Regression analysis is a statistical technique that used for studying linear relationships. The process begins with general form for relationship called as a regression model. Y is the dependent variable to represent the quantity and X is the explanatory variables.

## Why Regression Analysis

A regression analysis is made for 2 purposes. The first one is to predict the value of dependent variables. The second one is to estimate the effect of explanatory variable on the dependent variable.

## Steps On How to Interpret Regression Analysis Results

- Look at the prediction equation to know the estimation of the relationship
- Refer to standard error of prediction in making predictions for individuals. Refer also to standard error for estimated mean for estimating average value of dependent variable
- Refer to standard errors of coefficients to know how much you can trust all the estimations of the effects of the explanatory variables
- Look at significance levels of t-ratios in order to know how strong the evidence in supporting each explanatory variables
- Used adjective coefficient of determination in measuring potential explanatory power of the model
- Compare beta weights of explanatory variables to rank them to know explanatory importance.

## Tips on How to Interpret Regression

Interpreting regression seems to hard for some individuals because they need to check for the X and Y. before you decide to interpret, be sure to look for the significance level of your ration. This helps you to know how strong your variables. You also need to compare and used adjective coefficient.

Interpreting seems not to be easy but when you have the results, you should focus on it. You need to estimate the effect of your explanatory variable on your dependent variable. It is also necessary to determine if there is evidence if your explanatory variable belongs to the regression model.