AI Tutor

Premium Lesson

Upgrade to unlock this lesson, practice questions, and your personal tutor.

This lesson is part of a track

Public learning track

Bivariate Quantitative Modeling and Regression Analysis

12th Grade · Math · Open Global Math

9 lessons

Goal

Fit linear, quadratic, and exponential models to bivariate data, evaluate fit via residuals and correlation, and distinguish correlation from causation.

9 lessons
0 of 9 done
  1. Constructing and Interpreting Scatter Plots for Bivariate Quantitative DataUp next
  2. Calculating and Interpreting Pearson's Correlation CoefficientNot started yet
  3. Fitting Least-Squares Linear Regression Lines and Interpreting ParametersNot started yet
  4. Evaluating Model Adequacy Using Residuals and Residual PlotsNot started yet
  5. Fitting Quadratic and Polynomial Models to Non-Linear Bivariate DataNot started yet
  6. Linearizing Exponential Data Using Semi-Log PlotsNot started yet
  7. Linearizing Power Models Using Log-Log TransformationsNot started yet
  8. Identifying Influential Points and High Leverage ObservationsYou are here
  9. Distinguishing Correlation from Causation and Lurking VariablesNot started yet
View full track

Learning standards covered

GlobalOGM.13.STA.5Open Global Math Standards 2026™ · 12th Grade

Represent quantitative bivariate data on scatter plots; fit linear, quadratic, and exponential models; compute and interpret Pearson's correlation coefficient $r$, slope, and intercept; assess model adequacy using residual plots; and rigorously distinguish correlation from causation.