630-936-4771 / Steve@StatisticallySignificantConsulting.com

Linear regression is a common Statistical
Data Analysis
technique. It is used to determine the extent to which there
is a linear relationship between a dependent variable and one or more
independent variables. There are two types of linear regression, simple linear
regression and multiple linear regression.

In **simple linear regression** a single independent variable is used to predict the value of a dependent variable. In
**multiple linear regression** two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. In both cases there is only a single dependent variable.

The dependent variable must be measured on a continuous measurement scale (e.g. 0-100 test score) and the independent variable(s) can be measured on either a categorical (e.g. male versus female) or continuous measurement scale. There are several other assumptions that the data must satisfy in order to qualify for linear regression.

Simple linear regression is similar to correlation in that the purpose is to measure to what extent there is a linear relationship between two variables. The major difference between the two is that correlation makes no distinction between independent and dependent variables while linear regression does. In particular, the purpose of linear regression is to "predict" the value of the dependent variable based upon the values of one or more independent variables.

When you hire me to do the statistical analysis for your dissertation, I guarantee that I will use the appropriate statistical tests for your dissertation results chapter. I can perform virtually any standard statistical analysis (using SPSS) and I provide ongoing statistical help to ensure that you fully understand all of the statistics that I used for your study

Simply contact me by phone or email to get started.

Steve Creech

630-936-4771 | Steve@StatisticallySignificantConsulting.com