A-Level Mathematics Revision — Regression & Correlation
Revise Regression & Correlation for A-Level Mathematics with a topic explanation, worked example and common mistakes. Check the board notes for specification differences.
At a glance
- What StudyVector is
- An exam-practice platform with board-aligned questions, explanations, and adaptive next steps.
- This topic
- Regression & Correlation in A-Level Mathematics: explanation, examples, and practice links on this page.
- Who it’s for
- Students revising A-Level Mathematics for UK exams.
- Exam boards
- Check your course page and the topic board notes for supported specifications.
- Free plan
- Sign up free to use tutor paths and feedback on your answers. Free access is Free daily revision · No card required. Pricing
- What makes it different
- Syllabus-shaped practice and progress tracking—not generic AI answers.
This page includes a topic explanation and a worked example. Check your course for current practice coverage.
Recommended next topic
Next step: Statistical Sampling
Continue in the same course — structured practice and explanations on StudyVector.
Go to Statistical SamplingTopic explanation
What is Regression & Correlation?
Regression and correlation at A-Level involve analysing the relationship between two variables. You will learn to calculate and interpret the product moment correlation coefficient to measure the strength of a linear relationship, and to find the equation of a regression line to make predictions.
Board notes: All A-Level Maths boards (AQA, Edexcel, OCR) cover regression and correlation. The calculation of the product moment correlation coefficient and the equation of the regression line are key topics for all boards.
Step-by-step explanationWorked examples
Worked example
A set of data has a product moment correlation coefficient of 0.8. This indicates a strong positive linear relationship between the two variables. The equation of the regression line of y on x is y = 2x + 5. If x = 10, the predicted value of y is 2(10) + 5 = 25.
Practise this topic
Start with low-focus cards for Regression & Correlation, then move into full exam-style practice when you want the heavier session.
Common mistakes
- 1Confusing correlation with causation. A strong correlation between two variables does not necessarily mean that one causes the other; there may be a third variable involved.
- 2Extrapolating beyond the range of the data when using a regression line to make predictions. The regression line is only valid for the range of the data used to create it.
- 3Incorrectly interpreting the product moment correlation coefficient. A value close to 1 or -1 indicates a strong linear relationship, while a value close to 0 indicates a weak linear relationship.
Regression & Correlation exam questions
Check the available question sets for Regression & Correlation. Use your course and exam board to confirm which practice is relevant.
Regression & Correlation exam questionsGet help with Regression & Correlation
Get a personalised explanation for Regression & Correlation from the StudyVector tutor. Ask follow-up questions and work through problems with step-by-step support.
Open tutorSave your progress in Regression & Correlation
Start a free account for low-focus question cards, feedback and Play routes across available topics. Free daily limits apply; no card required.
Continue your revision
A public question for Regression & Correlation is still being reviewed. Your course page shows the topics currently available for practice.
Continue with Regression & Correlation
Create a free account to keep your course choice and save your practice progress.
Start free low-focus cardsAlready have an account? Log in
Frequently asked questions
What is the difference between the regression line of y on x and the regression line of x on y?
The regression line of y on x is used to predict y from x, and it minimises the sum of the squared vertical distances from the data points to the line. The regression line of x on y is used to predict x from y, and it minimises the sum of the squared horizontal distances.
What is the product moment correlation coefficient?
The product moment correlation coefficient (PMCC), denoted by r, is a measure of the linear correlation between two variables. It takes a value between -1 and 1, where 1 is total positive linear correlation, -1 is total negative linear correlation, and 0 is no linear correlation.