Categorical ANOVA

  • This applet is designed to teach an application of probability. This java applet works by simulating a situation where a three stage rocket is about to be launched. In order for a successful launch to occur all three stages of the rocket must successfully pass their pre-takeoff tests. By default, each stage has a 50% chance of success, however, this can be altered by dragging the bar next to each stage.
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  • In this applet, we simulate a series of hypothesis of tests for the value of the parameter p in a Bernoulli random variable. Each column of red and green marks represents a sample of 30 observations. "Successes'' are coded by green marks and "failures'' by red marks.

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  • This applet simulates rolling dice to illustrate the central limit theorem. The user can choose between 1, 2, 6, or 9 dice to roll 1, 5, 20, or 100 times. The distribution is graphically displayed. This applet needs to be resized for optimal viewing.

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  • This page will perform an analysis of variance for the situation where there are three independent variables, A, B, and C, each with two levels. The user may enter data directly or copy and paste from a spreadsheet or other application.

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  • This page will compute the One-Way ANOVA for up to five samples. The design can be either for independent samples or correlated samples (repeated measures or randomized blocks). This page will also perform pair-wise comparisons of sample means via the Tukey HSD test

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  • This page will perform a two-way factorial analysis of variance for designs in which there are 2-4 levels of each of two variables, A and B, with each subject measured under each of the AxB combinations.

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  • This page will perform a two-way factorial analysis of variance for designs in which there are 2-4 randomized blocks of matched subjects, with 2-4 repeated measures for each subject.

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  • This page will compute the Two-Way Factorial ANOVA for Independent Samples, for up to four rows by four columns. This page will also calculate the critical values of Tukey's HSD for purposes of post-ANOVA comparisons.

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  • For n = 50 to 400, in steps of size 5, this program computes and displays (1) the exact probability P(|A_n - p| >= epsilon), where A_n is the average outcome of n Bernoulli trials with probability p of success, and (2) the Chebyshev estimate p(1-p)/(n(epsilon^2)) for this probability. You can specify p and epsilon.
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  • These lecture notes are composed of nearly 180 PowerPoint slides that have been coverted to a pdf file (6 per page) on Biomedical Imaging. The following topics are outlined: Vocabulary, Displaying Data, Central Tendency and Variability, Normal Z-scores, Standardized Distribution, Probability, Samples & Sampling Error, Type I and Type II Errors, Power of a Test, Hypothesis Testing, One Sample Tests, Two Independent Sample Tests, Two Dependent Sample Tests & Estimation, Correlation and Regression Techniques, Non-Parametric Statistical Tests, Applications of Central Limit Theorem, Law of Large Numbers, Design of Studies and Experiments, Fisher's F-Test, Analysis Of Variance(ANOVA), Principle Component Analysis (PCA), Chi-Square Goodness-of-fit test, Multiple Linear Regression, General Linear Model, Bootstrapping and Resampling.
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