Predict outcomes
Estimate pocket depth from age, smoking status, and plaque levels.
Dental research
Identify which risk factors truly influence implant failure or caries.
Report results
Present coefficients, odds ratios, and confidence intervals in papers.
Click a card to learn and try it.
Click the chart to add data points, then fit a regression line. Try preset dental datasets.
Adjust the slider to see how smoking affects implant failure probability.
Watch a regression study unfold step-by-step: recruit patients, collect data, fit the model, interpret results.
Linear Regression
Report: B (95% CI), p-value
e.g. B=0.04 (0.02-0.06), p=0.001
Logistic Regression
Report: OR (95% CI), p-value
e.g. OR=2.5 (1.3-4.8), p=0.006
Model Summary
Report: R², Adjusted R², F-stat
e.g. R²=0.42, F(3,96)=23.1, p<0.001
| Model Type | Coefficient | 95% CI | p-value | Interpretation |
|---|---|---|---|---|
| Linear Age → CAL | B = 0.04 | 0.02 – 0.06 | 0.001 | Each year of age increases CAL by 0.04 mm |
| Multiple Age + Smoking → PD | Bage=0.03 Bsmoke=1.2 | 0.01–0.05 0.6–1.8 | 0.004 <0.001 | Adjusted for confounders |
| Logistic Smoking → Implant Failure | OR = 2.5 | 1.3 – 4.8 | 0.006 | Smokers have 2.5x odds of implant failure |
"Multiple linear regression was performed to examine the effect of age, smoking status, and plaque index on clinical attachment loss. The model was statistically significant, F(3, 96) = 23.1, p < 0.001, R² = 0.42. Age (B = 0.04, 95% CI: 0.02–0.06, p = 0.001) and smoking (B = 1.2, 95% CI: 0.6–1.8, p < 0.001) were significant predictors, while plaque index was not (B = 0.15, 95% CI: -0.1–0.4, p = 0.22)."
Continuous outcome. Y = B0 + B1*X. Report B with 95% CI and p-value. Check linearity, normality of residuals.
Multiple predictors. Adjusts for confounders. Check multicollinearity (VIF < 10). Report adjusted R².
Binary outcome (yes/no). Reports odds ratios. Use when predicting implant failure, disease presence.
OR = 1 (no effect), OR > 1 (increased risk), OR < 1 (protective). Always report with 95% CI.
Independence, linearity, no multicollinearity, adequate sample size. EPV >= 10 for logistic regression.
R² = proportion of variance explained. Higher is better. Adjusted R² penalizes adding useless predictors.