Grade 12 · printable course

Statistics and Data Science: An Introduction to Statistical Reasoning (Part 2)

Grade 12, Units 5-8 · 324 pages · 37 lessons · PDF, print at home

A full-year, non-calculus statistics course designed as the standard fourth-year alternative to Calculus. Students learn to summarize and visualize real data, design studies that can support causal claims, model relationships between variables, and use probability to quantify uncertainty in decisions. Every major technique is computed by hand on small data sets before being computed in a free spreadsheet on real, downloadable public data sets (U.S. Census/ACS, CDC BRFSS, FiveThirtyEight, World Bank, General Social Survey). The course treats "what does this result NOT license you to claim" as a first-class learning objective, not an afterthought, and ends with students designing, executing, analyzing, and reporting an original study. This volume holds units 5 to 8: Modeling Randomness: Binomial and Normal Distributions; Sampling Distributions: The Hinge of Inference; Inference for One and Two Groups; Extending Inference and the Capstone Study.

Delivered by email within minutes as a PDF stamped with your name. Print any part as often as you need for the children in your household; the link never expires.

$21.99

one household licence

What is inside

Every unit and lesson in this book, in order. The same lessons are taught online at lettersanddevotion.com, so a family can move between paper and screen.

Statistics and Data Science: An Introduction to Statistical Reasoning

37 lessons · try a lesson online free

Unit 5: Modeling Randomness: Binomial and Normal Distributions10 lessons
  1. 37.The four defining conditions of a binomial setting
  2. 38.The binomial probability formula P(X=k) = C(n,k)p^k(1-p)^(n-k)
  3. 39.Mean and standard deviation of a binomial distribution
  4. 40.The emergence of normal shape from repeated independent binomial trials (informal Central Limit intuition)
  5. 41.The 68-95-99.7 empirical rule for the normal distribution
  6. 42.The z-score formula z = (x - μ)/σ applied under a continuous normal model
  7. 43.Cumulative normal proportions via table lookup and NORM.DIST/NORM.INV
  8. 44.Appropriateness of the normal model as a fit for a real distribution
  9. 45.Discrimination between binomial-appropriate, normal-appropriate, and neither-appropriate scenarios
  10. 46.The applied use and limits of the normal model as a decision tool
Unit 6: Sampling Distributions: The Hinge of Inference9 lessons
  1. 47.The sampling distribution of the sample mean for a small finite population
  2. 48.The distinction between a population distribution and a sampling distribution of a statistic
  3. 49.The emerging shape/center/spread pattern of a simulated sampling distribution as trials and sample size vary
  4. 50.The Central Limit Theorem's claim and its conditions of applicability
  5. 51.The standard error formulas for a sample mean and a sample proportion
  6. 52.The applicability conditions of the Central Limit Theorem to a real, previously unseen sampling scenario
  7. 53.The distinction between bias and variability as two independent properties of a sampling method
  8. 54.Classification of a real-world sampling flaw as a bias problem, a variability problem, or both
  9. 55.A spreadsheet-based empirical sampling distribution built from a real dataset
Unit 7: Inference for One and Two Groups10 lessons
  1. 56.Null and alternative hypotheses for a one-sample claim about a proportion or mean
  2. 57.One-sample confidence interval for a proportion and for a mean
  3. 58.The relationship between margin of error and confidence level, sample size, and variability
  4. 59.The correct long-run-procedure interpretation of a confidence interval, versus the probability-of-parameter misinterpretation
  5. 60.The full one- and two-sample hypothesis-testing procedure
  6. 61.Selection among one-sample/two-sample, proportion/mean inference procedures
  7. 62.The Type I/Type II error tradeoff
  8. 63.Common misinterpretations of p-values and confidence intervals
  9. 64.A real two-group comparison from a public dataset, including significance, practical importance, and causal limits
  10. 65.The general test-statistic formula structure shared across one-sample, two-sample, t- and z- procedures
Unit 8: Extending Inference and the Capstone Study8 lessons
  1. 66.The chi-square test statistic for independence, computed from observed and expected counts in a two-way table
  2. 67.The distinction between chi-square test of independence and chi-square goodness-of-fit
  3. 68.A decision framework for choosing among t-tests, two-proportion z-tests, chi-square tests, and confidence intervals
  4. 69.The gap between a statistically significant finding and a licensed causal or practical claim
  5. 70.A study design plan including sampling/experimental method and bias safeguard
  6. 71.The student's own selected inference procedure applied to their own collected or retrieved dataset
  7. 72.The specific limitations of the student's own study design and conclusion
  8. 73.The degrees-of-freedom rule for a chi-square test on a two-way table

More for Grade 12

Everything for Grade 12 · All printable books