How these work
Each unit is a complete lesson: a narrated walkthrough you can pause and replay, figures built to be manipulated rather than just looked at, and a short check at the end. Open one and it talks you through the section; choose Present instead and the narration stays off, which is the version to put on a projector.
Sections follow Elementary Statistics by Navidi and Monk.
Units
Sampling & Types of Data
Why a sample has to be random, what goes wrong when it isn't, and how to tell a qualitative variable from a quantitative one.
Graphical Summaries
Frequency tables, bar graphs and histograms — and how the choice of class width changes the story a histogram tells.
Measures of Center and Spread
Mean against median, and why deviations get squared before they are averaged.
Measures of Spread and Measures of Position
Standard deviation and the Empirical Rule, then z-scores and quartiles for locating a single value within a distribution.
Least Squares Regression and Correlation
What r measures, what it misses, and where the line of best fit comes from.
Basic Concepts of Probability
Sample spaces, the long-run view of probability, and the places intuition about chance goes wrong.
Random Variables
Probability distributions and expected value, ending with the airline that deliberately sells more tickets than it has seats.
The Binomial Distribution
When the binomial applies, when it does not, and how to compute with it without reaching for a table.
Continuous Distributions and the Normal Curve
How area under a density curve becomes probability, then the normal curve itself: z-scores, finding areas, and working backward from an area to a value, by calculator or TI-84.
Sampling Distributions and the Central Limit Theorem
Why sample means behave predictably (their center, their spread and their shape), and how the Central Limit Theorem lets you find probabilities for a sample mean.
The Central Limit Theorem for Proportions & Assessing Normality
The Central Limit Theorem for sample proportions and the probabilities that follow, then how to judge whether a data set is approximately normal.
Confidence Intervals for a Population Proportion
How a sample proportion becomes an interval estimate, what "95% confident" really means, and how the confidence level and the sample size change the margin of error.
Note: Section 8.3 is taught before Section 8.2, so this lesson is listed first.
Confidence Intervals for a Population Mean (σ Unknown)
Why an unknown σ calls for the t distribution, how degrees of freedom work, and how to build a confidence interval for a population mean and check that the method applies.
Basic Principles of Hypothesis Testing
How to state null and alternate hypotheses, the logic behind a test, how to word a conclusion, and the two kinds of error a test can make.
Hypothesis Tests for Proportions and Means
Complete hypothesis tests for a population proportion and a population mean: the test statistic, the P-value, the decision and the assumptions, by hand and with technology.