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
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
Relating coefficients to probability
Hypothesis tests (P values) for β1
Analysis checklist: Simple logistic regression
Error messages from simple logistic regression
Key concepts: Deming regression
Analysis checklist: Deming regression
