Public learning track
Continuous Random Variables and the Normal Distribution
10th Grade · Math · Open Global Math
8 lessons
Goal
Analyze the normal distribution curve, compute z-scores, and calculate probabilities and percentiles using the standard normal model.
Featured Diagrams
Connecting Histograms and Empirical Data to the Normal Curve
8 lessons
0 of 8 done
- 1Connecting Histograms and Empirical Data to the Normal CurveUp nextStart
- 2Anatomy of the Normal Curve: Mean, Symmetry, and Inflection PointsPremiumNot started yet
- 3The Empirical Rule: 68-95-99.7% ApproximationsNot started yet
- 4Standardizing Values: Calculating and Interpreting Z-ScoresNot started yet
- 5Using a Z-Table to Find Probabilities Under the Normal CurveNot started yet
- 6Finding Normal Distribution Probabilities Using the Standard NormalNot started yet
- 7Inverse Normal Calculations: Finding Cutoff Values from PercentilesNot started yet
- 8Finding Unknown Mean and Standard Deviation in Normal ModelsNot started yet
Standards Covered
- OGM.11.STA.2Open Global Math Standards 2026™ · Math · Level 11 / High School 2 (Age 15) · Grade 10Calculate and interpret measures of center (mean, median, mode) and dispersion (range, interquartile range, variance, standard deviation) for population and sample parameters (μ, σ, x̄, s); construct and interpret histograms (equal/unequal class width, frequency density), cumulative frequency graphs, box-and-whisker plots, and dot plots.
- 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.
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.