Calculating the regression

Regression analysis is most applied technique of statistical analysis and modeling.

If two variables are involved, the variable that is basis of estimation is called independent variable and variable whose value is estimated is called dependent variable. Dependent variable is known as response while independent variable is known as explanatory variable.

In regression it is convenient to define X as the explanatory variable (or independent) variable and Y as the outcome (or dependent) variable. We are concerned with determining how well X can predict Y. It is important to know which variable is the outcome (Y) and which is the explanatory (X). This may sound obvious but in education research it is not always clear for example does greater interest in reading predict better reading skills? But it may be that having better reading skills encourages greater interest in reading. Education research is littered with such 'chicken and egg' arguments. Make sure that you know what your hypothesis about the relationship is when you perform a regression analysis as it is fundamental to your interpretation.

As with correlation, regression is used to analyze the relation between two continuous (scale) variables. However, regression is better suited for studying functional dependencies between factors. The term functional dependency implies that X [partially] determines the level of Y. For example, there is a function dependency between age and blood pressure since as one ages, blood pressure increases. In contrast, there is no functional dependency between arm length and leg length since increasing the length of an arm will have no effect on leg length (or vice versa)