Simple linear regression
Purpose
Use OLS to analyze conditional associations with a continuous outcome.
Data and assumptions
Use one independent observation per row. Declare categorical factors and their reference levels. Each model uses complete cases. Check linearity, collinearity, residuals and influential observations.
Configure this method
Set a continuous Y and one explanatory factor X. Declare categorical X in analysis settings and select its reference. One factor may produce several dummy columns.
Variables
| Control | Input |
|---|---|
| Outcome Y | Select one variable |
| Predictors X | Select variables |
| Treat as categorical | Select variables |
| Reference categories | Choose categorical reference levels |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Standard error type | Conventional standard errors, HC1 robust standard errors, HC3 robust standard errors | standard |
Analysis parameters remain visible in the main configuration area. Advanced formatting contains only the table title and numeric decimals. Methods that calculate intervals also show the significance level; 0.05 corresponds to 95% intervals.
Read the results
B uses original units; beta is standardized. Confidence intervals and R-squared complement p-values. HC1/HC3 adjust standard errors but do not correct a wrong functional form or omitted-variable bias.
Workflow and exports
- Upload the dataset and choose the method from the sidebar or search.
- Select the variables above, set the design-specific options, then run the analysis.
- Inspect the paper-style result. Reconfigure from the result area, or open raw output for diagnostics and sample details.
- Rename and keep result tabs as needed. Export Word, Excel, CSV or TXT; batch export can include the selected AI interpretation. Word retains figures. AI text should be checked against the estimates.