Graphical displays that researchers can employ as an integral part of the data analysis process are frequently more revealing than traditional, numerical summary statistics. Providing strategies for examining data more effectively, this volume focuses on: univariate methods such as histograms, smoothed histograms, univariate scatterplots, quantile plots, box plots, dot plots. It describes bivariate methods such as scatterplot construction guidelines, jittering for overplotted points, marginal boxplots, scatterplot slicing, the Loess procedure for nonparametric scatterplot smoothing, and banking to 45 degrees for enhanced visual perception.

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William G. Jacoby is a Professor in the Department of Political Science at Michigan State University. He is also a Research Scientist at the University of Michigan, where he serves as Director of the Inter-University Consortium for Political and Social Research (ICPSR) Summer Training Program in Quantitative Methods of Social Research.
Professor Jacoby joined the MSU faculty in 2003. Previously, he held positions at the University of South Carolina, Ohio State University, and the University of Missouri. He received his Ph.D. from the University of North Carolina, Chapel Hill in 1983.
Professor Jacoby's main professional interests are mass political behavior (public opinion, political attitudes, voting behavior) and quantitative methodology (measurement theory, scaling methods, statistical graphics, modern regression). His current research focuses on citizen ideology and belief system organization, value choices and their implications for subsequent political orientations, measuring policy priorities in the American states, the implications of measurement assumptions for statistical models, and graphical strategies for data analysis.
Recently, Professor Jacoby has taught courses on public opinion, regression analysis, scaling methods, and statistical graphics.

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Statistical Graphics for Univariate and Bivariate Data

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