Polynomial Regression Calculator


Instructions: You can use this Multiple Linear Regression Calculator to estimate a linear model by providing the sample values for one predictor \((X)\), and its powers up to a certain order, and one dependent variable \((Y)\), by using the form below:

Y values (comma or space separated) =
X values (comma or space separated) =
Order of Polynomial (Integer. Less than 10)
Name of the dependent variable (Optional)
Name of the independent variable (Optional)

Polynomial Regression Calculator

More about this Polynomial Regression Calculator so you can have a deeper perspective of the results that will be provided by this calculator. Polynomial Regression is very similar to Simple Linear Regression, only that now one predictor and a certain number of its powers are used to predict a dependent variable \(Y\). The polynomial linear regression model is

\[ Y = \displaystyle \beta_0 + \beta_1 X + \beta_2 X^2 + ... + \beta_n X^n + \epsilon\]

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 predictor \(X\) 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:

\[ \hat Y = \displaystyle \hat\beta_0 + \hat\beta_1 X + \hat\beta_2 X^2 + ... + \hat\beta_n X^n\]

If, on the other hand, you want to use only one predictor, without power, you can use this simple linear regression calculator instead. Or if you have multiple predictors, you need to use this multiple linear regression calculator.




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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