Introducing the concept of the probability of an event. Also covers set operations and the sample-point method.

A brief introduction to what we're going to discuss in later chapters.

Introducing conditional probability and independence of events. Bayes' rule comes in as well.

Some basic tools such as the permutation test and the binomial test. We also introduce order statistics and ranks, which will come in handy in later chapters.

The probability mass function, cumulative distribution function, expectation and variance for random variables.

The Wilcoxin signed rank test explained.

We introduce the binomial (Bernoulli), geometric and Poisson probability distributions and their properties. The properties include their expectations, variances and moment generating functions.

Explore whether the sample is consistent with a specified distribution at the population level. Kolmogorov's test, Lilliefors test and Shapiro-Wilk test are introduced, as well as tests for runs or trends.

The probability density function, cumulative distribution function, expectation and variance for a continuous random variable.

An obvious extension of the one-sample procedures.

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