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Random variables and expectation

Distributions, expected value, variance, linearity of expectation.

A random variable is a number attached to each outcome; its expectation is the probability-weighted average, and expectation is linear even when the variables are dependent. Picture it: the distribution as a bar chart, the mean as its balance point. Think it: expectation is an integral against the probability measure.

Àwọn ààtò àwọn ìṣàmúlò-ètò · 10 * 1/2

Arithmetic · Arithmetic

Evaluate 10 * 1/2

\frac{10}{2}

Àwọn ìṣàmúlò-ètò

10 \cdot 1 \cdot \frac{1}{2} = 5
Multiply: 10·1(1/2) = 5.

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Bii o ṣe le: Random variables and expectation

Multiply: 10·1(1/2) = 5.

Àwọn Àtòjọ-ẹ̀yàn

What is the difference between probability and statistics?

Probability goes from a known model to what the data should look like; statistics goes from data back to the model. Probability theory is the deductive half.

What does the law of large numbers promise?

That the average of many independent samples converges to the expected value. It says nothing about any single trial.

Diẹ̀ nínú Probability Theory

Sample spaces and the axioms The common distributions The law of large numbers and the central limit theorem