Regression analysis
Summary
This operator performs a multiple linear regression analysis.
Method
Regression analysis is a statistical process for estimating the relationships among variables. Specifically, it is estimated, how the value of a criterion variable (dependent variable) changes when a predictor (independent variable) is varied. The estimation target is a function of the independent variables called the regression function. For more information see for example Wikipedia Regression Analysis.
Source: https://en.wikipedia.org/wiki/Regression_analysis#/media/File:Linear_regression.svg
The operation "Regression Analysis" produces estimates for the coefficients of the independent variables, and an evaluation of the regression in form of a string. Additionally, it is possible to display different statistical measures regarding the regression analysis and plot the data.
Example: Does the employee count predict sales?
Situation  A company expects a linear relation between the number of employees and sales. Therefore, they measure the number of employees and the sales figures in different regions. This assumption shall be examined by calculating a linear regression analysis. 

Settings  In this example, we chose the following settings: 
Result 

ProjectFile 
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Settings
This operator performs a multiple linear regression analysis.
Columns of input table
Parameter
Examples
Example 2: Multivariate linear regression
Situation  The company from example 1 provides a training for their employees, and assumes that it has a positive effect on the resulting sales. Therefore, the number of employees, their training status (yes/no), and sales figures are measured in different regions. We now want to calculate a regression model which includes only significant predictors of the sales figures. Furthermore, we want to estimate the average sales in case the significant factors are increased by one. 

Settings  In this example, we chose the following settings: 
Result 

Troubleshooting
Problem  Frequent Causes  Solutions 

Error message or "n. def."  1. There are too few values to estimate this figure.
 Create larger groups, or categories (= less differentiation by identifier categories). 
 2. An independent variable shows only one value and does not vary. No calculation is possible.
 Do not use this independent variable, since it does not vary (requirement for regression analysis). 
 3. Two or more variables are linearly dependent. E.g.,
Using A,B, and TOTAL as independent variables does not allow to distinguish between the effects of each single variable.  Do not use any of these variables (only independent variables). 
Error message  If the option "Select all numeric columns is set", the semantics of each column needs to be set to "Number"  Use the operator Format columns and change the semantics. 