Mei measures the height and the arm span of 12 friends. Can a graph help her guess a friend's arm span from their height?
Your guess
Pick one before you read on. It is fine to be wrong.
By the end you can
Draw a scatter graph and a line of best fit
Describe positive, negative or no correlation
Explain why correlation does not prove one thing causes another
Mei measures 12 friends. For each friend she writes the height and the arm span, from fingertip to fingertip, in centimetres. Each friend gives a pair of numbers.
The first friend is 142 cm tall with an arm span of 141 cm.
A scatter graph plots each pair as one point. The height is the x value and the arm span is the y value. Neither axis starts at 0 here, so each axis shows a zigzag break near its corner.
Draw it first
Draw the scatter graph and the line of best fit
1 Plot one dot for each friend: height across, arm span up.
2 Find the mean height and the mean arm span. Plot that mean point.
3 Draw one straight line through the middle of the dots and through the mean point. Aim for about as many dots on each side.
The straight line is the line of best fit. It is drawn by eye, so two people will draw slightly different lines. A good line follows the direction of the dots. It has about as many dots above it as below, and it goes through the mean point.
The line drawn here passes through (140, 141) and (170, 171). It goes up 1 for each 1 across, so its rule is y = x + 1.
Worked example
The 12 heights add up to 1 920 cm and the 12 arm spans to 1 932 cm. Find the mean point.
Mean height = 1 920 ÷ 12 = 160 cm.
Mean arm span = 1 932 ÷ 12 = 161 cm.
The mean point is (160, 161). The line of best fit should pass through it.
The line y = x + 1 passes through the mean point, because 160 + 1 = 161.
Answer: The mean point is (160, 161)
The pattern in the dots is called correlation. Here the dots rise from left to right. Taller friends tend to have longer arm spans. This is positive correlation: as one value rises, the other tends to rise.
A candle burns. The dots fall from left to right: negative correlation.
When the dots fall from left to right, as one value rises the other tends to fall. This is negative correlation. A burning candle gets shorter as the hours go by.
The number of letters in a name has nothing to do with jumping. The dots show no pattern: no correlation.
The correlation is strong when the dots lie close to a straight line, and weak when they are more spread out. The steepness of the line does not matter. A gentle slope can still have dots tight around it, as long as the line is not level.
Worked example
Use the line y = x + 1 to estimate the arm spans of friends 168 cm and 150 cm tall.
For a height of 168 cm, the line gives y = 168 + 1 = 169.
For a height of 150 cm, the line gives y = 150 + 1 = 151.
Both heights are inside the data, which runs from 142 cm to 176 cm. So the estimates are reasonable.
Answer: About 169 cm and about 151 cm
Go up from 168 cm to the line, then across to the arm span axis: about 169 cm.
A line of best fit is for estimates inside the range of the data. This is interpolation. Going beyond the data is extrapolation. It is unreliable, because the pattern may not carry on.
Solid: inside the data. Dashed: extended too far. The dashed line reaches 0 cm at about 8 hours. Beyond that it would give a candle with a negative length.
Worked example
The candle line is y = 20 − 2.6x, with y the length in cm and x the hours. Find the length at 3.5 hours. Is the line fair to use at 10 hours?
At 3.5 hours the line says 2.6 × 3.5 = 9.1 cm have burned.
The length is 20 − 9.1 = 10.9 cm.
At 10 hours the line says 2.6 × 10 = 26 cm have burned.
That is more than the 20 cm of candle, so the length would be negative. Ten hours is outside the data, so the line cannot be used there.
Answer: About 10.9 cm. No: the line gives a negative length
Now a warning. Two things can be correlated without one causing the other. On a beach, the number of ice creams sold each day and the number of cases of sunburn rise together.
See it three ways
With things
On 8 days a beach counts the ice creams sold and the cases of sunburn.
As a picture
In words
Strong positive correlation. But ice cream does not burn skin, and sunburn does not make people buy ice cream. On hot sunny days both go up. The hot sunny weather is the hidden third factor.
In symbols
Correlation does not prove cause.
A wrong idea many people have
If two things are correlated, one must cause the other.
Why it feels right: When two things rise together it feels as if one must be pulling the other.
The test that breaks it: Ice creams sold and sunburn cases are strongly correlated. Neither causes the other. Hot sunny weather makes both rise.
What is true: Correlation shows that two things go together. To say one causes the other we need other evidence, and we should look for a hidden third factor.
Try it
Measure hand span and foot length
You need: 8 people to measure, a ruler, squared paper.
For each person, measure the hand span and the foot length in centimetres.
Plot a dot for each person: hand span across, foot length up.
Find the mean point. Draw a line of best fit through it with about as many dots on each side.
What you will see: The dots rise from left to right. People with a bigger hand span tend to have a longer foot. Your own dots may be more or less spread out.
If you cannot try it, this is what happens.
A wrong idea many people have
A steeper line means stronger correlation.
Why it feels right: A steep line looks more definite than a flat one.
The test that breaks it: Draw two scatter graphs. In one, the dots hug a flat line. In the other, the dots are spread loosely around a steep line. The first has stronger correlation.
What is true: Strength depends on how close the dots are to the line. The steepness depends on the scales chosen for the axes.
Key words
scatter graph
a graph with one dot for each pair of measurements.
correlation
the pattern between two measurements: positive, negative or none.
line of best fit
a straight line drawn through the middle of the dots on a scatter graph.
extrapolation
using a line beyond the range of the data, which is unreliable.
Back to your guess
The answer is B: Plot each friend as one point, height across and arm span up, and look for a pattern.
One point for each friend, with the height across and the arm span up, makes a scatter graph. If the points fall along a line, the pattern can be used to estimate. Totals and bars lose the link between a friend's two measurements.
Check yourself
Try each one before you look. A wrong answer tells you where to go next.
A walker records the temperature at different heights climbed. What does the scatter graph show?
See the answer
Negative correlation
The dots fall from left to right. As the climb gets bigger the temperature gets lower.
If you chose A: In positive correlation the dots rise from left to right. Here they fall: as the climb rises, the temperature falls. Go to M4.7.6.
If you chose B: The dots follow a clear pattern, falling from left to right. No correlation would be a scatter with no direction. Go to M4.7.6.
Jump heights of 10 people are plotted against the day of the month on which each was born. What does this scatter graph show?
See the answer
No correlation
The dots show no pattern. Knowing the day of the month a person was born tells us nothing about the jump height.
If you chose B: The dots do not rise from left to right. They are scattered with no direction. Go to M4.7.6.
If you chose C: The dots do not fall from left to right either. They are scattered with no direction. Go to M4.7.6.
The line of best fit for the arm spans is y = x + 1. Estimate the arm span of a new friend, not in the table, who is 165 cm tall.
See the answer
166 cm
y = x + 1 with x = 165 gives y = 165 + 1 = 166.
If you chose A: You used the height as the arm span. The line is y = x + 1, so add 1. Go to M4.7.6.
If you chose B: You took 1 away. The rule is y = x + 1, so the arm span is 1 more than the height. Go to M4.7.6.
If you chose C: 163 cm is the arm span of the friend in the table who is 165 cm tall. This is a new friend, so use the line. Go to M4.7.6.
The number of umbrellas sold and the number of road accidents in a town rise together. What is the best conclusion?
See the answer
Rainy weather probably makes both rise.
On rainy days more people buy umbrellas, and wet roads and poor sight bring more accidents. Rain is a hidden third factor. Correlation does not prove that one causes the other.
If you chose A: Umbrellas do not make crashes happen. Two things can rise together without one causing the other. Go to M4.7.6.
If you chose B: There is no evidence for that either. A hidden third factor is a better explanation. Go to M4.7.6.
A line of best fit was drawn from friends whose heights run from 142 cm to 176 cm. For which height is an estimate of the arm span the least reliable?
See the answer
190 cm
190 cm is beyond the tallest friend in the data, 176 cm. Extending the line there is extrapolation, and the pattern may not carry on.
If you chose A: 150 cm is inside the range of the data, so an estimate there uses the pattern the data show. Go to M4.7.6.
If you chose C: 160 cm is in the middle of the data, where the line is most trustworthy. Go to M4.7.6.
What makes the correlation in a scatter graph strong?
See the answer
The dots lie close to a straight line.
The closer the dots are to a line, the stronger the correlation.
If you chose A: Steepness depends on the scales of the axes. Strength is about how close the dots are to the line. Go to M4.7.6.
If you chose C: Many dots can be widely spread. Strength depends on how close the dots are to the line, not on how many there are. Go to M4.7.6.
From an earlier chapter
A bar of a histogram is 5 cm wide and 2.4 plants per cm high. How many plants does it stand for?
See the answer
12
The frequency is the area of the bar: 5 × 2.4 = 12.
A line of best fit has the rule y = 2x + 3. Estimate y when x = 7.
See the answer
17
y = 2 × 7 + 3 = 14 + 3 = 17.
Keep it
Copy this into your exercise book.
Scatter graph: one dot for each pair. Correlation: positive, negative or none. The line of best fit passes through the mean point. Use it inside the data. Correlation is not cause.
Draw: A scatter graph with a line of best fit, and under it the words positive, negative and none beside small sketches.