Quick answer: In jamovi: Regression → Correlation Matrix measures the strength and direction of a relationship (Pearson r for linear numeric data, Spearman for ordinal or non-normal), while Regression → Linear Regression goes further and models the line itself so you can predict one variable from another. Correlation asks how tightly do they move together?; regression asks what line describes it, and how well? Both report p-values — interpreted exactly as in our p-value explainer.
Table of Contents
Correlation first: the r you can defend
| r value | Reading |
|---|---|
| 0.0 – 0.3 (or −0.3 – 0.0) | Negligible to weak |
| 0.3 – 0.5 | Moderate |
| 0.5 – 0.7 | Strong |
| 0.7 – 1.0 | Very strong — check for near-duplicate variables |
Setup: Regression → Correlation Matrix → variables in, tick Pearson under Correlation Coefficients and the significance + CI options. Two rules reviewers enforce: Pearson demands roughly linear, normally-paired data (switch to Spearman when not), and r is dimensionless — height in cm vs weight in kg correlates identically to metres vs pounds. In a worked example, study-hours vs exam score gives r = 0.72, p < .001: a strong positive association, and — the sentence every methods chapter needs — correlation alone never proves cause.
Regression: from relationship to prediction
Regression → Linear Regression: move exam score to Dependent Variable, study-hours to Covariates. The output that matters: the slope (each extra study-hour predicts +6.1 points), the intercept, R² (0.52 — the model explains 52% of score variance), and the overall F-test with p < .001. Report as: “Study hours predicted exam scores, b = 6.1, R² = .52, F(1, 38) = 41.2, p < .001.” Tick Confidence interval under Model Coefficients and jamovi prints the CI for every coefficient; the residual plots under Assumption Checks (Q-Q, residual vs fitted) are the normality/equal-variance eyeball.
The R Commander route to the same model is mapped in our R Commander tests guide, and the earlier stepping stone — measuring groups rather than lines — is the t-test workflow. For one-to-one coaching on model choice, assumptions and reporting, Ampersand Academy teaches statistics with jamovi one-to-one.
Official documentation
For the full reference behind this guide, read the official jamovi site. It is the source the details above are drawn from, and the first place to check when an option or attribute name changes.
Frequently asked questions
What is the difference between correlation and regression?
Correlation measures how strongly two variables move together with a single r between -1 and 1. Regression fits a predictive line and quantifies how much the outcome changes per unit of the predictor, plus how much variance the model explains.
When should I use Spearman instead of Pearson correlation?
When data are ordinal, clearly non-normal, or the relationship is monotonic but not straight-line. Spearman ranks the data first, so it tolerates skew that would distort Pearson.
What does R-squared mean in jamovi regression output?
R-squared is the share of outcome variance the model explains. An R-squared of 0.52 means 52 percent – but a high value never proves causation, only predictive fit on the data at hand.
How do I read the slope coefficient?
The slope is the predicted change in the outcome per one-unit increase in the predictor, holding other predictors constant. Always report it with its confidence interval from the Model Coefficients table.
Can correlation be significant but weak?
Yes – with large samples even r = 0.15 turns significant. That is why effect size and practical meaning matter alongside the p-value; the p-value only rules out chance.
Last updated on · Written by Dinesh Kumar R
