linear regression equation prediction calculator

LINEAR REGRESSION EQUATION PREDICTION CALCULATOR FREE

It is an estimate of the variance of the error term in the model.

S: This is the estimated error variance, or the average squared difference between the predicted values and the actual values. A lower log-likelihood value indicates a better fit. Log-Likelihood: This is a measure of the overall fit of the model. A high F-statistic and a low probability value indicate that the model is a good fit for the data. F-statistic: This is a measure of the overall significance of the model. It is similar to R-squared, but may be more accurate when there are many independent variables. R-squared: This is the adjusted R-squared, which takes into account the number of independent variables in the model. It ranges from 0 to 1, with a higher value indicating a better fit. R-squared: This is a measure of how well the model fits the data. Method: The method used to fit the model, in this case OLS (ordinary least squares).

Variable: This refers to the dependent variable, or the variable that is being predicted by the model. Here is an explanation of each of the measures:ĭep. The results provided by the multiple linear regression calculator include a number of statistical measures that can help you understand and evaluate the model. Interpreting the Output of a Multiple Linear Regression Calculator

LINEAR REGRESSION EQUATION PREDICTION CALCULATOR FREE

And best of all, it's completely free to use! We hope you find it useful and encourage you to try it out for yourself.įor additional information on entering data, see the documentation Overall, our online calculator is a valuable tool for anyone interested in understanding and analyzing data using linear regression. These plots can help you identify patterns and trends in the data, and can be useful for identifying potential outliers or errors in the data. In addition to these results, the calculator also generates scatter plots that show the predicted values of the dependent variable against the residuals, or the difference between the predicted values and the actual values. These measures can help you evaluate the strength of the relationship between the variables and the reliability of the model. In addition, the calculator also includes multiple tests, such as the Durbin-Watson, Jarque-Bera, and Breusch-Pagan tests, to evaluate the assumptions of the model. Once you have entered your data, the calculator will provide you with a number of results, including various statistical measures such as the R-squared value and F-statistic. To use the calculator, you can either copy and paste your data from Excel or input it directly into the text fields, separating each variable with a comma, space, or line break. Our online calculator allows you to perform data analysis using simple linear regression, which involves only one independent variable, or multiple linear regression, which involves more than one independent variable. Y = α + β x, defines a random variable drawn from the empirical distribution of the x values in our sample.Easily Analyze Data with Our Free Online Linear Regression Calculator The intercept of the fitted line is such that the line passes through the center of mass ( x, y) of the data points. In this case, the slope of the fitted line is equal to the correlation between y and x corrected by the ratio of standard deviations of these variables. It is common to make the additional stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is measured by its squared residual (vertical distance between the point of the data set and the fitted line), and the goal is to make the sum of these squared deviations as small as possible. The adjective simple refers to the fact that the outcome variable is related to a single predictor. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variable. In statistics, simple linear regression ( SLR) is a linear regression model with a single explanatory variable. Here the dependent variable (GDP growth) is presumed to be in a linear relationship with the changes in the unemployment rate. Okun's law in macroeconomics is an example of the simple linear regression. It has been suggested that Variance of the mean and predicted responses be merged into this article.

Linear regression equation prediction calculator