By James T. McClave, Terry T. Sincich
Descripción: For courses in introductory statistics. Classic, yet contemporary; theoretical, yet applied—McClave & Sincich’s Statistics gives you the best of both worlds. This text offers a trusted, comprehensive introduction to statistics that emphasises inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data. This new edition is extensively revised with an eye on clearer, more concise language throughout the text and in the exercises. Ideal for one- or two-semester courses in introductory statistics, this text assumes a mathematical background of basic algebra. Flexibility is built in for instructors who teach a more advanced course, with optional footnotes about calculus and the underlying theory. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
Contenido: Chapter 1 Statistics, Data, and Statistical Thinking
Chapter 2 Methods for Describing Sets of Data
Chapter 3 Probability
Chapter 4 Discrete Random Variables
Chapter 5 Continuous Random Variables
Chapter 6 Sampling Distributions
Chapter 7 Inferences Based on a Single Sample: Estimation with Confidence Intervals
Chapter 8 Inferences Based on a Single Sample: Tests of Hypothesis
Chapter 9 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
Chapter 10 Analysis of Variance: Comparing More than Two Means
Chapter 12 Multiple Regression and Model Building
Chapter 13 Categorical Data Analysis
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