Multiple Linear Regression Calculator


Instructions: You can use this Multiple Linear Regression Calculator to estimate a linear model by providing the sample values for several predictors \((X_i)\) and one dependent variable \((Y)\), by using the form below:

Y values (comma or space separated) =
X values (comma or space separated, press '\' for a new variable)
Name of the dependent variable (Optional)
Name of the independent variables (Comma separated. Optional)


Multiple Linear Regression Calculator

More about this Multiple Linear Regression Calculator so you can have a deeper perspective of the results that will be provided by this calculator. Multiple Linear Regression is very similar to Simple Linear Regression, only that two or more predictors \(X_1\), \(X_2\), ..., \(X_n\) are used to predict a dependent variable \(Y\). The multiple linear regression model is

\[ Y = \displaystyle \beta_0 + \beta_1 X_1 + \beta_2 X_2 + ... + \beta_n X_n + \epsilon\]

where \(\epsilon\) is the error term that has the property of being normally distributed with mean 0 and constant variance \(\epsilon ~ N(0, \sigma^2)\). After providing sample values for the predictors \(X_1\), \(X_2\), ..., \(X_n\) and the response variable \(Y\), estimates of the population slope coefficients are obtained by minimizing the total sum of squared errors. The estimated model is expressed as:

The expression that is used to compute the odds for the occurrence of an event, \(p\), given its probability is shown below:

\[ \hat Y = \displaystyle \hat\beta_0 + \hat\beta_1 X_1 + \hat\beta_2 X_2 + ... + \hat\beta_n X_n\]

If, on the other hand, you want to use only one predictors, you can use this simple linear regression calculator instead.




In case you have any suggestion, or if you would like to report a broken solver/calculator, please do not hesitate to contact us.

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