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Basics of Regression Analysis

Regression

A measure of relation between the mean value of one variable (e.g. output) and corresponding values of other values (e.g. time and cost)

Uses

•         Association (fundamental of regression). If there is association between variables then we go for    regression analysis
•         Prediction (predict one variable by knowing other)
•         Estimation (parameter estimation)

 Variables involve liner regression

·         Y axis dependent variables

·         X axis independent variables (non random variables, using X to predict Y variable, X is in     researcher hand)

Example:         

•     Y is regressed on X (Classical regression model)
•     Blood pressure is regressed on age
•     Dependent is regressed on independent

·         Assumptions (LINE)

•         Linearity (Straight line, Regression line)
•          Independence (For X1 the value of Y1 is not dependent on Y2)
  
•         For any given value X a set of Y value is normally distributed
•         Equal variances