Regression Predicted Values Calculator

Instructions: Use this Regression Predicted Values Calculator to find the predicted values by a linear regression analysis based on the sample data provided by you. Please input the data for the independent variable \((X)\) and the dependent variable (\(Y\)), in the form below:

Independent variable \(X\) sample data (comma or space separated) =
Dependent variable \(Y\) sample data (comma or space separated) =
Independent variable Name (optional) =
Dependent variable Name (optional) =

Regression Predicted Values Calculator

One of the main objectives of regression is to obtain predictions. This is, linear regression models are predictive by nature. One of the goals when conducting a regression analysis is to find the corresponding predicted values, mathematically written as (\(\hat y\)).

Once we have estimate the regression coefficients corresponding to the y-intercept and slope, \(\hat \beta_0\) and \(\hat \beta_1\), we can proceed with the calculation of predicted values.

How do you compute regression predicted values?

The calculation is simple, but need to compute the regression coefficients first. Once you have the slope and y-intercept, you compute the regression predicted values using the following formula:

\[ \hat y = \hat \beta_0 + \hat \beta_1 x \]

What else can you do with the predicted values?

The predicted values are fairly useful. First, you can compute residuals, which are extremely useful to assess the various linear regression model assumptions.

Also, you can use predicted values to make a scatterplot of observed versus predicted values, which is one of the residual plots you will look into in order to assess the model assumptions. In fact, this calculator will also provide this plot of observed versus predicted values.

Other regression-related calculators

If you are dealing with more than one predictor, you will likely need this multiple linear regression calculator , which is more appropriate in that case.

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