Free statistics calculators designed for data scientists. This Least Squares Regression Calculator:
Can be comma separated or one line per data point; you can also cut and paste from Excel.
Saved in your browser; you can retrieve these and use them elsewhere on this site.
Need to pass an answer to a friend? It's easy to link and share the results of this tool. Hit calculate - then simply cut and paste the url after hitting calculate - it will retain the values you enter so you can share them via email or social media.
This is a online regression calculator for statistical use. Enter your data as a string of number pairs, separated by commas. Enter each data point as a separate line. Then hit calculate. The linear regression calculator will estimate the slope and intercept of a trendline that is the best fit with your data.
This page includes a regression equation calculator, which will generate the parameters of the line for your analysis. It can serve as a slope of regression line calculator, measuring the relationship between the two factors. This tool can also serve as a sum of squared residuals calculator to give you a perspective on fit & accuracy.
You can save your data for use with this webpage and the similar tools on this site. Just hit the "save data" button. It will save the data in your browser (not on our server, it remains private). It will appear on the list of saved datasets below the data entry panel. To retrieve it, all you need to do is click the "load data" button next to it.
This linear regression calculator fits a trend-line to your data using the least squares technique. This approach optimizes the fit of the trend-line to your data, seeking to avoid large gaps between the predicted value of the dependent variable and the actual value. The Least Squares Regression Calculator will return the slope of the line and the y-intercept. It will also generate an R-squared statistic, which evaluates how closely variation in the independent variable matches variation in the dependent variable (the outcome). For a deeper view of the mathematics behind the approach, here's a regression tutorial.
To help you visualize the trend - we display a plot of the data and the trend-line we fit through it. If you hover or tap on the chart (in most browsers), you can get a predicted Y value for that specific value of X.The equation of the line is of particular interest since you can use it to predict points outside your original data set. Similarly, the r-squared gives you an estimate of the error associated with effort: how far the points are from the calculated least squares regression line.
Some practical comments on real world analysis:
The underlying calculations and output are consistent with most statistics packages. It applies the method of least squares to fit a line through your data points. The equation of the regression line is calculated, including the slope of the regression line and the intercept. We also include the r-square statistic as a measure of goodness of fit. This equation can be used as a trendline for forecasting (and is plotted on the graph).
Want to know more? This page has some handy linear regression resources.
