Regression and Residual Plot (linearfit)
The linearfit
function uses linear regression to predict a straight line through a bivariate scatter plot. The linearfit
function takes two parameters:

The numeric field containing the independent (x) variable.

The numeric field containing the dependent (y) variable.
Sample syntax
select linearfit(petal_length_d, petal_width_d) as prediction,
residual,
petal_length_d,
petal_width_d
from iris
limit 150
Result set
The result set contains a random sample of records that match the WHERE
clause. If no WHERE
clause is included, the random sample will be taken from the entire result set. The size of the random sample can be controlled by the LIMIT
clause. The default sample size, if no limit is applied, is 25,000.
The linearfit
function returns the predicted value for each record. There are three additional fields that can be selected when the linearfit
function is used:

residual
: The residual value for each sample. The residual value is the sample’s dependent (y) value minus the predicted value. The residual represents the error of the regression prediction for each sample. 
The independent variable for each sample.

The dependent variable for each sample.
Visualizations
There are a number of visualizations that can flow from the regression result set.
The first visualization shown is a scatter plot with petal_length_d
on the xaxis and petal_width_d
on the yaxis. This can be used to visualize the relationship between the two variables in the regression analysis.
The second visualization shows the petal_length_d
variable on the xaxis and the prediction for petal_width_d
on yaxis.
The last visualization plots the predictions on the xaxis and the residual on the yaxis. This residual plot can be used to visualize the error of the regression model across the full range of predictions.