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Descriptive Statistics

arithmetic mean weighted mean median mode range variance standard deviation interquartile range

Probability

sample space and events addition rule multiplication rule conditional probability Bayes theorem independence expected value law of total probability

Distributions

Bernoulli distribution binomial distribution Poisson distribution uniform distribution normal distribution exponential distribution z-score standardization central limit theorem simulation

Sampling & Estimation

simple random sampling sampling distribution of mean standard error point estimate confidence interval for mean confidence interval for proportion margin of error bootstrap estimate

Hypothesis Testing

null and alternative hypotheses p-value one-sample z test one-sample t test two-sample t test paired t test chi-square independence test Type I and Type II errors

Relationships & Models

covariance Pearson correlation Spearman rank correlation simple linear regression coefficient of determination R² residual analysis multiple regression concept confounding variables

Statistics / Distributions

central limit theorem simulation

Learn central limit theorem simulation in Statistics with a dedicated explanation, a concept-specific example, practical cautions, and a mastery check.

12 min read • Intermediate

statistics-024.py

python

import random,statistics
rng=random.Random(7)
means=[statistics.mean(rng.expovariate(1) for _ in range(40)) for _ in range(2000)]
print(round(statistics.mean(means),3),round(statistics.pstdev(means),3))

What to watch

Content source & attribution

Original Syntaxis Content · Original Syntaxis Content

Lesson quizzes

central limit theorem simulation Mastery Check →

z-score standardization

simple random sampling