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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 DataYou are here
  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 ObservationsNot started yet
  9. Distinguishing Correlation from Causation and Lurking VariablesNot started yet
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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.