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  • This lesson describes bootstrapping in the context of a statistics class for psychology students.
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  • This excerpt from Engineering Statistics Handbook gives a definition for and examples of outliers. A sub-page also discusses Grubbs' Test for Outliers
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  • A short discussion of what outliers are and their helpfulness in analyzing data.
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  • This text document lists detailed learning objectives for introductory statistics courses. Learning objectives are brief, clear statements of what learners will be able to perform at the end of a course. These objectives were developed for a one semester general education introductory statistics course. The objectives cover the broad categories of Graphics, Summary Statistics, The Normal Distribution, Correlation and Scatterplots, Introduction to Regression, Two way Tables, Data Collection and Surveys, Basic Probability, Sampling Distributions, Confidence Intervals, Tests of Hypothesis, and T-distributions.
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  • Users can select from detailed tables and geographical comparison tables to generate data from the 2000 Census.
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  • This webpage uses the criminal trials in the US Justice system to illustrate hypothesis testing, type I error, and type II error. An applet allows the user to examine the probability of type I errors and type II errors under various conditions. An applet allows users to visualize p-values and the power of a test. Keywords: type I error, type II error, type one error, type two error, type 1 error, type 2 error
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  • This video is an example of what is known in psychology as selective attention. When a person is instructed to only focus on the number of times a ball is passed between players wearing a white shirt it is sometimes difficult to see what else is going on.
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  • This is an example of "growing" a decision tree to analyze two possible outcomes. The tree's branches examine the two possible conditions of employee drug use with corresponding probabilities. This example looks at the final outcome probabilities of being correctly and incorrectly identified versus testing accuracy.
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  • This page explores Benford's Law: For naturally occurring data, the digits 1 through 9 do not have equal probability of being the first significant digit in a number; the digit 1 has greater odds of being the first significant digit than the others. This law can be used to catch tax fraud because truly random numbers used by embezzlers do not meet this condition.
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  • This random number generator produces a data table with up to 10 columns and up to 2500 rows. For random integers, users must specify the data range. For data from a Normal (Gaussian) distribution, users specify mean and standard deviation.

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