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ISLAMIC MEDICAL EDUCATION RESOURCES-03

0508-REGRESSION ANALYSIS

By Professor Omar Hasan Kasule Sr.

Learning Objectives:

  • Concepts of dependent and independent variables in a scatter-gram
  • Difference between correlation and regression
  • The linear regression line and the linear regression equation (Y=a + bx)
  • Interpreting the intercept, a (+ve and -ve)
  • Interpreting the regression coefficient, b (+ve, -ve, range -infinity to +infinity)
  • Use of the regression line/equation for  testing of association
  • Use of regression for  prediction (interpolation and extrapolation)
  • Definition and use of multiple linear regression
  • Use of multivariate methods in controlling confounding and prediction

 

Key Words and Terms:

              Prediction, linear extrapolation

              Prediction, linear interpolation

               Regression Analysis

               Regression Coefficient, partial

               Regression coefficient, total

               Regression Equation/Line

               Regression, multiple linear

               Regression, multiple logistic

               Regression, non-linear

               Regression, simple linear

               Regression. Logistic

               Variable, dependent

               Variable, independent

 

UNIT OUTLINE

LINEAR REGRESSION

A. Overview or Regression

B. Simple Linear Regression

C. Multiple Linear Regression

D. Prediction in Regression

 

LOGISTIC REGRESSION

A. Definition

E. Multiple Logistic Regression

 

UNIT SYNOPSIS

LINEAR REGRESSION

Regression to the mean, first described by Francis Galton (1822-1911) is one of the basic laws of nature, sunan al llah fi al kawn. The simple linear regression equation is y=a + bx where y is the dependent/response variable, a is the intercept, b is the slope/regression coefficient, and x is the dependent/predictor variable.

 

Multiple linear regression, a form of multivariate analysis, is defined by y=a+b1x1 + b2x2 + …bnxn.

 

Linear regression is used for prediction (intrapolation and extrapolation) and for analysis of variance.

 

LOGISTIC REGRESSION

Logistic regression is non-linear regression with y dichotomous/binary Logistic regression is used in epidemiology because of a dichotomized outcome variable. Multiple logistic regression is used for prediction and other purposes.

Professor Omar Hasan Kasule Sr. August 2005