A simple linear regression predicts one continuous outcome from one continuous predictor. The output has two layers, the overall model fit and the individual coefficient, and APA 7 wants both. People often report only the R², or only the p value, and lose marks. This guide gives you the exact format, a worked example you can copy, and the mistakes reviewers catch.
What you need before you write a single word
An APA 7 write-up of a simple linear regression needs two layers of results.
The model fit:
- R² (the proportion of variance explained).
- The F statistic with its two degrees of freedom (regression df and residual df).
- The p value for the model.
The coefficient for the predictor:
- The unstandardized slope (b).
- Its standard error (SE).
- The standardized coefficient (beta).
- The t value and its p value.
- Optionally a 95% confidence interval for b, which APA 7 encourages.
The APA 7 format template
Report both layers in running text using this pattern:
A simple linear regression predicting [outcome] from [predictor] was significant, F(df1, df2) = X.XX, p = .XXX, R² = .XX. [Predictor] significantly predicted [outcome], b = X.XX, SE = X.XX, β = .XX, t = X.XX, p = .XXX, 95% CI [X.XX, X.XX].
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Formatting rules reviewers actually check:
- Italicize F, b, SE, t, p. R² and beta are reported as shown.
- No leading zero on p, R², or beta (all bounded by 1). Keep the leading zero on b and the CI, which can exceed 1.
- Report both degrees of freedom for F, regression first: F(1, 98).
- Round to two decimals. If p is below .001, write p < .001.
A worked example
Suppose you predicted job performance from a conscientiousness score in 100 employees.
- Model: F(1, 98) = 22.45, p < .001, R² = .19
- Coefficient: b = 0.42, SE = 0.09, β = .43, t = 4.74, p < .001, 95% CI [0.24, 0.60]
Written up in APA 7, that becomes:
A simple linear regression predicting job performance from conscientiousness was significant, F(1, 98) = 22.45, p < .001, R² = .19. Conscientiousness significantly predicted job performance, b = 0.42, SE = 0.09, β = .43, t = 4.74, p < .001, 95% CI [0.24, 0.60]. For each one-point increase in conscientiousness, predicted performance rose by 0.42 points, and the model explained 19 percent of the variance in performance.
Now do it with your numbers
Paste the table straight out of your SPSS output window. You’ll get the APA 7 sentence, the effect size with its confidence interval, and a check that your p-value actually matches your test statistic.
No account. Your dataset never leaves your computer — only the summary numbers you paste are sent. If we can’t read your table we keep a copy of it so we can add support for it; nothing we keep identifies you.
The APA 7 regression table
For anything beyond one predictor, a table is expected. Even for one predictor it reads cleanly. APA tables use horizontal rules only:
| Predictor | b | SE | beta | t | p |
|---|---|---|---|---|---|
| (Intercept) | 1.85 | 0.40 | --- | 4.63 | < .001 |
| Conscientiousness | 0.42 | 0.09 | .43 | 4.74 | < .001 |
Note. N = 100. R² = .19, F(1, 98) = 22.45, p < .001.
Reporting a non-significant result
Use the same statistics; change the wording, and do not call it a trend:
Conscientiousness did not significantly predict job performance, F(1, 98) = 2.31, p = .132, R² = .02, b = 0.15, 95% CI [−0.05, 0.35].
Where to find these numbers in SPSS
- Model Summary: R Square (and Adjusted R Square).
- ANOVA: the Regression row's df and F, the Residual row's df, and Sig.
- Coefficients: B (b), Std. Error, Beta (β), t, Sig., and the 95% Confidence Interval for B if you requested it.
Mistakes reviewers catch
- Reporting only R². Give the F test for the model and the coefficient for the predictor, not just the variance explained.
- Confusing b and beta. b is the unstandardized slope in original units; beta is standardized. Report both, and remember beta drops the leading zero while b keeps it.
- A leading zero on R², beta, or p. APA drops it for values bounded by 1.
- Omitting the degrees of freedom. F needs both: F(1, 98).
- Claiming causation from an observational model. "Predicts" is not "causes."
- Writing p = .000. Report p < .001.
Before you report: did the model's assumptions hold?
Simple linear regression assumes:
- A linear relationship between predictor and outcome (check a scatterplot).
- Independent residuals.
- Homoscedasticity (constant spread of residuals).
- Approximately normal residuals.
Check these with a residual plot before trusting the model. If the relationship is clearly non-linear, a straight-line model is the wrong tool.
Let KyroStat do the write-up for you
Formatting a regression, with two layers of results and the b-versus-beta rules, is where errors creep in. KyroStat fits the model, checks the residual assumptions, and hands you the finished APA 7 sentences, a publication-ready coefficient table, the plot, and the underlying Python or R code. Upload your spreadsheet, and the report is done in seconds.
Frequently asked questions
Do I report R² or adjusted R²? For a simple regression with one predictor, R² is fine. With several predictors, report adjusted R², which penalizes adding predictors.
What is the difference between b and beta? b is the slope in the original units of the variables; beta is the standardized slope in standard-deviation units. Report both.
How do I report the degrees of freedom for the F test? Regression df first, then residual df: F(1, 98) for one predictor and 100 cases.
My p value shows as .000. What do I write? Report p < .001. A p value is never exactly zero.
Which related guides might I need? Often these: hierarchical regression, moderation analysis and confidence intervals.