The Least-Squares Regression Line (College Board AP® Statistics)

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Written by: Mark Curtis

Reviewed by: Dan Finlay

Least-squares regression line

What is the least-squares regression line?

The least-squares regression line is a special type of regression line that:
- minimizes the sum of the squares of the residuals
- and that passes through the mean point $(\bar{x}, \bar{y})$
  - where $\bar{x}$ is the mean of the $x$ -values
  - and $\bar{y}$ is the mean of the $y$ -values
It is used to predict $y$ -values from given $x$ -values
- Its full name is the least-squares regression line of $y$ on $x$
- This is not the same line if you wanted to predict $x$ -values from given $y$ -values
  - That is the least-squares regression line of $x$ on $y$
  - You cannot swap $x$ and $y$

What is the sum of the squares of the residuals?

The sum of the squares of the residuals for any regression line is found by
- calculating the residuals for each data point
- squaring each residual
- then adding together all these squared values

Why is the sum of the squares of the residuals minimized?

Residuals are like errors when comparing regression lines
- A good regression line should minimize the residuals
  - So compare the sum of all the residuals for different regression lines
- However, the sum of all the residuals is zero
  - The positive residuals end up cancelling out the negative ones
- So, instead, compare the sum of the squares of the residuals
  - because squaring the residuals makes them all positive
  - which stops any cancellation
The regression line with the smallest possible sum of the squares of the residuals is the least-squares regression line

What is the equation of the least-squares regression line?

The equation of the least-squares regression line is given by
- $\hat{y} = a + b x$
  - $a$ is the $y$ -intercept
  - $b$ is the slope
  - note the order of the terms
- where $\hat{y}$ is the $y$ -value predicted by the regression line
  - This is usually different to the actual $y$ -value of a data point
- $x$ is the explanatory variable
- $b = r \frac{s_{x}}{s_{y}}$
  - where $r$ is the correlation coefficient
  - $s_{x} = \sqrt{\frac{1}{n - 1} \sum_{}^{} {(x_{i} - \bar{x})}^{2}} = \sqrt{\frac{\sum_{}^{} {(x_{i} - \bar{x})}^{2}}{n - 1}}$
  - $s_{y} = \sqrt{\frac{1}{n - 1} \sum_{}^{} {(y_{i} - \bar{y})}^{2}} = \sqrt{\frac{\sum_{}^{} {(y_{i} - \bar{y})}^{2}}{n - 1}}$
- and $\bar{y} = a + b \bar{x}$
  - which rearranges to $a = \bar{y} - b \bar{x}$ (to find $a$ )
  - You need to find $b$ before you can find $a$
In practice, the equation of the least-squares regression line is found using technology
- e.g. a calculator

Examiner Tips and Tricks

The formulas for the equation of the least-squares regression line are given in the exam.

How do I interpret the slope of a regression line?

The slope, $b$ , of the regression line $\hat{y} = a + b x$ is
- the amount by which the predicted $y$ -variable, $\hat{y}$ , changes for every 1 unit of increase in the $x$ -variable
  - i.e. the increase in $\hat{y}$ per unit increase in $x$

How do I interpret the y-intercept of a regression line?

The $y$ -intercept, $a$ , of the regression line $\hat{y} = a + b x$ is
- the predicted value of $y$ when the explanatory variable, $x$ , equals zero
In some contexts, the y-intercept may not have a logical interpretation

Worked Example

The scatterplot below shows the number of hours spent studying (on the $x$ -axis) against the score in a test out of 16 points (on the $y$ -axis), for five different students.

The equations of three different regression lines are shown, together with sums of squares of their residuals in the table below. The variable $\hat{y}$ is the predicted value of $y$ . One of these three regression lines is the least-squares regression line.

Graph showing three linear regression lines: ŷ = 4x, ŷ = 2 + 3x, and ŷ = 2.4 + 2.8x along with data points marked as black Xs on a grid. Axes labeled "x" and "y".

Regression equation	Sum of the squares of the residuals
$\hat{y} = 2.4 + 2.8 x$	25.6
$\hat{y} = 2 + 3 x$	26
$\hat{y} = 4 x$	40

(a) Explain how you know which regression line is the least-squares regression line.

Answer:

The least-squares regression line minimizes the sum of the squares of the residuals

The regression line $\hat{y} = 2.4 + 2.8 x$ has the smallest sum of the squares of the residuals, as 25.6 < 26 < 40

We are told that one of the three regression lines is the least-squares regression line

This means $\hat{y} = 2.4 + 2.8 x$ is the least-squares regression line

(b) Explain what the $y$ -intercept and the slope of the least-squares regression line mean in context.

Answer:

The $y$ -intercept of a regression line is the predicted value of $y$ when $x$ is zero

The slope of a regression line is the amount of change in the predicted value of $y$ for every increase by 1 in the value of $x$

The $y$ -intercept shows that a student who has done no studying is predicted to score 2.4 (which rounds to 2 points) out of 16

The slope shows that the predicted score of a student increases by 2.8 points per hour of studying

Last updated: 28 August 2024

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