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Intro to Probability Distribution

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An intro into probability distributions

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  • January 7, 2025
  • 5
  • 2024/2025
  • Class notes
  • Olivia podolak lewandowska
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sobikaaravi
Probability‬


‭outline is on the side‬

‭Skewness and Kurtosis‬
‭➔‬‭two more descriptive stats, associated w/ spread‬
‭➔‬‭skewness‬‭:‬‭how symmetrical the observations are around‬‭the mean‬
‭◆‬ ‭important because it influences the measures of central tendency and probabilistic‬
‭distributions‬
‭◆‬ ‭positive distribution/ left-skewed‬‭:‬‭the peak is towards‬‭the left and the tail is along the‬
‭right end of the curve‬
‭●‬ ‭extreme values are closer to the mean, meaning the mean is greater than the‬
‭median‬
‭●‬ ‭the mean is the better measure when looking at a positive distribution‬
‭◆‬ ‭negative distribution/ right-skewed‬‭:‬‭the peak is towards‬‭the right and the tail is along the‬
‭left end of the curve‬
‭●‬ ‭extreme values are by the peak, meaning the mean is less that the median‬
‭●‬ ‭the median is the better measure ,when looking at a negative distribution‬
‭➔‬‭kurtosis‬‭:‬‭how clustered or flat the observations are‬
‭◆‬ ‭mesokurtic‬‭:‬‭kurtosis of 0‬
‭●‬ ‭normal distribution‬
‭◆‬ ‭leptokurtic‬‭:‬‭positive kurtosis‬
‭●‬ ‭skinner than usual‬
‭◆‬ ‭platykurtic‬‭:‬‭negative kurtosis‬
‭●‬ ‭flatter than usual (think platypus w/ a flat beak)‬
‭◆‬ ‭does not affect measures of central tendency‬

‭Probability Theory‬
‭➔‬‭provides description of hidden structure that exists within an infinite/large population of‬
‭observations/outcomes‬
‭◆‬ ‭look at the structure within the chaos‬
‭➔‬‭way of defining boundaries between results of mere chance vs results not by mere chance‬
‭➔‬‭two approaches to probability‬
‭◆‬ ‭rational‬
‭◆‬ ‭empirical‬

, ‭Approach‬ ‭Source‬ ‭Priority‬ ‭Starting Point‬

‭Rational (A priori)‬ k‭ nowledge based on‬ r‭ eason > experience‬‭;‬ e‭ xpected outcomes‬
‭reason/logic‬‭, not‬ ‭raises reasoning and‬ ‭(educated guess w/ all‬
‭experience‬ ‭logic to mathematical‬ ‭all information);‬
‭precision‬ ‭expected % are‬
‭determined before the‬
‭occurrence of an‬
‭outcome‬

‭Empirical (A‬ k‭ nowledge is based on‬ ‭experience > reason‬ ‭ bserved outcome‬
o
‭posteriori)‬ ‭experience/observati‬ ‭(deduce based on‬
‭on‬‭rather than reason‬ ‭gathered info through‬
‭alone‬ ‭experience); expected‬
‭% determined on‬
‭experience; additional‬
‭observations could‬
‭lead to updating‬
‭current expectations‬



‭Basic Definitions‬
‭➔‬‭event/outcome‬‭:‬‭the occurrence of something that is‬‭observed‬
‭➔‬‭probability‬‭:‬‭the likelihood of an event happening‬
‭◆‬ ‭expressed in proportions (0-1) or percentages (0-100%)‬
‭◆‬ ‭P(event) = number of event outcomes/total number of outcomes‬
‭➔‬‭complement rule‬‭:‬‭P(A) + P’(A) = 1‬‭OR‬‭100%‬
‭➔‬‭example‬‭of‬‭A priori/rational approach‬
‭◆‬ ‭using a 6-sided die, what is the probability of rolling a 6?‬
‭●‬ ‭(possible outcomes: 1,2,3,4,5, and 6)‬
‭◆‬ ‭P(6) = number of 6s on a die / total number of numbers on a die‬
‭●‬ ‭P(6) = 1 /6‬
‭●‬ ‭0.1666667‬
‭➔‬‭example‬‭of‬‭A posteriori/empirical approach‬
‭◆‬ ‭you have a bag of marbles. you don’t know how many red and black marbles are inside.‬
‭◆‬ ‭Pick 1: you pick out a marble at random, it’s red. observed relative frequency between‬
‭red and black marbles?‬
‭●‬ ‭red = 100% and black = 0%‬
‭○‬ ‭you haven’t witness a black marble from the bag yet to have any reason for‬
‭believing a black marble exists in the bag‬

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