Upload data from a comma or tab separated file.

Upload data from a .xls or .xlsx file.

Upload data from a .json file.

Upload data from a .dta file.

Upload data from a .sav file.

Upload data from a .sas7bdat file.

Upload data from a RDS file.

Select a data set from the drop down box and click on submit.

Rename variables and modify data types.

Click on Yes to select variables.

Click on Yes to filter data.

Screen data for missing values, verify variable names and data types.

Click on Yes to create a random sample of data.

Click on Yes to partition data into training and test set.

Ordinary least squares regression.

Test for constant variance. It assumes that the error terms are normally distributed.

Test if k samples have equal variances.

Test for heteroskedasticity under the assumption that the errors are independent and identically distributed (i.i.d.).

Test for heteroskedasticity under the assumption that the errors are independent and identically distributed (i.i.d.).

Variance inflation factor, tolerance, eigenvalues and condition indices.

Added variable plot provides information about the marginal importance of a predictor variable, given the other predictor variables already in the model. It shows the marginal importance of the variable in reducing the residual variability.

The residual plus component plot indicates whether any non-linearity is present in the relationship between response and predictor variables and can suggest possible transformations for linearizing the data.

Graph to determine whether we should add a new predictor to the model already containing other predictors. The residuals from the model is regressed on the new predictor and if the plot shows non random pattern, you should consider adding the new predictor to the model.