Linear Regression Calculator

If you want to calculate a linear regression online just copy your data into the table above. Then select a metric dependent variable and one or more independent variables.

Depending on how many independent variables you click, either a simple linear regression or a multiple linear regression will be calculated. The results will be displayed as follows:

Of course you can also calculate a logistic regression with DATAtab. If you like, you can load the data set:

Regression analysis calculator

The Regression analysis calculator is a statistical tool used to model the relationship between a dependent variable and one or more independent variables.

• Dependent Variable (Y): The outcome or the variable you're trying to predict or explain.
• Independent Variables (X): The variables you think have an effect on the dependent variable.

The Simple Linear Regression models the relationship between the dependent variable and one independent variable using a linear equation. The Multiple Linear Regression involves two or more independent variables affecting the dependent variable.

Multiple regression calculator

Multiple regression is a statistical technique used to understand the relationship between one dependent variable and two or more independent variables. It's an extension of simple linear regression and provides a way to predict the outcome of a variable based on the values of several other variables.

Lasso Regression Calcualtor

You can also calcualte a Lasso Regression when you want to impose an L₁ penalty on your model’s coefficients to both regularize and perform automatic feature selection—forcing less important feature weights to shrink exactly to zero, which helps reduce multicollinearity, improve interpretability, and prevent overfitting, especially in high-dimensional datasets.

Ridge Regression Calcualtor

A Ridge Regression can be calculated when you want to impose an L₂ penalty on your model’s coefficients to uniformly shrink them toward zero—dampening the impact of less informative predictors without driving any weights exactly to zero.