I am currently trying to run a linear model on a large data set, but am running into issues with some specific variables.
pv_model <- lm(SalePrice ~ MSSubClass + LotConfig + GarageArea + LotFrontage, data = train)
summary(pv_model)
Here is code for my regression. SalePrice, MSSubClass, GarageArea, and LotFrontage are all numeric fields, while LotConfig is a factored variable.
Here is the output of my pv_model:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98154.64 17235.51 5.695 1.75e-08 ***
MSSubClass 50.05 58.38 0.857 0.391539
LotConfigCulDSac 69949.50 12740.62 5.490 5.42e-08 ***
LotConfigFR2 19998.34 14592.31 1.370 0.170932
LotConfigFR3 21390.99 34126.44 0.627 0.530962
LotConfigInside 21666.04 5597.33 3.871 0.000118 ***
GarageArea 175.67 10.96 16.035 < 2e-16 ***
LotFrontage101 42571.20 42664.89 0.998 0.318682
LotFrontage102 26051.49 35876.54 0.726 0.467968
LotFrontage103 36528.81 35967.56 1.016 0.310131
LotFrontage104 218129.42 58129.56 3.752 0.000188 ***
LotFrontage105 61737.12 27618.21 2.235 0.025673 *
LotFrontage106 40806.22 58159.42 0.702 0.483120
LotFrontage107 36744.69 29494.94 1.246 0.213211
LotFrontage108 71537.30 42565.91 1.681 0.093234 .
LotFrontage109 -29193.02 42528.98 -0.686 0.492647
LotFrontage110 73589.28 27706.92 2.656 0.008068 **
As you can see, the first variables operate correctly. Both the factored and numeric fields respond appropriately. That is, until it gets to LotFrontage. For whatever reason, the model runs the regression on every single level of LotFrontage.
For reference, LotFrontage describes the square footage of the subject's front yard. I have properly cleaned the data and replaced NA values. I really am at a loss for why this particular column is acting so unusually.
Any help is greatly appreciated.
If I download the data from the kaggle link or use a github link and do:
Suggest that you read in the csv again like above.