Regression Analysis: How to Run It Online and Report It in APA7
Regression models an outcome from one or more predictors. The type of outcome decides the model: linear regression for a continuous outcome, logistic regression for a yes/no outcome.
What is Regression Analysis?
Regression models an outcome from one or more predictors. The type of outcome decides the model: linear regression for a continuous outcome, logistic regression for a yes/no outcome.
When to use it
Use it to predict an outcome, to estimate the unique contribution of each predictor while holding the others constant, or to adjust a relationship for covariates.
Example question: How well do study hours and sleep predict exam scores?
Preparing your data
One row per participant: one column for the outcome and one column for each predictor. Categorical predictors need a clear coding (for example 0 and 1).
Example dataset (simulated; first rows of 100): id, study_hours, sleep_hours (predictors), exam_score (outcome, 0 to 100).
| id | study_hours | sleep_hours | exam_score |
|---|---|---|---|
| 1.0 | 14.7 | 5.4 | 79.0 |
| 2.0 | 12.1 | 9.0 | 70.0 |
| 3.0 | 8.9 | 6.0 | 79.0 |
| 4.0 | 15.8 | 5.1 | 83.0 |
| 5.0 | 14.8 | 7.3 | 78.0 |
| 6.0 | 7.8 | 7.3 | 59.0 |
Download the full example CSV to practise. If the tool asks for a different layout, follow the instructions on its page.
How to run it on StatFas
- Open the Regression Analysis tool. You do not need an account to run an analysis.
- Enter your data or upload your file, laid out as described in Preparing your data.
- Select your variables. Choose the outcome (dependent) variable and one or more predictors (independent variables).
- Choose any options the tool offers (for example the confidence level, a post hoc test or the error bar type) and run the analysis.
- Read the results on the page. Check the assumptions first, then the test statistic, the p-value and the effect size.
- To save your results, log in or create a free account. Every analysis stays free; the account is only needed to download.
Assumptions and checks
- Match the model to the outcome: continuous outcome → linear; binary outcome → logistic.
- Observations are independent.
- Do not enter predictors that are almost duplicates of each other (multicollinearity).
- Have enough cases: a common rule of thumb is at least 10–20 cases per predictor in linear regression and at least 10 events per predictor in logistic regression.
Key numbers to look at
- Model fit: R2 and the overall F test (linear) or the likelihood-ratio χ² (logistic).
- Coefficients with standard errors, p-values and 95% confidence intervals.
- Standardised coefficients (β) or odds ratios to compare predictors.
- Residual checks to see whether the assumptions hold.
Effect size
R2 (variance explained) for linear models; odds ratios for logistic models; β or b with confidence intervals for individual predictors.
Benchmarks such as small, medium and large are conventions (Cohen, 1988), not rules. Interpret size in the context of your field.
Reporting Regression Analysis in APA7 style
Template
Worked example (simulated data)
The numbers below were computed from the example CSV above, so you can reproduce them. They illustrate the format and are not real findings.
| Predictor | b | SE | β | 95% CI | p |
|---|---|---|---|---|---|
| Intercept | 48.46 | 4.79 | — | [38.95, 57.97] | < .001 |
| study hours | 1.67 | 0.19 | .66 | [1.29, 2.05] | < .001 |
| sleep hours | 0.45 | 0.59 | .06 | [−0.73, 1.62] | .454 |
APA7 reminders: italicise statistical symbols (M, SD, t, F, p, r), report exact p-values to two or three decimals (use p < .001 for very small values), omit the leading zero for values that cannot exceed 1 (.45, not 0.45), and put degrees of freedom in parentheses. See the APA7 cheat sheet.
Common mistakes
- Choosing linear regression for a binary outcome.
- Interpreting regression coefficients as causal effects without a causal design.
- Adding many predictors to a small sample (overfitting).
Related analyses and alternatives
- Linear Regression: For a continuous outcome.
- Logistic Regression: For a yes/no outcome.
- Mediation: To test whether a variable carries an effect.
Not sure which analysis you need? Use the test chooser.
Frequently asked questions
What is the difference between correlation and regression?
Correlation describes a symmetric association. Regression models one variable as an outcome of others and lets you control for additional variables and predict new values.
How many predictors can I include?
It depends on your sample size and effect sizes. Avoid long lists of predictors unless you have a large sample or a prior power analysis.
References
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
This guide is for learning and for preparing results for your own reports. You remain responsible for choosing an appropriate analysis, checking its assumptions and interpreting the results. Follow the formatting rules of your journal or institution where they differ from APA7.