To develop next an equation to express the relationship between two variables and estimate the value of the dependent variable Y based on a selected value of the independent variable X.
The technique used to develop the equation for the straight line and make these predictions is called Regression analysis.
Regression and Correlation analyses show us how to determine both the nature and strength of a relationship between two variables.
We often find a casual relationship between variables; that is, the independent variable “ cause” the dependent variable to change.
It is important that you consider the relationship found by regression to be relationships of association but not necessarily of cause and effect.
Unless you have specified reasons for believing that the values of the dependent variable are caused by the values of the independent variable(s), don’t infer causality from the relationship you find by regression.
R2 is the co-efficient of multiple determination, is the percent of the variation explained by the regression. It is the sum of squares due to regression divided by the sum of squares total
The technique used to develop the equation for the straight line and make these predictions is called Regression analysis.
Regression and Correlation analyses show us how to determine both the nature and strength of a relationship between two variables.
We often find a casual relationship between variables; that is, the independent variable “ cause” the dependent variable to change.
It is important that you consider the relationship found by regression to be relationships of association but not necessarily of cause and effect.
Unless you have specified reasons for believing that the values of the dependent variable are caused by the values of the independent variable(s), don’t infer causality from the relationship you find by regression.
· An equation that defines the relationship between two variables.
· General Form of Regression Equation
· Y’ = a + bX
· Y’ - read Y prime, is the predicted value of the Y variable for selected value of the X value.
· b- is the intercept. It is the estimated value of Y When X = 0. Another way to put it is : a estimated value of Y where the regression line crosses the Y-axis when X is zero.
X is any value of the independent variable that is selected
X - is a value of the independent variable
Y - is a value of the dependent variable
n - is the number of items in the sample
In the regression analysis we estimate one variable based on another variable
The Variable being estimated is the dependent variable
The variable used to make the estimate is the independent variable.
R2 is the co-efficient of multiple determination, is the percent of the variation explained by the regression. It is the sum of squares due to regression divided by the sum of squares total
