# How to Calculate Residual Sum of Squares in R

A residual is the difference between an observed value and a predicted value in a regression model.

It is calculated as:

Residual = Observed value – Predicted value

One way to understand how well a regression model fits a dataset is to calculate the residual sum of squares, which is calculated as:

Residual sum of squares = Σ(ei)2

where:

• Σ: A Greek symbol that means “sum”
• ei: The ith residual

The lower the value, the better a model fits a dataset.

We can easily calculate the residual sum of squares for a regression model in R by using one of the following two methods:

```#build regression model
model <- lm(y ~ x1 + x2 + ..., data = df)

#calculate residual sum of squares (method 1)
deviance(model)

#calculate residual sum of squares (method 2)
sum(resid(model)^2)
```

Both methods will produce the exact same results.

The following example shows how to use these functions in practice.

### Example: Calculating Residual Sum of Squares in R

For this example, we’ll use the built-in mtcars dataset in R:

```#view first six rows of mtcars dataset

mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
```

The following code shows how to fit a multiple linear regression model for this dataset and calculate the residual sum of squares of the model:

```#build multiple linear regression model
model <- lm(mpg ~ wt + hp, data = mtcars)

#calculate residual sum of squares (method 1)
deviance(model)

 195.0478

#calculate residual sum of squares (method 2)
sum(resid(model)^2)

 195.0478```

We can see that the residual sum of squares turns out to be 195.0478.

If we have two competing models, we can calculate the residual sum of squares for both to determine which one fits the data better:

```#build two different models
model1 <- lm(mpg ~ wt + hp, data = mtcars)
model2 <- lm(mpg ~ wt + disp, data = mtcars)

#calculate residual sum of squares for both models
deviance(model1)

 195.0478

deviance(model2)

 246.6825
```

We can see that the residual sum of squares for model 1 is lower, which indicates that it fits the data better than model 2.

We can confirm this by calculating the R-squared of each model:

```#build two different models
model1 <- lm(mpg ~ wt + hp, data = mtcars)
model2 <- lm(mpg ~ wt + disp, data = mtcars)

#calculate R-squared for both models
summary(model1)\$r.squared

 0.8267855
summary(model2)\$r.squared

 0.7809306```

The R-squared for model 1 turns out to be higher, which indicates that it’s able to explain more of the variance in the response values compared to model 2.