Scatter Plot Correlation Questions- Practice Set

What Scatter Plots Actually Show

A scatter plot is just dots on a graph. That's it. One variable on the x-axis, another on the y-axis, and each dot represents one data point. The whole point is to see if there's a relationship between the two variables.

Students waste time overcomplicating this. You're looking for patterns. Does y increase when x increases? Decrease? Stay flat? That's correlation in its simplest form.

The Three Correlation Types You Must Know

Positive Correlation

When x goes up, y goes up. The dots trend upward from left to right. More hours studied = higher grades. More money spent on ads = more sales. Simple pattern.

Negative Correlation

When x goes up, y goes down. The dots trend downward from left to right. More time spent watching TV = lower grades. Higher price = lower demand. Also simple.

No Correlation

No pattern at all. Dots scattered randomly. Knowing x tells you nothing about y. This is what most students miss—zero correlation is still a valid answer.

Reading Scatter Plots: The Practical Method

Here's how professionals actually read scatter plots:

• First, look at the overall direction. Don't focus on individual dots—squint if you have to.
• Second, check the strength. Tight cluster means strong correlation. Wide scatter means weak correlation.
• Third, ignore outliers unless they're explicitly relevant.
• Fourth, never assume causation. Two variables moving together doesn't mean one causes the other.

That last point trips up more students than any other on exams. Correlation is not causation. Say it out loud. Write it down. Remember it.

Practice Questions

Question 1

A dataset shows ice cream sales on the x-axis and drowning deaths on the y-axis. The dots trend strongly upward. What's the relationship?

Answer: Positive correlation exists. That's it. Don't say ice cream causes drowning or vice versa. Both increase during summer months. There's a lurking variable (temperature) creating the illusion of a direct relationship.

Question 2

A scatter plot has points arranged in a perfect vertical line. What type of correlation does this show?

Answer: None. A vertical line means x doesn't vary—or has no relationship with y. This is a trick question. Some students say "infinite correlation" or "perfect correlation." Wrong. You need variation in both axes for correlation to exist.

Question 3

You're given a scatter plot showing hours of sleep (x) and test scores (y). The points cluster tightly around a line that slopes upward. Describe the correlation.

Answer: Strong positive correlation. The tight clustering around the line indicates strength. The upward slope indicates direction. Students often stop at "positive correlation" and lose points for not addressing strength.

Question 4

A company plots employee tenure (x) against productivity ratings (y). The dots form a cloud with no discernible pattern. What do you conclude?

Answer: No correlation exists between tenure and productivity in this dataset. Longer tenure doesn't predict higher or lower productivity. This is useful information—don't force a relationship that isn't there.

Question 5

Look at the data below. Which shows the strongest correlation?

• Dataset A: correlation coefficient r = 0.92
• Dataset B: correlation coefficient r = -0.85
• Dataset C: correlation coefficient r = 0.31

Answer: Dataset A has the strongest correlation. You're comparing absolute values. 0.92 is closer to 1 (or -1) than 0.85 or 0.31. The sign only indicates direction, not strength.

Correlation Coefficient Reference Table

Negative values follow the same strength scale. r = -0.78 is a strong correlation, just negative in direction.

Common Mistakes That Cost Points

• Confusing correlation with causation. This is the big one. Every single time.
• Misreading the axes. Check which variable is on which axis before answering.
• Ignoring outliers. One dot in the corner doesn't change the overall pattern unless the question specifically asks about it.
• Over-interpreting weak patterns. A slight upward trend with scattered dots is weak correlation, not strong.
• Forgetting to describe both strength and direction. "Positive correlation" isn't complete. Say "strong positive" or "weak negative."

How To Approach Exam Questions

When you see a scatter plot question:

1. Identify the variables on each axis
2. Determine the direction (positive, negative, or none)
3. Assess the strength (how close to a line are the points?)
4. Note any outliers or unusual patterns
5. Answer only what was asked—don't volunteer extra interpretation

If the question asks "describe the correlation," you need direction and strength. If it asks "is there a relationship," you need existence. Read carefully.

Quick Study Guide

Positive correlation: x up, y up ↗

Negative correlation: x up, y down ↘

No correlation: random scatter, no pattern

Strong correlation: points cluster tightly

Weak correlation: points spread widely

r values close to 1 or -1 = strong. r values close to 0 = weak or none.

Correlation does not prove causation. Ever. 🔑

When to Use Scatter Plots

Scatter plots work when you want to:

• Find patterns in two continuous variables
• Spot outliers that don't fit the pattern
• Show relationships to an audience quickly
• Decide if further analysis (like regression) is worth doing

They don't work well for categorical data, small datasets (under 20 points), or when you need precise values rather than patterns.

That's the full picture. Read scatter plots by looking for direction, then strength, then anomalies. Don't invent patterns that aren't there. Don't assume one variable causes changes in another. The math tells you what exists—not why it exists.