# How to Calculate SST, SSR, and SSE in R

We often use three different sum of squares values to measure how well a regression line actually fits a dataset:

1. Sum of Squares Total (SST) – The sum of squared differences between individual data points (yi) and the mean of the response variable (y).

• SST = Σ(yiy)2

2. Sum of Squares Regression (SSR) – The sum of squared differences between predicted data points (ŷi) and the mean of the response variable(y).

• SSR = Σ(ŷiy)2

3. Sum of Squares Error (SSE) – The sum of squared differences between predicted data points (ŷi) and observed data points (yi).

• SSE = Σ(ŷi – yi)2

The following step-by-step example shows how to calculate each of these metrics for a given regression model in R.

### Step 1: Create the Data

First, let’s create a dataset that contains the number of hours studied and exam score received for 20 different students at a certain college:

```#create data frame
df <- data.frame(hours=c(1, 1, 1, 2, 2, 2, 2, 2, 3, 3,
3, 4, 4, 4, 5, 5, 6, 7, 7, 8),
score=c(68, 76, 74, 80, 76, 78, 81, 84, 86, 83,
88, 85, 89, 94, 93, 94, 96, 89, 92, 97))

#view first six rows of data frame

hours score
1     1    68
2     1    76
3     1    74
4     2    80
5     2    76
6     2    78```

### Step 2: Fit a Regression Model

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

```#fit regression model
model <- lm(score ~ hours, data = df)

#view model summary
summary(model)

Call:
lm(formula = score ~ hours, data = df)

Residuals:
Min      1Q  Median      3Q     Max
-8.6970 -2.5156 -0.0737  3.1100  7.5495

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  73.4459     1.9147  38.360  < 2e-16 ***
hours         3.2512     0.4603   7.063 1.38e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.289 on 18 degrees of freedom
Multiple R-squared:  0.7348,	Adjusted R-squared:  0.7201
F-statistic: 49.88 on 1 and 18 DF,  p-value: 1.378e-06
```

### Step 3: Calculate SST, SSR, and SSE

We can use the following syntax to calculate SST, SSR, and SSE:

```#find sse
sse <- sum((fitted(model) - df\$score)^2)
sse

[1] 331.0749

#find ssr
ssr <- sum((fitted(model) - mean(df\$score))^2)
ssr

[1] 917.4751

#find sst
sst <- ssr + sse
sst

[1] 1248.55
```

The metrics turn out to be:

• Sum of Squares Total (SST): 1248.55
• Sum of Squares Regression (SSR): 917.4751
• Sum of Squares Error (SSE): 331.0749

We can verify that SST = SSR + SSE:

• SST = SSR + SSE
• 1248.55 = 917.4751 + 331.0749

We can also manually calculate the R-squared of the regression model:

• R-squared = SSR / SST
• R-squared = 917.4751 / 1248.55
• R-squared = 0.7348

This tells us that 73.48% of the variation in exam scores can be explained by the number of hours studied.

You can use the following calculators to automatically calculate SST, SSR, and SSE for any simple linear regression line:

## 2 Replies to “How to Calculate SST, SSR, and SSE in R”

1. April says:

In the SSE equation at the top of the article, the y-hat and y seem to be transposed.

2. bipul verma says:

Kindly mention that people use different terminologies
SSR sometimes mean sum of squared residuals
SSE sometimes mean explained sum of square