Filled with practical learning activities to adopt within your classroom, this book places reasoning about quantification at the core of learning and teaching statistics.
Filled with practical learning activities to adopt within your classroom, this book places reasoning about quantification at the core of learning and teaching statistics.
Produktdetails
Produktdetails
IMPACT: Interweaving Mathematics Pedagogy and Content for Teaching
Luis Saldanha is a professor of Didactics of Mathematics in the department of mathematics at l'Université du Québec à Montréal, Canada. His research focuses on mathematical thinking, specifically the development of students' statistical reasoning in relation to their engagement with instruction designed to foster their understanding of statistical concepts. Neil J. Hatfield is an assistant research professor in the Department of Statistics at Pennsylvania State University, U.S.A. His main research interests focus on cognition related to the concept of distribution; the teaching of statistics, data science, and probability; and diversity, equity, and inclusion in STEM. Egan J. Chernoff is a professor of Mathematics Education at the University of Saskatchewan, Canada. His editorial affiliations include Statistics Education Research Journal; The Mathematics Enthusiast; Mathematical Thinking and Learning; Journal of Mathematical Behavior; Canadian Journal of Science, Mathematics, and Technology Education; and more. Caterina Primi is a full professor in Psicometria at the Faculty of Psychology at the University of Florence, Italy. She is an experienced teacher of graduate, post-graduate, and PhD level courses in statistics, research methods, and psychological testing.
Inhaltsangabe
Introduction 1. Individual Cases, Attributes, and Data 2. Collections of Cases, Attributes of Collections, and Measures of Such Attributes 3. Samples, Populations, and Quantifying their Variation 4. Processes, Randomness, and Probability 5. Sampling Distributions and Statistical Inference Appendix Index
Introduction 1. Individual Cases, Attributes, and Data 2. Collections of Cases, Attributes of Collections, and Measures of Such Attributes 3. Samples, Populations, and Quantifying their Variation 4. Processes, Randomness, and Probability 5. Sampling Distributions and Statistical Inference Appendix Index
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