LECTURE NO FIVE : STATISTICS LECTURES 05

Statistics lecture "Regression"

1. Lecture No 05 Course Title : Statistics Course Code : MIC 2251

2. Regression What is Regression Analysis? Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. Linear regression is the most commonly used type of regression because it is easier to analyze as compared to the rest . Linear regression is used to find the line that is the best fit to establish a relationship between variables.

3. Regression Regression analysis includes several variations, such as linear, multiple linear, and nonlinear. The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship.

4. Regression Analysis – Linear Model Assumptions Linear regression analysis is based on six fundamental assumptions : The dependent and independent variables show a linear relationship between the slope and the intercept. The independent variable is not random. The value of the residual (error) is zero. The value of the residual (error) is constant across all observations. The value of the residual (error) is not correlated across all observations. The residual (error) values follow the normal distribution.

5. Linear Regression analysis The goal of linear regression is to find the best-fitted line through the data points. For two variables, x, and y, the regression analysis can be visualized as follows: