Regression analysis(also called the Least Squares Method) is a statistical method for predicting the future and other outcomes. If you have some data from the past, that data can be used to make an estimate of what will happen in the future. This also works when the data is not related to time. Remember, you don't need a crystal ball to make predictions, just some data.

Regression analysis is a statistical technique for estimating the relationship between 2 or more variables. In other terms, it is a way of predicting one or more variables in terms of other variables. This is accomplished by obtaining as much data as possible. Next the data is used in a calculation that minimizes the absolute error of the dependent variable using the least squares method. For more information go to Learn Regression Analysis as well as Interpreting Regression Results.

Simple Regression Analysis - This elementary technique is used to calculate a straight line relationship between 2 variables from collected data. This method minimizes the absolute vertical distance between the y data values and the y values on the calculated line. (Coming soon)

Orthogonal Regression - This is a differerent approach that minimizes the orthogonal or perpendicular distance between the y data values and the y values on the calculated line. (Coming soon)

Deming Regression - This variation of the method takes into account the variability of both the independent and dependent variables. (Coming soon)

Regression Trendlines - How can you tell if a set of data is in a trend? One way is to perform a linear regression analysis. From the calculated results you can see what the slope is doing. (Coming soon)

Multiple Linear Regression - When you have multiple variables that determine an outcome, then this method can be performed to define that relationship. (Coming soon)

Regression Analysis is a powerful technique for finding the relationship between variables. When this relationship is defined, then, for a given set of independent variables, a dependent variable value can be predicted.

The links below are specific questions and answers about statistics and how to use them.