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{ "item_title" : "Probability and Statistics", "item_author" : [" Zhao Bin "], "item_description" : "This book bridges abstract theory and real-world application, serving as a foundational resource for students and practitioners in probability, statistics, and data science. It addresses the philosophical challenge of navigating a non-deterministic world by distinguishing probability (deductive, mathematical known) from statistics (inductive, scientific unknown). Part I: The Machinery of Probability formalizes chance through axioms, sample spaces, and set theory. Conditional probability reshapes perspective by updating beliefs with new information, leading to independence/dependence concepts. Bayes' Theorem is highlighted as a revolutionary tool for hypothesis updating. Random variables (discrete/continuous) and distributions (Binomial, Poisson, Normal) are explored, with the Normal distribution emphasized via the Central Limit Theorem for its ubiquity in inference. Expectation is introduced for risk assessment. Part II:: The Practice of Inference applies theory to draw population conclusions from samples. Sampling concepts (bias, variability, randomization) and sampling distributions underpin uncertainty quantification. Confidence intervals (interpreting 95% confidence) and hypothesis testing (p-values, Type I/II errors) are explained conceptually, warning against misinterpretation pitfalls. Exploring Relationships and Advanced Applications extends to multivariate analysis via correlation and regression. Linear regression models relationships, with slope/intercept interpretation, R-squared for fit assessment, and assumption checks. The book concludes with modern applications (machine learning, A/B testing) and ethical considerations, stressing statistical literacy to combat misuse. Ultimately, the text cultivates a mindset embracing uncertainty, evidence-based reasoning, and quantified doubt, empowering readers to navigate incomplete information with rigorous intellectual tools.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/99/935/674/9999356745_b.jpg", "price_data" : { "retail_price" : "90.00", "online_price" : "90.00", "our_price" : "90.00", "club_price" : "90.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Probability and Statistics|Zhao Bin

Probability and Statistics

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Overview

This book bridges abstract theory and real-world application, serving as a foundational resource for students and practitioners in probability, statistics, and data science. It addresses the philosophical challenge of navigating a non-deterministic world by distinguishing probability (deductive, mathematical "known") from statistics (inductive, scientific "unknown"). Part I: The Machinery of Probability formalizes chance through axioms, sample spaces, and set theory. Conditional probability reshapes perspective by updating beliefs with new information, leading to independence/dependence concepts. Bayes' Theorem is highlighted as a revolutionary tool for hypothesis updating. Random variables (discrete/continuous) and distributions (Binomial, Poisson, Normal) are explored, with the Normal distribution emphasized via the Central Limit Theorem for its ubiquity in inference. Expectation is introduced for risk assessment. Part II:: The Practice of Inference applies theory to draw population conclusions from samples. Sampling concepts (bias, variability, randomization) and sampling distributions underpin uncertainty quantification. Confidence intervals (interpreting "95% confidence") and hypothesis testing (p-values, Type I/II errors) are explained conceptually, warning against misinterpretation pitfalls. Exploring Relationships and Advanced Applications extends to multivariate analysis via correlation and regression. Linear regression models relationships, with slope/intercept interpretation, R-squared for fit assessment, and assumption checks. The book concludes with modern applications (machine learning, A/B testing) and ethical considerations, stressing statistical literacy to combat misuse. Ultimately, the text cultivates a mindset embracing uncertainty, evidence-based reasoning, and quantified doubt, empowering readers to navigate incomplete information with rigorous intellectual tools.

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Details

  • ISBN-13: 9789999356749
  • ISBN-10: 9999356745
  • Publisher: Eliva Press
  • Publish Date: October 2026
  • Dimensions: 9 x 6 x 0.88 inches
  • Shipping Weight: 1.27 pounds
  • Page Count: 432

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