A W.H. Freeman Interactive e-Book
Discovering Statistics shows students that statistics can be interesting,...
[+]
Discovering Statistics shows students that statistics can be interesting, useful, and often fun. The author’s friendly, conversational writing style appeals to students, helping them understand the meaning behind statistical data. Larose’s approach to introductory statistics is for instructors who want their students to understand computational methods, while also developing statistical reasoning and critical thinking skills.
The Approach
Balanced analytical and computational coverage. The text integrates data interpretation and discovery-based methods with complete computational coverage. Through his unique and careful use of pedagogy, the author helps students to develop their ‘statistical sense’—understanding the meaning behind the numbers. Equally, the text includes integrated and comprehensive computational coverage, including step-by-step solutions within examples. Select examples include screen shots and computer output from TI-83/84, Excel, and Minitab, with key-stroke instructions located in the Step-by-Step Technology Guides at the end of sections.
Communication of Results. Discovering Statistics emphasizes how students in the real world will need to explain their results to others who have never taken a statistics course. It emphasizes the importance of keeping in mind how to interpret their results to non-specialists.
Emphasis on variability. The importance of variability in the introductory statistics curriculum cannot be overstated. Students who do not acquire an intuitive grasp of the concept of variability have a much diminished prospect of success in the course. Specifically, without a solid appreciation of how statistics may vary, there is little chance that students will be able to understand the crucial topic of sampling distributions.
Emphasis on sampling distributions and the Central Limit Theorem. Outside the Bayesian and nonparametric paradigms, all of statistical inference depends on sampling distributions. All test statistics, all confidence interval formulas depend on sampling distributions. It is vital that students have a grasp of sampling distributions and the Central Limit Theorem in order to completely understand classical statistical inference.