# Scatter Graphs Flashcards

1️⃣ Familiarise yourself with the flashcards:

• Look through all the flashcards to see what’s on both sides.
• Make sure you understand the information on each card. If something’s unclear, click the link to the revision notes at the bottom of the page for more details.

2️⃣ Test yourself:

• Look at the question or prompt on each card and try to remember the answer before flipping it over.
• Check the answer and make a note of any cards you find challenging and need to go over more.

3️⃣ Consistently Review and Practice:

• Use spaced repetition: spend more time on the cards you struggle with and go over them more often.
• Regularly review all the flashcards to help you better understand and retain the information over time.

Note: We may include questions that have multiple correct answers. It’s useful to remember specific examples to understand these concepts better.

## What are scatter graphs useful for?

Scatter graphs are useful for identifying relationships between different sets of data, visualising trends, and making predictions based on observed patterns.

## How do you construct a scatter graph?

Collect two related sets of data, draw the x and y axes, label them, plot the points, and draw a line of ‘best fit’ to represent the correlation between the datasets.

## What does a strong correlation look like on a scatter graph?

A strong correlation occurs when data points are close to the line of best fit, indicating a clear relationship between the two variables.

## What is the difference between positive and negative correlation?

Positive correlation occurs when an increase in one variable corresponds with an increase in the other.

Negative correlation occurs when an increase in one variable leads to a decrease in the other.

## What is interpolation?

Interpolation is estimating a value within the range of the data based on the line of best fit. The value is not directly measured but guessed based on the data we already have.

## What is extrapolation?

Extrapolation is predicting values beyond the range of the data by extending the line of best fit.

This method carries more uncertainty but can be useful for forecasting based on current trends.