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Synopsis
An introduction to statistics which features explanations, reinforced with examples and applications. Among the applications are case studies using actual research data to demonstrate important concepts and techniques. Examples incorporate more computer techniques and draw on a wide range of material from psychology, political science, criminal justice and other social sciences. The text features a treatment of regression simple regression, multiple regression and model building, with a selection of "real world" case studies and over 1,000 exercises. There are also sample print-outs for leading statistical software packages including MINITAB, SAS, and SPSS.
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From the Publisher
This book presents balanced coverage of the theory and application of statistics while enhancing students' critical thinking skills. It teaches students how to analyze data that appear in situations in the world around them--almost all of the examples and exercises are based on current, real-world applications pulled from journals, magazines, news articles, and commerce. This text shows students that knowledge of statistics is important to their own lives.
From the Back Cover
This hands-on introduction to statistics presents balanced coverage of both the theory and application of statistics, showing readers how to analyze data that appear in situations in the world around them. It features an abundance of examples and exercises--nearly all based on current, real-world applications pulled from journals, magazines, news articles, and commerce. TI-83 boxes throughout describe the keystrokes and output of TI-83 graphing calculator, where appropriate, and an Exploring Data with a Computer feature provides 5 large "real" datasets (available on disk and referenced in an Appendix) as the basis for statistical explorations using a statistical software package. Computer output from SPSS, Minitab, SAS, and ASP are integrated throughout. Statistics, Data, and Statistical Thinking. Methods for Describing Sets of Data. Probability. Discrete Random Variables. Continuous Random Variables. Sampling Distributions. Inferences Based on a Single Sample: Estimation with Confidence Intervals. Inferences Based on a Single Sample: Tests of Hypotheses. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses. Analysis of Variance: Comparing More Than Two Means. Simple Linear Regression. Multiple Regression and Model Building. Categorical Data Analysis. Nonparametric Statistics. For anyone needing a contemporary, applications-oriented introduction to statistics.
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