Predicting the unpredictable.
Calculus is finished; this is where statistics begins. A single random outcome cannot be predicted, but the pattern across many repetitions can be described precisely. These two lessons build that description, and lead directly to the inference work in Term 3.
Lesson 1
Probability Review
Tree diagrams, arrays, the addition rule, conditional probability and independence. Assumed knowledge that every distribution question depends on. Twenty practice questions, ten harder ones, and a ClassPad guide.
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Discrete Random Variables
Probability distributions, expected value as a long-run average and balance point, variance and standard deviation, and the effect of linear changes of scale. Twenty practice questions, ten harder ones, and a ClassPad guide.
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Bernoulli & Binomial Distributions
Two-outcome trials, counting successes across many independent trials, the binomial formula, and its mean and variance. Twenty practice questions, ten harder ones, and a ClassPad guide.
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