Describing Correlation
Cricket Australia analysts use correlation to find which batting statistics best predict match wins, not always the obvious ones. To use correlation like this, you need to describe both direction (positive/negative) and strength (strong/moderate/weak) precisely.
Practise this lesson
Three printable worksheets that build from foundations to mastery, or build your own from any module’s questions.
A scatterplot shows that taller people tend to earn more. Before reading on, decide: is this relationship strong or weak? Is it positive or negative? How would you describe it in a complete sentence?
Correlation describes the relationship between two variables in a scatterplot. It has two components you must always state together.
Direction: positive (both variables increase together), negative (as one increases the other decreases), or no correlation.
Strength: strong (points close to a line), moderate, or weak (widely scattered).
e.g. "strong positive linear correlation"
Key facts
- The three possible directions: positive, negative, none
- The three possible strengths: strong, moderate, weak
- The correct language for a full correlation description
Concepts
- What "direction" means visually on a scatterplot
- What "strength" means in terms of how close points are to a line
- Why both components are needed to describe correlation
Skills
- Identify direction and strength from a scatterplot description
- Write a complete correlation description using correct vocabulary
- Match a description to one of several scatterplot patterns
The direction of correlation describes whether the two variables move in the same direction or opposite directions as you move along the x-axis.
- Positive: Higher x tends to pair with higher y. The points slope upward from left to right. Example: hours studied and exam score, more study tends to give higher scores.
- Negative: Higher x tends to pair with lower y. The points slope downward from left to right. Example: temperature and hot coffee sales, hotter days mean fewer hot drinks sold.
- No correlation: No consistent pattern. Example: shoe size and IQ score, no systematic relationship.
Correlation direction: positive means both variables increase together; negative means one increases as the other decreases; no correlation means there is no consistent linear trend between the variables.
Pause, copy the three correlation directions: positive (points rise left to right), negative (points fall left to right), and no correlation (points show no consistent direction) with a one-line description of each into your book.
Quick check: A scatterplot shows that as daily temperature increases, the number of umbrellas sold decreases. What direction is this correlation?
Direction tells you whether the pattern goes up, down, or nowhere, but two scatterplots can both be positive and look completely different. Strength captures how tightly the points cluster around an imaginary straight line: strong means points are close to the line, moderate means some scatter, and weak means points are widely spread with only a vague trend.
The strength of correlation describes how closely the points follow a straight line.
- Strong: Points are tightly clustered along a line, you can predict y from x with high accuracy. The trend is obvious at a glance.
- Moderate: Points follow a general trend but with noticeable scatter, you can predict the direction of y but not its exact value.
- Weak: Points are widely scattered, there is barely a pattern, and x is a poor predictor of y.
Think of strength as "how close together are the dots?", the tighter the band of points around a straight line, the stronger the correlation.
Correlation strength describes how tightly the points cluster around a linear trend: strong (tight cluster), moderate (visible trend with scatter), or weak (barely discernible trend). Strength and direction are reported together.
Pause, copy the three strength categories: strong (points close to a line), moderate (noticeable scatter around the line), and weak (wide scatter, pattern barely visible) into your book.
Which does NOT belong? Descriptions used when classifying correlation:
Direction (positive/negative/none) and strength (strong/moderate/weak) must always appear together in an exam description, writing only one of them earns partial marks at best. Combine them in one sentence using the actual variable names: for example, "there is a strong positive association between hours of study and exam score".
A complete correlation description uses both direction AND strength. The standard HSC format is:
[strength] + [direction] + "linear correlation"
Examples:
- "There is a strong positive linear correlation between hours of study and exam score."
- "There is a moderate negative linear correlation between temperature and hot coffee sales."
- "There is a weak positive linear correlation between age and income." (Note: weak, not strong, because the scatter is large.)
- "There is no linear correlation between shoe size and reading ability."
A complete correlation description uses both direction and strength: e.g., 'strong positive', 'weak negative', or 'no correlation'. In HSC responses, always include both components and relate them to the actual variable names.
Pause, copy the combined description template: "[strong/moderate/weak] [positive/negative] association between [x-variable] and [y-variable]" and write two example sentences using different real variable pairs into your book.
Complete: A scatterplot showing points tightly grouped along a downward-sloping line would be described as a linear correlation.
Worked examples · 3 in a row, reveal as you go
A scatterplot of study hours vs exam score shows points rising from lower-left to upper-right, closely grouped around a straight line. Describe the correlation.
Four scatterplot descriptions: (A) points slope down, tightly grouped. (B) points slope up, widely scattered. (C) random scatter. (D) points slope up, closely grouped. Which matches "weak positive linear correlation"?
A researcher plots the number of cigarettes smoked per day (x) against resting heart rate (y). The points show a general upward trend but with noticeable scatter. Describe the correlation.
For each situation below, write a full correlation description (strength + direction + "linear correlation"):
- Daily temperature (°C) vs hot coffee sales: points slope steeply down, tightly clustered.
- Number of absences vs final grade: general downward trend, wide scatter.
- Birth month vs favourite colour: no pattern visible.
- Height vs weight for adults: clear upward trend, moderate scatter.
At the start you described the relationship between height and earnings. This relationship, if it exists, would likely be described as a weak positive linear correlation there is an upward trend (taller tends to earn more in some datasets), but the scatter is large because many other factors (education, occupation, experience) affect earnings much more than height does.
Pick your answer, then rate your confidence. Each retry pulls a fresh mix from the bank.
Q1. A scatterplot shows the relationship between hours of TV watched per day and academic grade (%). The points cluster loosely around a downward-sloping line. (a) What is the direction of this correlation? (b) What is the strength? (c) Write a complete description of the correlation. (3 marks)
Q2. A student says: "The scatterplot shows positive correlation." Explain why this description is incomplete and rewrite it in full. (2 marks)
Answers (click to reveal)
Activity answers: (1) Strong negative linear correlation. (2) Weak negative linear correlation. (3) No linear correlation. (4) Moderate positive linear correlation.
Q1 (3 marks): (a) Negative, as TV hours increase, grades decrease [1]. (b) Moderate, points cluster loosely (not tightly) around the line [1]. (c) "There is a moderate negative linear correlation between hours of TV watched per day and academic grade" [1].
Q2 (2 marks): The description is incomplete because it states only the direction (positive) but not the strength [1]. A complete description requires both: e.g. "There is a strong positive linear correlation between the two variables" [1].
Classify direction and strength, write full descriptions, and match patterns. Beat the boss to bank a tier. Replays welcome.
Climb platforms answering correlation questions. Pool: lesson 02.
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