Categorical ANOVA

  • This module discusses the probability of an event and relative frequency. The applet shows how empirical probability converges to theoretical probability as the sample size increases. The follow-up example includes an applet that simulates drawing differently colored balls from an urn.
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  • It is easy to lie with statistics, but it is easier to lie without them. Quote attriuted to Frederick Mosteller (1916 - 2006)

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  • This tutorial exposes students to conducting multiple comparisons in SPSS. This html based tutorial provides extensive screen shots and an example data set. Topics covered in the tutorial include one way ANOVA, preplanned contrasts, Bonferroni, Post Hoc Tukey's HSD, and Scheffe's multiple contrasts.
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  • The data presented in this article refer to the reliability of ball bearings in manufacturing. Rather than exploring the data to obtain a multiple linear regression solution, a theoretically derived equation is given and the data is used to test it. Key Words: Failure times; Percentiles; Weighted least squares.
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  • The dataset presented in this article contains information on the prices and weights of diamond stones; it can be used to illustrate simple linear regression and encourage students to think critically about the appropriateness of a model. The data is in .dat format. Key Words: Extrapolation; Interpretation of intercept; Model-building; Transformations.
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  • This article presents a dataset containing the 1970 draft lottery information, which illustrates a nonrandom procedure. Key Words: Chi-square; Correlation; Exploratory data analysis; Graphical analysis; Randomness; Regression.
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  • This article describes a dataset containing monthly household electric billing charges for ten years. The data can be used to illustrate graphing, descriptive statistics, correlation, seasonal decomposition, a variety of smoothing methods, ARIMA models, forecasting, and multiple regression.
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  • This article presents a dataset based on an industrial case study using design of experiments. It can be used to discuss sample size, power, statistical significance, interaction terms, Type I and Type II errors, the role and importance of the error term, design of experiments, and analysis of variance.
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  • This article describes a dataset containing Major League Baseball data from seasons 1969 through 2000 and illustrates how this data can be used as a course long project covering basic data management, the use of exploratory data analysis to "clean" data, and construction of regression models. The data is in .dat format.
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  • This article describes a dataset on the 2000 National Football League (NFL) regular season and the exploratory data analyses performed. Key Words: Multivariate analysis; Summary ranking measures
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