Please enable JavaScript to view this site.

This guide is for an old version of Prism. Browse the latest version or update Prism


Simple linear and simple logistic regression with Prism

Scroll Prev Top Next More

Prism offers two forms of simple regression: simple linear regression and simple logistic regression. Although these analyses are related, we discuss them separately. To learn more about the similarities and differences of simple linear regression and simple logistic regression, read more about them in the “Principles of Regression” portion of this guide (simple linear regression, simple logistic regression).

Simple linear regression fits a straight line through your data to find the best-fit value of the slope and intercept.

Simple logistic regression estimates the probability of obtaining a “positive” outcome (when there are only two possible outcomes, such as “positive/negative”, “success/failure”, or “alive/dead”, etc.).

How to: Simple linear regression

 Finding the best-fit slope and intercept

 Interpolating from a linear standard curve

 Advice: When to fit a line with nonlinear regression

 Confidence and prediction bands (linear regression)

 Graphing tips: Simple linear regression

 Difference between linear regression and correlation

 How to fit one line to two data sets

Results of simple linear regression

 Slope and intercept

 r2, a measure of goodness-of-fit of simple linear regression

 Standard deviation of the residuals

 Is the slope significantly different than zero?

 Comparing slopes and intercepts

 Runs test following linear regression

 Analysis checklist: Simple linear regression

How to: Simple logistic regression

 Fitting a simple logistic regression model

 Example: Simple logistic regression

Results of simple logistic regresion

 Interpreting the coefficient estimates

 X at 50%

 Odds Ratios

 Relating coefficients to probability

 Hypothesis tests (P values) for β1

 Area under the ROC curve

 Goodness-of-fit metrics

 Analysis checklist: Simple logistic regression

 Error messages from simple logistic regression

Deming regression

 Key concepts: Deming regression

 How to: Deming regression

 Q&A: Deming Regression

 Analysis checklist: Deming regression


© 1995-2019 GraphPad Software, LLC. All rights reserved.