This operator performs a multiple linear regression analysis.


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.

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?


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.


In this example, we chose the following settings:


  • The results of the regression analysis are shown in the table below.
  • The evaluation '* * ***' in column H shows that the absolute value of the coefficient "Employees" is greater or equal 1, and that the variable "Employees" has a significant effect on "Sales".
  • Therefore, also R², Adj. R², and the p-value of ANOVA fall into the ranges specified by us in the operation.
  • Based on these results, we assume a linear relationship between the number of employees and sales figures.
  • The coefficient "c Employees" in column C means that it is estimated that 1.637 additional units of sales are to be expected per additional employee.


Confluence Op Regression.gzip

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This operator performs a multiple linear regression analysis.

Columns of input table



Example 2: Multivariate linear regression



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.


In this example, we chose the following settings:


  • The table below shows the results of the regression analysis.
  • For the variable "Training", p-value was > 0.1 (our limit chosen in the settings), and it was therefore excluded from the regression model automatically.
  • Based on the results, the sales figures are predicted to increase from 101.7 (Sales Median in column J) to 104 (Sales estimated in column K) if an additional employee is hired.



Frequent Causes


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.,

  • TOTAL = A + B
  • A = 3*B - TOTAL

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.

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