Scatter Plot Worksheet

Most scatter plot worksheets ask students to plot a handful of abstract points and draw a line through them. This one skips the abstraction. Students get two scatter plots already built from real data and have to read what the pattern in the dots is actually telling them, not just where each point lands.

The first scatter plot is small and personal: a kid running an ice cream shop, tracking daily temperature against sales for twelve days. Students read the line of best fit drawn through that scatter and use it to answer a real question — how much should Ari expect to sell when it's 21°C outside? That's reading a scatter plot for a purpose, not just labeling axes.

The second scatter plot raises the stakes. It comes from real wage data out of New Zealand, plotting hours worked against salary. Students have to reckon with something messier than the ice cream example: multiple points stack up at the same x-value, since different people work the same hours but earn different amounts. That's the moment a scatter plot stops being a clean line and starts requiring students to talk about correlation without pretending it's a guarantee.

This works well as a follow-up to a lesson on correlation and line of best fit, or as a warm-up the day after you introduce reading scatter plots. It assumes students already know how to plot points; the focus here is entirely on interpretation — is there a pattern, how strong is it, and what can you actually predict from it.

Both scenarios also build a habit students need well beyond this unit: looking at a scatter plot and asking how much it really proves. A line of best fit is a description of a trend, not a guarantee, and the worksheet never lets students forget that. That distinction matters just as much when a student is looking at a stock chart or a savings projection later on as it does when they're staring at Ari's ice cream sales. Below is the worksheet exactly as it appears in Rapunzl's curriculum, ready to assign as-is or adapt for your classroom.

This worksheet comes from Module 14 of Rapunzl's financial literacy curriculum, part of the Financial Equations unit that connects data interpretation to the kind of financial decisions students will actually run into.

Linear Equations In Practice

Through this activity, students will put linear equations into practice with real-world financial examples, such as calculating interest and analyzing investment returns. By the end of this exercise, students will be able to apply their understanding of linear equations to real-world financial scenarios and make informed decisions about their finances.

Part 1: Ice Cream Sales

Ari recorded the daily high temperature (in Celsius) and total sales from his ice cream shop for 12 days. He plotted the data on the graph below and drew a line of best fit.

1. Does the data appear to have a correlation? If so, is it positive or negative?

 

1. Based on the line of best fit that Ari drew, how much money in sales should he expect when the temperature is 21℃?

 

1. In your own words, explain why it makes sense that this data has a correlation?

 

1. Create a scenario like Ari’s ice cream shop that will have a negative correlation.

 

Part 2: Real World Data

The following graph comes from real world salary data from a study in New Zealand. Inspect the graph then answer the following questions.

1. What does it mean that there are multiple different y values at 40 hours worked?

 

1. What is the approximate slope of the line of best fit drawn in this graph? What does this mean?

 

1. There is not actually a fixed increase in salary based on the number of hours worked. What does our line of best fit tell us if not exactly how salary will increase?

Teacher Notes

The two parts of this worksheet are deliberately different in difficulty. Part 1 is a controlled, single-cause relationship: temperature drives ice cream sales, so the scatter plot shows a clean positive correlation that's easy to see and easy to justify. Use it to confirm students can read a line of best fit before moving on.

Part 2 is where the real thinking happens. The wage scatter plot is messy — hours worked doesn't determine salary the way temperature drives ice cream sales, and students need to grapple with that directly in question 3. Watch for students who want to say the line of best fit "proves" a fixed hourly rate. It doesn't, and the worksheet is built to surface that misconception rather than avoid it.

Question 4 asks students to invent their own negative-correlation scatter plot. This is a good moment for a quick share-out: hearing five or six different examples out loud does more to cement the idea of negative correlation than any additional worksheet item would. Common student answers involve things like hours of sunlight and hot chocolate sales, or price and quantity sold — both work well as discussion anchors if a student gets stuck.

This activity is one piece of the full Financial Equations unit inside the Rapunzl teacher portal, sequenced with guided notes, slides, and additional practice sets that build on scatter plots, line of best fit, and correlation.

Want the rest of the Financial Equations unit, plus the tools to run it in your classroom? Book a Rapunzl demo and see how students connect data to real financial decisions.