Context
Correlation output
A pupil investigates the relationship between weekly revision hours and assessment score.
Simulated output
> cor(study$hours, study$score) [1] 0.82
Which conclusion is best?
Choose an option, then check the feedback.
Back to RStudio and Project Skills
Higher Applications of Mathematics
Scatter plots and correlation in RStudio
Graphing and measuring relationships.
RStudio reference
Check commands and output meanings.
Project support
Plan reports, conclusions and limitations.
Mixed quiz
Review RStudio and project skills.
Before you start
- Know the statistical question you are trying to answer.
- Check that variables are named clearly and measured in suitable units.
- Be ready to write an interpretation, not just copy RStudio output.
Method chooser
Which RStudio method do I use?
Do you need to load a data file?
Use read.csv() and check the data frame.
Do you need averages or spread?
Use mean(), median(), sd() and summary().
Do you need to compare distributions?
Use boxplot() or hist().
Do you need to check a relationship between two variables?
Use plot() and cor().
Do you need a straight-line model?
Use lm() and interpret the gradient.
Are you estimating a population mean?
Use a confidence interval from t.test().
Are you testing a mean or paired data?
Use t.test(), with paired = TRUE for matched pairs.
Are you testing two proportions?
Use prop.test().
Are you writing the project conclusion?
State evidence, context and limitations clearly.
Key idea
- This topic focuses on examining relationships between two numerical variables. In Higher Applications, RStudio is used as a practical tool to calculate, graph and test ideas from data.
- A strong answer shows what command was used, what output was produced, and what that output means in context. Use careful language such as 'This suggests...' or 'There is evidence to suggest...'.
Key commands and skills
- plot(x, y); cor(x, y)
- names(data)
- summary(data)
- Use comments in scripts with # to explain your steps.
Technology output practice
Output interpretation preview
Read the simulated output, pick out the key value, then turn it into a written conclusion. This is a learning preview, not a real RStudio environment.
Worked examples
Walkthrough 1
Run the command
A pupil is using RStudio for examining relationships between two numerical variables with a small school-friendly data set.
- Load or identify the data frame.
- Check the exact column names with names(data).
- Run the key command: plot(x, y); cor(x, y)
The output should be checked against the variables and the original question.
Walkthrough 2
Read the output
RStudio has produced numerical or graphical output.
- Find the key value, graph feature or p-value.
- Check the unit and variable name.
- Avoid copying every line of output into the conclusion.
Correlation describes strength and direction, but does not prove causation.
Walkthrough 3
Write the interpretation
The result must be used in a project conclusion.
- Start with a cautious phrase such as 'This suggests...'.
- Refer to the context and variables.
- Mention a limitation if the data set is small, biased or observational.
The conclusion should be clear, cautious and linked to evidence.
Watch out
- Misspelling a data frame or column name.
- Forgetting brackets or quotation marks in a command.
- Copying output without explaining what it means.
- Claiming causation from correlation.
- Using strong language such as 'proves' when the data only suggests evidence.
Statistics connection
Related Statistics topics
Next step
Move into practice
Use the learning notes to read output tables carefully, then try varied summary, correlation, regression and test-output interpretation.