Linear Regression

Linear regression:

Linear regression attempts to model the Relationship Between two variables by fitting a linear equation to Observed date. One variable is considerably to be an explanatory variable, and The Other is considerably to be the dependent variable. For Example, the Modeler Might want to report the Weights of Individuals to their Heights Using a linear regression model.
Before attempting to fit a linear model to Observed data, the Modeler should first determine whether or not there is the Relationship Between the variables of interest. This does not necessarily imply one variable That Causes The Other (Example for High SAT Scores not cause the highs college grids), But That there is add Significant Association Between the two variables. The Scatter plot dog be a helpful tool in determining the Strength of the Relationship Between two variables. If there Appear to be in association MOTION Between the explanatory and dependent variables ( ie , the Scatter plot does not indicate any increasing or decreasing trends), then fitting a linear regression model to the data Probably will not provide a useful model. Valuable The Numerical Measure of Association Between two variables is the coefficient correlations, Which is the value Between - 1 and 1 indicating the Strength of the association of the Observed data for the two variables.
The linear regression line you have an equation of the form Y = a + bx  Where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the Intercept (the value of y When x = 0).