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glenberg learning from data intr stat reasoning using jasp 4ed 2024

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Textbook in PDF format This fully updated fourth edition explores the foundations of statistical reasoning, focusing on how to interpret psychological data and statistical results. This edition includes three important new features. First, the book is closely integrated with the free statistical analysis program JASP. Thus, students learn how to use JASP to help with tasks such as constructing grouped frequency distributions, making violin plots, conducting inferential statistical tests, and creating confidence intervals. Second, reflecting the growing use of Bayesian analyses in the professional literature, this edition includes a chapter with an introduction to Bayesian statistics (also using JASP). Third, the revised text incorporates adjunct questions, that is, questions that challenge the student’s understanding, after each major section. Cognitive psychology has demonstrated how adjunct questions and related techniques such as self-explanation can greatly improve comprehension. Additional key features of the book include: A user-friendly approach, with focused attention to explaining the more difficult concepts and the logic behind them. End of chapter tables summarize the hypothesis testing procedures introduced, and exercises support information recall and application. The consistent use of a six-step procedure for all hypothesis tests that captures the logic of statistical inference. Multiple examples of each of the major inferential statistical tests. Boxed media reports illustrate key concepts and their relevance to real-world issues. A focus on power, with a separate chapter, and power analysis procedures in each chapter. With comprehensive digital resources, including large data sets integrated throughout the textbook, and files for conducting analysis in JASP, this is an essential text for undergraduate or beginning graduate statistics courses in psychology, education, and other applied social and health sciences. Preface Why statistics? Frequency distributions and percentiles Central tendency and variability z scores and normal distributions Overview of inferential statistics Probability Sampling distributions Logic of hypothesis testing Power Logic of parameter estimation Inferences about population proportions using the z statistic Inferences about μ when σ is unknown: The single-sample t test Comparing two population means: Independent samples Random sampling, random assignment, and causality Comparing two populations: Dependent samples Comparing more than two population means: Independent samples One-factor ANOVA for dependent samples Introduction to factorial designs Describing linear relationships: Regression Measuring the strength of linear relationships: Correlation Inferences from nominal data: The χ2 statistic Introduction to Bayesian statistics Glossary of symbols Tables Appendix A Appendix B Answers to selected exercises Index

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Glenberg A. Learning From Data. An Intr. to Stat Reasoning using JASP 4ed 2024.pdf 37.5 MB

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glenberg learning from data intr stat reasoning using jasp 4ed 2024

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