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Regression

  • As discussed, the murder rates for Blacks in the United States are substantially higher than those for Whites, with Latino murder rates falling in the middle. These differences have existed throughout the 20th and into the 21st century and, with few exceptions, are found in different sections of the United States. Although biological and genetic explanations for racial differences in crime rates, including murder, have been discredited and are no longer accepted by most criminologists, both cultural and structural theories are widespread in the literature on crime and violence. It is also important to remember that Latino is an ethnic rather than a racial classification. The point of this exercise is to examine differences in selected structural positions of Blacks, Whites and Latinos in the United States that may help explain long-standing differences in their murder rates.
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  • This applet shows a scatterplot of height versus foot length. Users can add or delete points and then guess the regression line by clicking "Your Line" and moving the blue regression line. By clicking "Regression Line" users can see the actual regression line. The applet also shows the correlation and R-square for the data as well as the residuals and squared residuals for the guessed regression line and the actual regression line.
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  • This page provides links to distribution calculators, conceptual demonstration applets, statistical tables, online data analysis packages, function and image-processing tools, and other online computing resources. Key Words: Binomial; Normal; Exponential; Chi-Square; Geometric; Hypergeometric; Negative Binomial; Poisson; Student's T; F-Distribution; Wilcoxon Rank-Sum; Central Limit Theorem; Regression; Normal Approximation to Poisson; Confidence Intervals; Hypothesis Tests; Power; Sample-Size; ANOVA; Galton's Board; Function Plots; Edge Detection; Image Warping & Stretching; Polynomial Model Fitting; Wilcoxon-Mann-Whitney Statistic.
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  • This applet allows students to explore three methods for measuring "goodness of fit" of a linear model. Users can manipulate both the data and the regression line to see changes in the square error, the absolute error, and the shortest distance from the data point to the regression line.
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  • This page provides a table for selecting an appropriate statistical method based on type of data and what information is desired from the data. It also compares parametric and nonparametric tests, one-sided and two-sided p-values, paired and unpaired tests, Fisher's test and the Chi-square test, and regression and correlation. It comes from Chapter 37 of the textbook, "Intuitive Biostatistics".
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  • Forecasting is very difficult, especially about the future. A quote of business economist Edgar R. Fiedler (1929 - 2003) found in "Across the Board", the magazine of The Conference Board, Inc. (June, 1977). The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.
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  • A quick pun about the "log scale" by Bruce White
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  • This article presents a dataset containing actual monthly data on computer usage in Best Buy stores from August 1996 to July 2000. This dataset can be used to illustrate time-series forecasting, causal forecasting, simple linear regression, unequal error variances, and variable transformation. Key Words: Model-building; Seasonal Variation.
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  • This article describes Galileo's data on falling bodies and projectiles and its use as an aid in teaching polynomial and nonlinear regression. Key Words: Independent and dependent variables; Graphical analysis.
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  • This article describes a dataset of days in office of US Presidents with outliers that are not mistakes or unusually high or low observations. The data illustrate that outliers need not be errors but could be particularly interesting cases and that data displays may differ in their ability to reveal interesting data structure. Key Words: Inliers; Interpretation in context.
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