Regression is a very important statistical tool for predicting the value of one variable when the value of another variable is known. It can only be applied when the variable with the unknown value is dependent or is correlated with the known value variable. Thus it is used to estimate the value of a dependent variable when the value of an independent variable is known. In our lives we can find many examples of dependent and independent variable like demand and price, performance of staff and number of people who attended training, etc. Regression can be single as well as multiple, in the latter case there are more than one independent variables. Regression analysis can become very tardy and complicated if done manually, nowadays most people use SPSS (Statistical Package for Social Sciences) to run most of their statistical tests. The software runs like most other software and is user friendly, to access simple regression just click on Analyze menu from the top of the window, select regression from drop down menu and then select linear from pop up menu, when the regression dialogue box appears follow the indicators and arrows to enter the dependent, independent variables and the method used, if more than one independent variable is entered then it will be multiple regression. One very important caution before applying regression technique in SPSS is checking for linearity in the relationship, nonlinear relationships will not be predictable using SPSS, also it needs to be checked that there is no correlation between the independent variables. For applying regression in SPSS dependent variable data needs to be in a continuous scale, and intra group variation should be low, that is, each data item should not vary significantly from the mean of the entire data set. These checks can also be performed using SPSS.