Computing and Interpreting OLS Estimates of the MLRM
  5. Random Variables and Matrix Notation
  6. Statistical Properties of OLS Estimates
  7. Formulating and Testing Multiple Hypotheses
  8. Prediction and Analysis of Variance in the MLRM
  9. Violation of MLRM Assumptions

Hot Link Index of Names Defined in multiple

  1. MLRM |-->The multiple linear regression model (MLRM) is a generalization of the simple LRM:
  2. mAa |-->Assumption A0 about the LRM
  3. mAb |-->Assumption A1: The Population Regression Equation (PRE)
  4. premat |-->The PRE written in Matrix Notation
  5. mols |-->Definition of variables used in OLS estimation
  6. ranmat |-->Random matrix
  7. expmat |-->Expectation of a random matrix V (m x r).
  8. varmat |-->Variance of a random vector
  9. vcmat |-->Variance of a linear transformation
  10. assmatb |-->Assumptions of the LRM in Matrix Notation
  11. mAc |-->Assumption A2
  12. mAd |-->Assumption A3
  13. mAe |-->Assumption A4
  14. mAfAg |-->Assumption A5-A6
  15. mAh |-->Assumption A7

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