STAT 101 · Fall Term

Course Chapters

Nine chapters from descriptive statistics through probability, distributions and statistical inference. All lectures available in the Study Lab.

✓ Lecture 1Chapters 0–1

Welcome to Statistics

What is statistics, three stages (Design, Description, Inference), population vs sample, types of variables, graphs for categorical data.

✓ Lecture 2Chapter 2

Describing Data with Numerical Measures

Mean, median, mode, variance, standard deviation, empirical rule, quartiles, IQR, boxplots.

✓ Lecture 3Chapter 4

Probability and Probability Distributions

Experiments, sample spaces, probability rules, conditional probability, independence, random variables.

✓ Lecture 4Chapter 5

Discrete Probability Distributions

Expected value, Bernoulli, Binomial and Poisson distributions — formulas, means, variances and applications.

✓ Lecture 5Chapter 6

The Normal Probability Distribution

Properties of the normal curve, Z-scores, standard normal table, finding probabilities and percentiles.

✓ Lecture 6Chapter 7

Sampling Distributions

Sampling distribution of x-bar, standard error, Central Limit Theorem, sampling distribution of p-hat.

✓ Lecture 7Chapter 8

Large-Sample Estimation

Point estimation, confidence intervals for μ and p, margin of error, sample size determination.

✓ Lecture 8Chapter 9

Large-Sample Tests of Hypotheses

H₀ and Hₐ, Type I and II errors, z-tests for μ and p, p-values, four-step procedure.

✓ Lecture 9Chapter 10

Inference from Small Samples

t-distribution, degrees of freedom, small-sample CIs and t-tests for μ.