“Significant” in terms of multiple linear regression means that, with the rest of the other attributes being considered, I have 95% certainty that the adjustment for this attribute is not zero. There are many reasons why attributes may be insignificant in our models. One reason may be that we do not have enough variation in the values to come up with an estimate. For example, basements are really rare in Texas. If there are only a few basements, then the model may not be able to come up with a suggested adjustment for basements since there is little variation. Another reason an attribute may not be significant is because it is highly correlated with another attribute in the model.

### Articles in this section

- FAQ Regression - What does R squared signify?
- FAQ Regression - How can bedrooms be considered an insignificant attribute?
- FAQ Regression - What is collinearity? Why is it important?
- FAQ Regression - Do appraisers have to use the regression results?
- FAQ Regression - Can I make an adjustment even when an attribute is not considered significant?
- FAQ Regression - What’s the value of analyzing 2000 properties? Don't we just need three?
- FAQ Regression - What does significant mean?
- FAQ Regression - When should I make adjustments to attributes other than the ones HouseCanary analyzed?
- FAQ Regression - What do the sliders mean?

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