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One of the key assumptions of linear regression is that the residuals are distributed with equal variance at each level of the predictor variable. This assumption is known as **homoscedasticity**.

When this assumption is violated, we say that heteroscedasticity is present in the residuals. When this occurs, the results of the regression become unreliable.

One way to handle this issue is to instead use **weighted least squares regression**, which places weights on the observations such that those with small error variance are given more weight since they contain more information compared to observations with larger error variance.

This tutorial provides a step-by-step example of how to perform weight least squares regression in R.

**Step 1: Create the Data**

The following code creates a data frame that contains the number of hours studied and the corresponding exam score for 16 students:

df

**Step 2: Perform Linear Regression**

Next, we’ll use the **lm()** function to fit a simple linear regression model that uses hours as the predictor variable and score as the response variable:

#fit simple linear regression model model #view summary of model summary(model) Call: lm(formula = score ~ hours, data = df) Residuals: Min 1Q Median 3Q Max -17.967 -5.970 -0.719 7.531 15.032 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 60.467 5.128 11.791 1.17e-08 *** hours 5.500 1.127 4.879 0.000244 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 9.224 on 14 degrees of freedom Multiple R-squared: 0.6296, Adjusted R-squared: 0.6032 F-statistic: 23.8 on 1 and 14 DF, p-value: 0.0002438

**Step 3: Test for Heteroscedasticity**

Next, we’ll create a residual vs. fitted values plot to visually check for heteroscedasticity:

#create residual vs. fitted plot plot(fitted(model), resid(model), xlab='Fitted Values', ylab='Residuals') #add a horizontal line at 0 abline(0,0)

We can see from the plot that the residuals exhibit a “cone” shape – they’re not distributed with equal variance throughout the plot.

To formally test for heteroscedasticity, we can perform a Breusch-Pagan test:

#load lmtest package library(lmtest) #perform Breusch-Pagan test bptest(model) studentized Breusch-Pagan test data: model BP = 3.9597, df = 1, p-value = 0.0466

The Breusch-Pagan test uses the following null and alternative hypotheses:

**Null Hypothesis (H**Homoscedasticity is present (the residuals are distributed with equal variance)_{0}):**Alternative Hypothesis (H**Heteroscedasticity is present (the residuals are not distributed with equal variance)_{A}):

Since the p-value from the test is **0.0466** we will reject the null hypothesis and conclude that heteroscedasticity is a problem in this model.

**Step 4: Perform Weighted Least Squares Regression**

Since heteroscedasticity is present, we will perform weighted least squares by defining the weights in such a way that the observations with lower variance are given more weight:

#define weights to use wt abs(model$residuals) ~ model$fitted.values)$fitted.values^2 #perform weighted least squares regression wls_model #view summary of model summary(wls_model) Call: lm(formula = score ~ hours, data = df, weights = wt) Weighted Residuals: Min 1Q Median 3Q Max -2.0167 -0.9263 -0.2589 0.9873 1.6977 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 63.9689 5.1587 12.400 6.13e-09 *** hours 4.7091 0.8709 5.407 9.24e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.199 on 14 degrees of freedom Multiple R-squared: 0.6762, Adjusted R-squared: 0.6531 F-statistic: 29.24 on 1 and 14 DF, p-value: 9.236e-05

From the output we can see that the coefficient estimate for the predictor variable *hours* changed a bit and the overall fit of the model improved.

The weighted least squares model has a residual standard error of **1.199** compared to **9.224** in the original simple linear regression model.

This indicates that the predicted values produced by the weighted least squares model are much closer to the actual observations compared to the predicted values produced by the simple linear regression model.

The weighted least squares model also has an R-squared of **.6762** compared to **.6296** in the original simple linear regression model.

This indicates that the weighted least squares model is able to explain more of the variance in exam scores compared to the simple linear regression model.

These metrics indicate that the weighted least squares model offers a better fit to the data compared to the simple linear regression model.

**Additional Resources**

How to Perform Simple Linear Regression in R

How to Perform Multiple Linear Regression in R

How to Perform Quantile Regression in R