Public learning track
Bivariate Association: Covariance and Correlation Analysis
11th Grade · Math · Open Global Math
7 lessons
Goal
Quantify, interpret, and evaluate bivariate numerical associations using scatter plots, sample covariance, and Pearson's correlation coefficient.
Featured Diagrams
Constructing and Interpreting Scatter Plots
7 lessons
0 of 7 done
- 1Constructing and Interpreting Scatter PlotsUp nextStart
- 2Calculating and Interpreting Sample CovariancePremiumNot started yet
- 3Calculating Pearson's Correlation Coefficient (r)Not started yet
- 4Properties and Interpretations of the Correlation CoefficientNot started yet
- 5Evaluating the Impact of Outliers on CorrelationNot started yet
- 6Understanding the Coefficient of Determination (r²)Not started yet
- 7Distinguishing Correlation from CausationNot started yet
Standards Covered
- OGM.12.STA.1Open Global Math Standards 2026™ · Math · Level 12 / High School 3 (Age 16) · Grade 11Calculate and interpret measures of central tendency (mean, median, mode), position (percentiles, quartiles), dispersion (range, interquartile range, variance, standard deviation), and bivariate association (covariance, correlation); evaluate frequency distributions.
- OGM.12.STA.3Open Global Math Standards 2026™ · Math · Level 12 / High School 3 (Age 16) · Grade 11Design statistical experiments and random sampling methods; evaluate published statistical studies, media reports, and sample surveys; draw valid inferences and critique misleading statistical claims.
Curriculum Framework
11th Grade · Math · Open Global Math
1
9 lessons
Univariate Data: Center, Position, Dispersion, and Distributions
Analyze univariate quantitative data by calculating and interpreting measures of central tendency, position, dispersion, and evaluating frequency distributions.
2
7 lessons
Bivariate Association: Covariance and Correlation Analysis
Quantify, interpret, and evaluate bivariate numerical associations using scatter plots, sample covariance, and Pearson's correlation coefficient.
3
7 lessons
Combinatorics and Counting Techniques
Master counting principles, permutations, and combinations to quantify outcomes in complex multi-stage probabilistic systems.
4
7 lessons
Foundations of Probability, Sample Spaces, and Compound Events
Construct formal sample spaces and calculate probabilities for simple, complementary, mutually exclusive, and compound events.
5
7 lessons
Conditional Probability, Tree Diagrams, and Bayes' Theorem
Evaluate conditional probabilities, test for statistical independence, and compute revised probabilities using the Law of Total Probability and Bayes' Theorem.
6
7 lessons
Statistical Study Design and Random Sampling Methodologies
Design valid comparative experiments and representative random sampling protocols while mitigating bias and confounding variables.
7
7 lessons
Critiquing Media Claims, Statistical Fallacies, and Inferential Reasoning
Evaluate published reports, public opinion polling data, deceptive graphics, and common statistical fallacies to draw sound inferential conclusions.