Elementary Probability

  • This lecture example discusses calculating chance with probabilities (a ratio of occurrence to the whole) or odds (a ratio of occurrence to nonoccurrence). It presents a clinical example of measuring the chance of initiating breastfeeding among 1000 new mothers. Tables are provided in pdf format.
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  • This page discusses the proper procedures for multiple comparison tests and reasons behind them.
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  • This text based website provides an explanation of some coincidences that are often discussed. It gives an explanation of the birthday problem along with a graphic display of the probability of birthday matches vs. the number of people included. It also discussess other popular coincidences such as the similarities between John F. Kennedy and Abraham Lincoln. It goes on to discuss steaks of heads and tails along with random features of stocks and the stock market prices.
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  • This tutorial explains the theory and use of two-way ANOVA and demonstrates it with an example on final exam scores. Data is given as well as SPSS and Minitab code.
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  • This tutorial introduces the basic concepts of probability using various examples. Topics include interpreting probability, calibration experiments, interpreting odds, sample space, basic rules, equally likely outcomes, constructing probability tables, unions and complements, mean, and two-way probability tables. A link to activities is also given.
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  • This page will calculate the factorial of any number.
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  • This page uses Bayes' Theorem to calculate the probability of a hypothesis given a datum. An example about cancer is given to help users understand Bayes' Theorem and the calculator. Key Word: Conditional Probability.
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  • This page gives a history of notation and symbols and who developed them for combinatorial analysis, the normal distribution, probability, and statistics. Quotes from the first papers to use these symbols are also given.
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  • This tutorial opens with a survey on polling. Upon completing the survey, students are taken through an election example which uses polling to explain random sampling, bias, margin of error, and confidence intervals.
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  • Everyday we have specific routines we engage in. Many of these routines are tailored to preventing us from becoming victims of crime. We do things like lock our doors, watch where we walk at night, or avoid walking alone. We take these actions because at some level we are afraid of the possibility of being a victim of crime. Although we may not consciously think about it, these routines may be influenced by a variety of factors. What factors might make some individuals more afraid than others?

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