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Before undertaking regression analysis, one must decide on which variables will be analysed. Regression analysis is predicting a variable from a number of other variables.
Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.
Regression :The average Linear or Non linear relationship between Variables.
Explanatory (or independent) variables are variables such that changes in their value are thought to cause changes in the "dependent" variables.
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
It measures associations between variables.
There are two regression lines if there are two variables - one line for the regression of the first variable on the second and another line for the regression of the second variable on the first. If there are n variables you can have n*(n-1) regression lines. With the least squares method, the first of two line focuses on the vertical distance between the points and the regression line whereas the second focuses on the horizontal distances.
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
Yes they can.
Yes.
Regression analysis is a statistical technique to measure the degree of linear agreement in variations between two or more variables.
Regression analysis describes the relationship between two or more variables. The measure of the explanatory power of the regression model is R2 (i.e. coefficient of determination).