Probability and Probability Distributions With R Applications Özlem Gürünlü Alma 10 İndirim — Özlem Gürünlü Alma

This textbook is designed to serve as both a primary and supplementary resource for all academic departments offering a course in probability Each chapter is systematically structured to begin with a theoretical exposition followed by detailed fully worked examples To support deeper understanding and practical application of probability concepts each section includes corresponding R programming implementations The content encompasses the foundational concepts of set theory construction of sample spaces conditional probability independence and Bayes theorem It further explores the concept of random variables properties of discrete and continuous random variables probability mass functions probability density functions and cumulative distribution functions Scenarios involving univariate bivariate and multivariate random variables are thoroughly analyzed Additionally the text covers independence of random variables conditional probability functions quantiles and key statistical measures associated with probability distributions including expected value variance moments moment generating functions covariance correlation characteristic functions and factorial moment generating functions Essential inequalities such as Markov Chebyshev and Cauchy Schwarz along with the Central Limit Theorem are presented with comprehensive exercises and R based solutions The book also provides an in depth examination of commonly used discrete and continuous probability distributions including Bernoulli Binomial Multinomial Geometric Negative Binomial Hypergeometric Generalized Hypergeometric Poisson Discrete Uniform Continuous Uniform Normal Standard Normal Bivariate Normal Log Normal Exponential Gamma Beta and Cauchy distributions For each distribution the probability and distribution functions distributional shapes expected values moments and moment generating functions are derived and illustrated with examples and R programming applications

Özlem Gürünlü Alma tarafından kaleme alınan Probability and Probability Distributions With R Applications Nobel Bilimsel Eserler eseri olarak okurlarla buluşuyor Probability and Probability Distributions With R Applications Özlem Gürünlü Alma Kitap Özeti This textbook is designed to serve as both a primary and supplementary resource for all academic departments offering a course in probability Each chapter is systematically structured to begin with a theoretical exposition followed by detailed fully worked examples To support deeper understanding and practical application of probability concepts each section includes corresponding R programming implementations The content encompasses the foundational concepts of set theory construction of sample spaces conditional probability independence and Bayes theorem It further explores the concept of random variables properties of discrete and continuous random variables probability mass functions probability density functions and cumulative distribution functions Scenarios involving univariate bivariate and multivariate random variables are thoroughly analyzed Additionally the text covers independence of random variables conditional probability functions quantiles and key statistical measures associated with probability distributions including expected value variance moments moment generating functions covariance correlation characteristic functions and factorial moment generating functions Essential inequalities such as Markov Chebyshev and Cauchy Schwarz along with the Central Limit Theorem are presented with comprehensive exercises and R based solutions The book also provides an in depth examination of commonly used discrete and continuous probability distributions including Bernoulli Binomial Multinomial Geometric Negative Binomial Hypergeometric Generalized Hypergeometric Poisson Discrete Uniform Continuous Uniform Normal Standard Normal Bivariate Normal Log Normal Exponential Gamma Beta and Cauchy distributions For each distribution the probability and distribution functions distributional shapes expected values moments and moment generating functions are derived and illustrated with examples and R programming applications Yayınevi Nobel Bilimsel Eserler Yazar Özlem Gürünlü Alma Sayfa 578 Sayfa Kağıt 1 Hamur Boyut 21 00x29 00 cm Basım Yılı Mayıs 2025 Barkod 9786253765125 Kategori Matematik

This textbook is designed to serve as both a primary and supplementary resource for all academic departments offering a course in probability Each chapter is systematically structured to begin with a theoretical exposition followed by detailed fully worked examples To support deeper understanding and practical application of probability concepts each section includes corresponding R programming implementations The content encompasses the foundational concepts of set theory construction of sample spaces conditional probability independence and Bayes theorem It further explores the concept of random variables properties of discrete and continuous random variables probability mass functions probability density functions and cumulative distribution functions Scenarios involving univariate bivariate and multivariate random variables are thoroughly analyzed Additionally the text covers independence of random variables conditional probability functions quantiles and key statistical measures associated with probability distributions including expected value variance moments moment generating functions covariance correlation characteristic functions and factorial moment generating functions Essential inequalities such as Markov Chebyshev and Cauchy Schwarz along with the Central Limit Theorem are presented with comprehensive exercises and R based solutions The book also provides an in depth examination of commonly used discrete and continuous probability distributions including Bernoulli Binomial Multinomial Geometric Negative Binomial Hypergeometric Generalized Hypergeometric Poisson Discrete Uniform Continuous Uniform Normal Standard Normal Bivariate Normal Log Normal Exponential Gamma Beta and Cauchy distributions For each distribution the probability and distribution functions distributional shapes expected values moments and moment generating functions are derived and illustrated with examples and R programming applications