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.
Featured Diagrams
Constructing and Interpreting Scatter Plots for Bivariate Quantitative Data
9 lessons
0 of 9 done
- 1Constructing and Interpreting Scatter Plots for Bivariate Quantitative DataUp nextStart
- 2Calculating and Interpreting Pearson's Correlation CoefficientPremiumNot started yet
- 3Fitting Least-Squares Linear Regression Lines and Interpreting ParametersNot started yet
- 4Evaluating Model Adequacy Using Residuals and Residual PlotsNot started yet
- 5Fitting Quadratic and Polynomial Models to Non-Linear Bivariate DataNot started yet
- 6Linearizing Exponential Data Using Semi-Log PlotsNot started yet
- 7Linearizing Power Models Using Log-Log TransformationsNot started yet
- 8Identifying Influential Points and High Leverage ObservationsNot started yet
- 9Distinguishing Correlation from Causation and Lurking VariablesNot started yet
Curriculum Framework
12th Grade · Math · Open Global Math
1
7 lessons
Advanced Univariate Distributions and Summary Statistics
Represent, summarize, and interpret univariate datasets using graphical representations, measures of central tendency, position, and dispersion.
2
6 lessons
Data Cleaning, Outlier Analysis, and Distributional Robustness
Identify anomalies, clean missing values, and evaluate the effect of extreme values on distribution metrics and reporting choices.
3
7 lessons
Continuous Normal Distributions and Probabilistic Modeling
Fit continuous normal distributions to empirical data, compute probabilities using Z-scores, and diagnose model validity.
4
6 lessons
Bivariate Categorical Data and Contingency Tables
Summarize bivariate categorical data, calculate joint, marginal, and conditional relative frequencies, and test for independence.
5
9 lessons
Bivariate Quantitative Modeling and Regression Analysis
Fit linear, quadratic, and exponential models to bivariate data, evaluate fit via residuals and correlation, and distinguish correlation from causation.