Public learning track
Discrete Random Variables and Binomial Probability Models
10th Grade · Math · Open Global Math
9 lessons
Goal
Define discrete probability mass functions, compute expected values and variances, and model Bernoulli/Binomial processes.
Featured Diagrams
Discrete vs. Continuous Random Variables
9 lessons
0 of 9 done
- 1Discrete vs. Continuous Random VariablesUp nextStart
- 2Discrete Probability Distributions and Mass FunctionsPremiumNot started yet
- 3Expected Value of Discrete Random VariablesNot started yet
- 4Variance and Standard Deviation of a Random VariableNot started yet
- 5Evaluating Fair Games and Financial Decisions Using Expected ValueNot started yet
- 6Characteristics of Binomial Experiments: The BINS CriteriaNot started yet
- 7Calculating Exact Binomial Probabilities Using the FormulaNot started yet
- 8Calculating Binomial Probabilities Using Cumulative Distribution FunctionsNot started yet
- 9Mean, Variance, and Standard Deviation of Binomial DistributionsNot started yet
Standards Covered
- OGM.11.STA.3Open Global Math Standards 2026™ · Math · Level 11 / High School 2 (Age 15) · Grade 10Analyze discrete random variables, probability mass functions, and expected values; analyze continuous Normal distributions (bell curve symmetry, parameters μ and σ, inflection points, empirical rule); calculate probabilities under binomial and normal models.
- OGM.11.STA.1Open Global Math Standards 2026™ · Math · Level 11 / High School 2 (Age 15) · Grade 10Calculate probabilities of single, combined, mutually exclusive, and independent events using Venn diagrams, tree diagrams, two-way tables, and sample spaces; apply the addition rule, product counting rule, permutations, combinations, and conditional probability formula; understand the Law of Large Numbers (Laplace rule) and risk analysis.
Curriculum Framework
10th Grade · Math · Open Global Math
1
8 lessons
Univariate Data: Visual Displays, Frequency Distributions, and Ogives
Construct and interpret visual representations of quantitative data including histograms, dot plots, box plots, and cumulative frequency curves.
2
7 lessons
Univariate Data: Measures of Center, Dispersion, and Linear Transformations
Calculate and interpret central tendency and spread parameters for populations and samples, analyzing the effects of linear data transformations.
3
7 lessons
Combinatorics, Sample Spaces, and Theoretical Probability
Apply the fundamental counting principle, permutations, combinations, and sample space models to compute exact probabilities.
4
7 lessons
Compound Probability, Conditional Events, and Risk Analysis
Solve multi-event probability problems using Venn diagrams, tree diagrams, two-way tables, conditional formulas, and risk metrics.
5
9 lessons
Discrete Random Variables and Binomial Probability Models
Define discrete probability mass functions, compute expected values and variances, and model Bernoulli/Binomial processes.
6
8 lessons
Continuous Random Variables and the Normal Distribution
Analyze the normal distribution curve, compute z-scores, and calculate probabilities and percentiles using the standard normal model.
7
7 lessons
Bivariate Quantitative Data: Scatter Plots and Linear Regression
Analyze bivariate relationships, fit least-squares regression lines, interpret correlation coefficients, and examine residuals.
8
7 lessons
Correlation vs. Causation, Media Literacy, and Inferential Reasoning
Critique statistical claims in media, differentiate correlation from causation, and evaluate sampling designs and inferential validity.