---
title: "Positive Association — AP Stats Definition & Exam Guide"
description: "Positive association means as one variable increases, the other tends to increase. Learn how to describe direction on AP Stats scatterplots and link it to r and slope."
canonical: "https://fiveable.me/ap-stats/key-terms/positive-association"
type: "key-term"
subject: "AP Statistics"
unit: "Unit 5"
---

# Positive Association — AP Stats Definition & Exam Guide

## Definition

In AP Statistics, a positive association is a relationship between two quantitative variables in which, as values of one variable increase, values of the other variable tend to increase. It describes the direction of a scatterplot and shows up as a positive correlation coefficient (r > 0) and a positive slope.

## What It Is

Positive association is the "[direction](/ap-stats/key-terms/direction "fv-autolink")" part of describing a [scatterplot](/ap-stats/key-terms/scatterplot "fv-autolink"). When you plot bivariate quantitative data (two numeric values for each individual, one on the x-axis and one on the y-axis), a positive association means the cloud of points generally rises from left to right. As the explanatory variable goes up, the response variable tends to go up too. Think hours studied and exam score, or temperature and ice cream sales.

The word "tend" is doing real work here. A positive association doesn't mean every single point follows the pattern, it means the overall trend is upward. Direction is just one of four things the CED wants in any scatterplot description, alongside form ([linear](/ap-stats/unit-5/representing-relationship-between-two-quantitative-variables/study-guide/3rWWsKXcnbYlqY64hQ1j "fv-autolink") or non-linear), strength (how tightly points follow the pattern), and unusual features (outliers, clusters). A positive association can be strong or weak, linear or curved. "Positive" only tells you which way the relationship leans.

## Why It Matters

Positive association lives in Topic 2.4 (Representing the Relationship Between Two [Quantitative Variables](/ap-stats/key-terms/quantitative-variable "fv-autolink")) in [Unit 2](/ap-stats/unit-2 "fv-autolink") and directly supports learning objective 2.4.B, describing the characteristics of a scatterplot. The essential knowledge spells it out: direction can be positive or negative, and positive means both variables tend to increase together. But this idea doesn't stay in Topic 2.4. It's the conceptual foundation for the sign of the correlation coefficient r, the sign of the slope in a least-squares regression line, and eventually the hypotheses you test when doing inference for slopes. If you can't read direction off a scatterplot, everything downstream in regression gets harder. It's also one of the easiest places to lose communication points on FRQs, because graders want "direction" named explicitly when you describe a scatterplot.

## Connections

### [Correlation Coefficient (Unit 2)](/ap-stats/key-terms/correlation-coefficient)

The sign of r is positive association turned into a number. If a scatterplot shows a positive association, r will be positive (between 0 and 1). Direction gives you the sign of r; strength gives you how close it is to 1.

### [Scatter Plot (Unit 2)](/ap-stats/key-terms/scatter-plot)

You can only judge positive association by looking at a scatterplot (or computing r from the data behind one). The full CED description checklist is form, direction, strength, and unusual features, and "positive" is your answer to the direction box.

### [Explanatory Variable (Unit 2)](/ap-stats/key-terms/explanatory-variable)

Positive association is usually phrased as "as the [explanatory variable](/ap-stats/key-terms/explanatory-variable "fv-autolink") increases, the response variable tends to increase." Knowing which variable is which lets you write that sentence in context, which is exactly how graded responses want it.

### Slope of the Least-Squares Regression Line (Units 2 & 9)

A positive association produces a positive slope in ŷ = a + bx, and a negative association produces a negative slope. In Unit 9, testing whether a true positive association exists becomes a [hypothesis test](/ap-stats/key-terms/hypothesis-test "fv-autolink") on the slope (Hₐ: β > 0).

## On the AP Exam

Multiple-choice questions test this in two directions. Some give you a scatterplot or a description and ask you to characterize the relationship, where a stem like "strong positive association with increasing variability at higher temperatures" (temperature vs. ice cream sales) is typical. Others give you a number and make you translate it back into direction, like recognizing that r = -0.78 means a negative association, or that ŷ = 25000 - 2500x for car age and resale value has a negative slope, so the association is negative. On FRQs, scatterplot description questions expect all four elements: form, direction, strength, and unusual features, in context. Writing "there is a strong, positive, linear association between hours studied and exam score" earns the point. Writing just "the variables are related" does not. Also avoid causal language. Say "is associated with," not "causes," unless the data come from a randomized experiment.

## positive association vs Strong association

Positive describes direction; strong describes strength. They're independent. You can have a strong negative association (r = -0.95) or a weak positive one (r = 0.2). Students often write "strong association" when a question asks for direction, or assume positive means strong. A correlation of r = -0.78 is stronger than r = 0.72, even though one is negative, because strength is about how close |r| is to 1.

## Key Takeaways

- A positive association means that as values of one variable increase, values of the other variable tend to increase, which appears as an upward trend from left to right on a scatterplot.
- Direction (positive or negative) is one of four required parts of a scatterplot description, along with form, strength, and unusual features.
- Positive association corresponds to a positive correlation coefficient (r > 0) and a positive slope in the least-squares regression line.
- Positive does not mean strong; r = 0.2 is a weak positive association while r = -0.95 is a strong negative one, because strength depends on how close |r| is to 1.
- Say variables "tend to" increase together, since individual points can break the pattern even when the overall direction is positive.
- A positive association alone never proves causation; only a well-designed randomized experiment can support a causal claim.

## FAQs

### What is a positive association in AP Stats?

A positive association is a relationship between two quantitative variables where, as one [variable](/ap-stats/unit-1/language-variation-variables/study-guide/nKpeaxi1H3Ht9aFhTHKt "fv-autolink") increases, the other tends to increase as well. On a scatterplot it looks like a point cloud rising from left to right, and it's part of describing direction under learning objective 2.4.B.

### Does a positive association mean one variable causes the other?

No. A positive association only describes a pattern in the data, not a causal mechanism. Unless the data come from a randomized experiment, a lurking variable could be driving both, so on the exam you should write "is associated with" rather than "causes."

### How is positive association different from positive correlation?

Association is the broader visual pattern, while correlation (r) specifically measures the strength and direction of a linear relationship. A scatterplot can show a positive but curved association, and r would understate it because r only captures linear patterns.

### Is r = -0.78 a stronger relationship than r = 0.72?

Yes. Strength depends on how close |r| is to 1, so -0.78 indicates a stronger (negative) linear association than 0.72 does (positive). The sign tells you direction, not strength.

### How do I tell if an association is positive from a regression equation?

Check the sign of the slope. In ŷ = 25000 - 2500x for car age and resale value, the slope is -2500, so the association is negative. A positive slope coefficient would mean a positive association.

## Related Study Guides

- [5.1 Graphical Representations Between Two Quantitative Variables](/ap-stats/unit-5/representing-relationship-between-two-quantitative-variables/study-guide/3rWWsKXcnbYlqY64hQ1j)

## Structured Data

```json
{"@context":"https://schema.org","@graph":[{"@type":"LearningResource","@id":"https://fiveable.me/ap-stats/key-terms/positive-association#resource","name":"Positive Association — AP Stats Definition & Exam Guide","url":"https://fiveable.me/ap-stats/key-terms/positive-association","learningResourceType":"Concept explainer","educationalLevel":"AP® / High School","about":{"@id":"https://fiveable.me/ap-stats/key-terms/positive-association#term"},"audience":{"@type":"EducationalAudience","educationalRole":"student"},"dateModified":"2026-06-11T05:22:53.521Z","isPartOf":{"@type":"Collection","name":"AP Statistics Key Terms","url":"https://fiveable.me/ap-stats/key-terms"},"publisher":{"@type":"Organization","name":"Fiveable","url":"https://fiveable.me"}},{"@type":"DefinedTerm","@id":"https://fiveable.me/ap-stats/key-terms/positive-association#term","name":"positive association","description":"In AP Statistics, a positive association is a relationship between two quantitative variables in which, as values of one variable increase, values of the other variable tend to increase. It describes the direction of a scatterplot and shows up as a positive correlation coefficient (r > 0) and a positive slope.","url":"https://fiveable.me/ap-stats/key-terms/positive-association","inDefinedTermSet":{"@type":"DefinedTermSet","name":"AP Statistics Key Terms","url":"https://fiveable.me/ap-stats/key-terms"}},{"@type":"FAQPage","mainEntity":[{"@type":"Question","name":"What is a positive association in AP Stats?","acceptedAnswer":{"@type":"Answer","text":"A positive association is a relationship between two quantitative variables where, as one [variable](/ap-stats/unit-1/language-variation-variables/study-guide/nKpeaxi1H3Ht9aFhTHKt \"fv-autolink\") increases, the other tends to increase as well. On a scatterplot it looks like a point cloud rising from left to right, and it's part of describing direction under learning objective 2.4.B."}},{"@type":"Question","name":"Does a positive association mean one variable causes the other?","acceptedAnswer":{"@type":"Answer","text":"No. A positive association only describes a pattern in the data, not a causal mechanism. Unless the data come from a randomized experiment, a lurking variable could be driving both, so on the exam you should write \"is associated with\" rather than \"causes.\""}},{"@type":"Question","name":"How is positive association different from positive correlation?","acceptedAnswer":{"@type":"Answer","text":"Association is the broader visual pattern, while correlation (r) specifically measures the strength and direction of a linear relationship. A scatterplot can show a positive but curved association, and r would understate it because r only captures linear patterns."}},{"@type":"Question","name":"Is r = -0.78 a stronger relationship than r = 0.72?","acceptedAnswer":{"@type":"Answer","text":"Yes. Strength depends on how close |r| is to 1, so -0.78 indicates a stronger (negative) linear association than 0.72 does (positive). The sign tells you direction, not strength."}},{"@type":"Question","name":"How do I tell if an association is positive from a regression equation?","acceptedAnswer":{"@type":"Answer","text":"Check the sign of the slope. In ŷ = 25000 - 2500x for car age and resale value, the slope is -2500, so the association is negative. A positive slope coefficient would mean a positive association."}}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"AP Statistics","item":"https://fiveable.me/ap-stats"},{"@type":"ListItem","position":2,"name":"Key Terms","item":"https://fiveable.me/ap-stats/key-terms"},{"@type":"ListItem","position":3,"name":"Unit 5","item":"https://fiveable.me/ap-stats/unit-5"},{"@type":"ListItem","position":4,"name":"positive association"}]}]}
```