This example uses the LinRegInternal and LinReg functions to find the coefficients of the variables that determine the selling price of a home in a given neighborhood. The response variable, SellingPrice (the selling price of the house in dollars) is modeled on these independent (predictor) variables:
- House size (in square feet)
- Lot size (in square feet)
- Number of bedrooms
- Whether the kitchen counter is granite (0 or 1)
- Whether the bathrooms are upgraded (0 or 1)
Input
housesize | lotsize | bedrooms | granite | upgradedbathroom | sellingprice |
---|---|---|---|---|---|
2397 | 14156 | 4 | 1 | 0 | 189900 |
2200 | 9600 | 4 | 0 | 1 | 195000 |
4032 | 10150 | 5 | 0 | 1 | 197900 |
3529 | 9191 | 6 | 0 | 0 | 205000 |
3247 | 10061 | 5 | 1 | 1 | 224900 |
2983 | 9365 | 5 | 0 | 1 | 230000 |
3536 | 19994 | 6 | 1 | 1 | 325000 |
3198 | 9669 | 5 | 1 | 1 |
The LinRegInternal function skips the last row of housing_data because it contains a NULL value.
SQL Call
SELECT * FROM LinReg ( ON LinRegInternal ( ON housing_data ) PARTITION BY 1 ) AS dt;
Output
coefficient_name value_col ---------------- ------------------- Intercept -21739.296665036818 housesize -26.930783509145726 lotsize 6.33452410459345 bedrooms 44293.76058418322 granite 7140.676293495372 upgradedbathroom 43179.19988882635
Download a zip file of all examples and a SQL script file that creates their input tables from the attachment in the left sidebar.