Test jonas

content/topics/Analyze/Regression/Reporting/model-assumptions.md

@@ -11,7 +11,7 @@ aliases:
 
 ## Overview
 
-The first step before estimating an OLS regression is to validate the model assumptions to ensure the estimates are reliable and the inference drawn is valid.
+The first step before estimating an OLS regression is to validate the model assumptions to ensure the estimates are reliable and the inference drawn is valid. 
 
 To illustrate how to check for model assumptions with an example, we use the built-in `cars` dataset which includes 50 data points of a car’s stop distance at a given speed. Since the dimensions are in miles per hour and foot, we first convert it into kilometer per hour and meter, respectively.
 
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-- **Scale-Location**: Finally, the scale-location plot shows the standardized residuals for all fitted values. The homoskedasticity assumption states that the error term should be the same across all values of the independent variables. Hence, we should check here if there is any pattern that stands out. In our case, this happens to be the case as the blue line stays rather flat. It would, however, be troublesome if the error value increases for higher speed values(i.e. if there is a “fanning out” pattern).
+- **Scale-Location**: Finally, the scale-location plot shows the standardized residuals for all fitted values. The homoskedasticity assumption states that the error term should be the same across all values of the independent variables. Hence, we should check here if there is any pattern that stands out. In our case, this happens to be the case as the blue line stays rather flat. It would, however, be troublesome if the error value increases for higher speed values(i.e. if there is a “fanning out” pattern).