PSYCH 240 Lecture Notes - Lecture 5: Probability Distribution, Probability Density Function, Binomial Distribution
21 Oct 2016
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Document Summary
Random processes looking at the possible outcomes/how often they happen. Probability - the long run" proportion of times something occurs. Law of large numbers - as n increases, p gets closer and closer to the true probability (p) of the outcome. Because probability is actually a long run proportion, it behaves like a proportion: Sum of probabilities must be equal to 1. Independent - one variable occurring does not affect the probability that the other will occur. P(a b) = p(a)+p(b)-p(a b) < getting at least one. Discrete probability distribution: just like a frequency distribution but with probability bar graph. We can assign a probability to a range of outcomes. Probability density function (pdf)/density curve - probability distribution when the outcomes are continuous. Area under the curve always equals 1 y-axis changes as the x-axis changes so that the area under the curve equals 1 y-axis values have no meaning.
