[PDF] Leveled Texts For Mathematics Data Analysis And Probability eBook
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With a focus on data analysis and probability, a guide to using leveled texts to differentiate instruction in mathematics offers fifteen different topics with high-interest text written at four different reading levels, accompanied by matching visuals and practice problems.
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
With a focus on number and operations, a guide to using leveled texts to differentiate instruction in mathematics offers fifteen different topics with high-interest text written at four different reading levels, accompanied by matching visuals and practice problems.
With a focus on fractions, decimals, and percents, a guide to using leveled texts to differentiate instruction in mathematics offers fifteen different topics with high-interest text written at four different reading levels, accompanied by matching visuals and practice problems.
This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program. Advanced undergraduate students and introductory graduate students from a variety of quantitative backgrounds will benefit from the transitional bridge that this volume offers, from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level. In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.
All students can learn about analyzing line graphs through text written at four different reading levels. Symbols on the pages represent reading-level ranges to help differentiate instruction. Provided comprehension questions complement the text.
All students can learn about analyzing bar graphs through text written at four different reading levels. Symbols on the pages represent reading-level ranges to help differentiate instruction. Provided comprehension questions complement the text.
All students can learn about analyzing pictographs through text written at four different reading levels. Symbols on the pages represent reading-level ranges to help differentiate instruction. Provided comprehension questions complement the text.
All students can learn about analyzing circle graphs through text written at four different reading levels. Symbols on the pages represent reading-level ranges to help differentiate instruction. Provided comprehension questions complement the text.
With a focus on geometry, a guide to using leveled texts to differentiate instruction in mathematics offers fifteen different topics with high-interest text written at four different reading levels, accompanied by matching visuals and practice problems.