# How to Use Method of Least Squares in R

The method of least squares is a method we can use to find the regression line that best fits a given dataset.

The following video provides a brief explanation of this method:

To use the method of least squares to fit a regression line in R, we can use the lm() function.

This function uses the following basic syntax:

```model <- lm(response ~ predictor, data=df)
```

The following example shows how to use this function in R.

## Example: Method of Least Squares in R

Suppose we have the following data frame in R that shows the number of hours studied and the corresponding exam score for 15 students in some class:

```#create data frame
df <- data.frame(hours=c(1, 2, 4, 5, 5, 6, 6, 7, 8, 10, 11, 11, 12, 12, 14),
score=c(64, 66, 76, 73, 74, 81, 83, 82, 80, 88, 84, 82, 91, 93, 89))

#view first six rows of data frame

hours score
1     1    64
2     2    66
3     4    76
4     5    73
5     5    74
6     6    81
```

We can use the lm() function to use the method of least squares to fit a regression line to this data:

```#use method of least squares to fit regression line
model <- lm(score ~ hours, data=df)

#view regression model summary
summary(model)

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

Residuals:
Min     1Q Median     3Q    Max
-5.140 -3.219 -1.193  2.816  5.772

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)   65.334      2.106  31.023 1.41e-13 ***
hours          1.982      0.248   7.995 2.25e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.641 on 13 degrees of freedom
Multiple R-squared:  0.831,	Adjusted R-squared:  0.818
F-statistic: 63.91 on 1 and 13 DF,  p-value: 2.253e-06
```

From the values in the Estimate column of the output, we can write the following fitted regression line:

Exam Score = 65.334 + 1.982(Hours)

Here’s how to interpret each coefficient in the model:

• Intercept: For a student who studies 0 hours, the expected exam score is 65.334.
• hours: For each additional hour studied, the expected exam score increases by 1.982.

We can use this equation to estimate the exam score a student will receive based on their hours studied.

For example, if a student studies for 5 hours, we would estimate that their exam score would be 75.244:

Exam Score = 65.334 + 1.982(5) = 75.244

Lastly, we can create a scatter plot of the original data with the fitted regression line overlaid on the plot:

```#create scatter plot of data
plot(df\$hours, df\$score, pch=16, col='steelblue')

#add fitted regression line to scatter plot
abline(model)
```

The blue circles represent the data and the black line represents the fitted regression line.