STA220H1 Lecture Notes - Lecture 7: Bernoulli Distribution, Probability Mass Function, Bernoulli Trial
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STA220H1 Full Course Notes
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Vocabulary: random variable, discrete, continuous, probability mass function, probability. Notation: x, x, , x , s. Discrete probability distributions: discrete uniform, bernoulli, binomial. Continuous probability distributions: probability density function, continuous. Expectation (expected value/mean) and variance (standard deviation): formula for discrete random variables, expectation and variance of linear transformations of random variables, expectation and variance of the average of independent and identically distributed random variables. Normal distributions: parameters and properties, the standard normal distribution, the normal probability table, normal quantile plots. A discrete random variable takes on a countable number of distinct values. A bernoulli trial has two possible outcomes- a success and a failure , with a constant probability of success . If we code the outcome for a bernoulli trial with numeric values 1 for success and 0 for failure , we get a bernoulli random variable. The variance measures the spread of a distribution. The square root of the variance is the standard deviation.
