Download or read book A Primer in Probability Second Edition written by Kathleen Subrahmaniam and published by CRC Press. This book was released on 1990-07-27 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Somewhat revised/expanded new edition of a problem-oriented introductory undergraduate text, the first edition of which appeared about a decade ago. The author writes with courteous clarity, and imposes only modest demands upon the mathematical skills of her readers. Problems at the end of each of t

Download or read book A Primer in Probability written by Kathleen Subrahmaniam and published by CRC Press. This book was released on 2018-10-08 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Somewhat revised/expanded new edition of a problem-oriented introductory undergraduate text, the first edition of which appeared about a decade ago. The author writes with courteous clarity, and imposes only modest demands upon the mathematical skills of her readers. Problems at the end of each of t

Download or read book A Primer in Probability written by K. Kocherlakota and published by New York : M. Dekker. This book was released on 1979 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: A first glimpse of probability; Basic concepts of probability; Counting procedures and their applications in computing probabilities; Conditional probability; Independence; Random variables; Describing random variables and their distributions; Describing the joint behavior of several random variables; Special discrete probability models.

Download or read book Introduction to Probability Second Edition written by Joseph K. Blitzstein and published by CRC Press. This book was released on 2019-02-08 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 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. The second edition adds many new examples, exercises, and explanations, to deepen understanding of the ideas, clarify subtle concepts, and respond to feedback from many students and readers. New supplementary online resources have been developed, including animations and interactive visualizations, and the book has been updated to dovetail with these resources. Supplementary material is available on Joseph Blitzstein’s website www. stat110.net. The supplements include: Solutions to selected exercises Additional practice problems Handouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110. Links to lecture videos available on ITunes U and YouTube There is also a complete instructor's solutions manual available to instructors who require the book for a course.

Download or read book A Natural Introduction to Probability Theory written by R. Meester and published by Springer Science & Business Media. This book was released on 2008-03-16 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compactly written, but nevertheless very readable, appealing to intuition, this introduction to probability theory is an excellent textbook for a one-semester course for undergraduates in any direction that uses probabilistic ideas. Technical machinery is only introduced when necessary. The route is rigorous but does not use measure theory. The text is illustrated with many original and surprising examples and problems taken from classical applications like gambling, geometry or graph theory, as well as from applications in biology, medicine, social sciences, sports, and coding theory. Only first-year calculus is required.

Download or read book Statistical Inference written by George Casella and published by CRC Press. This book was released on 2024-05-23 with total page 1746 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. Features The classic graduate-level textbook on statistical inference Develops elements of statistical theory from first principles of probability Written in a lucid style accessible to anyone with some background in calculus Covers all key topics of a standard course in inference Hundreds of examples throughout to aid understanding Each chapter includes an extensive set of graduated exercises Statistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations. This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.

Download or read book Basic Probability Theory written by Robert B. Ash and published by Courier Corporation. This book was released on 2008-06-26 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introduction to more advanced courses in probability and real analysis emphasizes the probabilistic way of thinking, rather than measure-theoretic concepts. Geared toward advanced undergraduates and graduate students, its sole prerequisite is calculus. Taking statistics as its major field of application, the text opens with a review of basic concepts, advancing to surveys of random variables, the properties of expectation, conditional probability and expectation, and characteristic functions. Subsequent topics include infinite sequences of random variables, Markov chains, and an introduction to statistics. Complete solutions to some of the problems appear at the end of the book.

Download or read book A Primer for the Mathematics of Financial Engineering written by Dan Stefanica and published by . This book was released on 2011 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Introduction to Probability written by George G. Roussas and published by Academic Press. This book was released on 2013-11-27 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Probability, Second Edition, discusses probability theory in a mathematically rigorous, yet accessible way. This one-semester basic probability textbook explains important concepts of probability while providing useful exercises and examples of real world applications for students to consider. This edition demonstrates the applicability of probability to many human activities with examples and illustrations. After introducing fundamental probability concepts, the book proceeds to topics including conditional probability and independence; numerical characteristics of a random variable; special distributions; joint probability density function of two random variables and related quantities; joint moment generating function, covariance and correlation coefficient of two random variables; transformation of random variables; the Weak Law of Large Numbers; the Central Limit Theorem; and statistical inference. Each section provides relevant proofs, followed by exercises and useful hints. Answers to even-numbered exercises are given and detailed answers to all exercises are available to instructors on the book companion site. This book will be of interest to upper level undergraduate students and graduate level students in statistics, mathematics, engineering, computer science, operations research, actuarial science, biological sciences, economics, physics, and some of the social sciences. Demonstrates the applicability of probability to many human activities with examples and illustrations Discusses probability theory in a mathematically rigorous, yet accessible way Each section provides relevant proofs, and is followed by exercises and useful hints Answers to even-numbered exercises are provided and detailed answers to all exercises are available to instructors on the book companion site

Download or read book A Mathematical Primer for Social Statistics written by John Fox and published by SAGE. This book was released on 2009 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ideal primer for students and researchers across the social sciences who wish to master the necessary maths in order to pursue studies involving advanced statistical methods

Download or read book A Primer on Statistical Distributions written by Narayanaswamy Balakrishnan and published by John Wiley & Sons. This book was released on 2004-12-04 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed as an introduction to statistical distribution theory. * Includes a first chapter on basic notations and definitions that are essential to working with distributions. * Remaining chapters are divided into three parts: Discrete Distributions, Continuous Distributions, and Multivariate Distributions. * Exercises are incorporated throughout the text in order to enhance understanding of materials just taught.

Download or read book Probability and Measure Theory written by Robert B. Ash and published by Academic Press. This book was released on 2000 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Measure Theory, Second Edition, is a text for a graduate-level course in probability that includes essential background topics in analysis. It provides extensive coverage of conditional probability and expectation, strong laws of large numbers, martingale theory, the central limit theorem, ergodic theory, and Brownian motion. Clear, readable style Solutions to many problems presented in text Solutions manual for instructors Material new to the second edition on ergodic theory, Brownian motion, and convergence theorems used in statistics No knowledge of general topology required, just basic analysis and metric spaces Efficient organization

Download or read book Probability Theory written by and published by Allied Publishers. This book was released on 2013 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability theory

Download or read book Introduction to Probability and Statistics written by Giri and published by Routledge. This book was released on 2019-01-22 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beginning with the historical background of probability theory, this thoroughly revised text examines all important aspects of mathematical probability - including random variables, probability distributions, characteristic and generating functions, stochatic convergence, and limit theorems - and provides an introduction to various types of statistical problems, covering the broad range of statistical inference.;Requiring a prerequisite in calculus for complete understanding of the topics discussed, the Second Edition contains new material on: univariate distributions; multivariate distributions; large-sample methods; decision theory; and applications of ANOVA.;A primary text for a year-long undergraduate course in statistics (but easily adapted for a one-semester course in probability only), Introduction to Probability and Statistics is for undergraduate students in a wide range of disciplines-statistics, probability, mathematics, social science, economics, engineering, agriculture, biometry, and education.

Download or read book A Primer of Ecological Statistics written by Nicholas J. Gotelli and published by Sinauer Associates, Incorporated. This book was released on 2004 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Part I: FundamentaIs of Probability and Statistical Thinking. Chapter 1: An lntroduction to Probability. What Is Probability? Measuring Probability. The Probability of a Single Event. Prey Capture by Carnivorous Plants. Estimating Probabilities by Sampling . Problems in the Definition Probability The Mathematics of Probability. Defining the Sample Space. Complex and Shared Events: Combining Simple Probabilities. Probability Calcu1ations: Milkweeds and Caterpillars. Complex and Shared Events: Rules for Combining Sets, Conditional Probabilities. Bayes' Theorem. Chapter 2: Random Variables and Probability Distributions. Discrete Random Variables. Bernoulli Random Variables. An Example of a Bernoulli Trial. Many Bernoulli Trials = A Binomial Randorn Variable. The Binomial Distribution. Poisson Random Variables. An Example of a Poisson Random Variable: Distribution of aRare Plant. The Expected Value of a Discrete Random Variable. The Variance of a Discrete Random Variable. Continuous Random Variables. Uniform Random Variables. The Expected Value of a Continuous Random Variable. Normal Random Variables. Useful Properties of the Normal Distribution. Other Continuous Random Variables. The Central Limit Theorem. Chapter 3: Summary Statistics: Measuresof Location and Spread. Measures of Location. The Arithmetic Mean Other Means. Other Measures of Location: The Median and the Mode. When to Use Each Measure of Location. Measures of Spread. The Variance and the Standard Deviation. The Standard Error of the Mean. Skewness, Kurtosis, and Central Moments. Quantiles. Using Measures of Spread. Some Philosophical Issues Surrounding Summary Statistics. Confidence Intervals. Generalized Confidence lntervals. Chapter 4: Framing and Testing Hypotheses. Scientific Methods. Deduction and lnduction. Moderrn-Day lnduction: Bayesian lnference. The Hypothetico-Deductive Method. Testing Statistical Hypotheses. Statistical Hypotheses versus Scientific Hypotheses. Statistical Significance and P - Values. Errors in Hypothesis Testing. Parameter Estimation and Prediction. Chapter 5:Three Frameworks for Statistical Analysis. Sample Problem. Monte Carlo Analysis. Step 1: Specifying the Test Statistic. Step 2: Creating the Null Distribution. Step 3: Deciding on a One- or Two- Tailed Test. Step 4: Calculating the Tail Probability. Assumptions of the Monte Carlo Method. Advantages and Disadvantages of the Monte Carlo Method. Parametric Analysis. Step 1: Specifyjng the Test Statistic. Step 2: Specifying the Null Distribution. Step 3: Calculating the Tail Probability. Assumptions of the Parametric Method. Advantages and Disadvantages of the Parametric Method. Least-Squares Parameter Estimates 246 Variance Components and the Coefficient of Determination. Hypothesis Tests with Regression. The Anatomy of an ANOVA Table. Other Tests and Confidence IntervaIs. Assumptions of Regression. Diagnostic Tests For Regression. Plotting ResiduaIs. Other Diagnostic Plots. The lnfluence Function. Monte Cado and Bayesian Analyses. Linear Regression Using Monte Cado Methods. Linear Regression Using Bayesian Methods. Other Kinds of Regression Analyses. Robust Regression. Quantile Regression. Logistic Regression. Non-Linear Regression. Multiple Regression. Path AnaIysis. Model Selection Cri teria. Model Selection Methods for Multiple Regression. Model Selection Methods in Path Analysis. Bayesian Model Selection. Chapter 10: The Analysis Of VarianceSymbols and Labels in ANOVA. ANOVA and Partitioning of the Sum of Squares. The Assumptions of ANOVA. Hypothesis Tests with ANOVA. Constructing F- Ratios. A Bestiary of ANOVA Tables. Randomized Block. Nested ANOVA. Two- Way ANOVA. ANOVA for Three- Way and n- Way Designs. Split-Plot ANOVA. Repeated Measures ANOVA. ANCOVA. Random versus Fixed Factors in ANOVA. Partitioning the Variance in ANOVA. After ANOVA: Plotting and Understanding Interaction Terms. Plotting Results from One-Way ANOVAs. Plotting Results from Two- Way ANOVAs. Understanding the lnteraction Term. Plotting Results fram ANCOVAs. Comparing Means. A Posteriori Comparisons. A Priori Contrasts. Bonferroni Corrections and the Problem of Multiple Tests. Chapter 11: The Analysis of Categorical Data. Two- Way Contingency Tables. Organizing the Data. Are the Variables lndependent? Testing the Hypothesis: Pearson's Chi-square Test. An Alternative to Pearson's Chi-Square: The G- Test. The Chi-square Test and the G- Test for R x C Tables. Which Test To Choose? Multi- Way Contingency Tables. Organizing the Data. On to Multi- Way Tables! Bayesian Approaches to Contingency Tables. Tests for Goodness-of-Fit. Goodness-of- Fit Tests for Discrete Distributions. Testing Goodness-of-Fit for Continuous. Distributions: The Kolmogorov-Smirnov Test. Chapter 12: The Analysis Of Multivariate Data. Approaching Multivariate Data. The Need for Matrix Algebra. Comparing Multivariate Means. Comparing Multivariate Means of Two Samples: Hotelling's y2 Test. Comparing Multivariate Means of More Than Two Samples: A Simple MANOVA. The Multivariate Normal Distribution. Testing for Multivariate Normality. Measurements of Multivariate Distance. Measuring Distances between Two IndividuaIs. Measuring Distances Between Two Groups. Other Measurements of Distance. Ordination. Principal Component Analysis 406 Factor Analysis. Principal Coordinates Analysis. Correspondence Analysis. Non-Metric Multidimensional Scaling. Advantages and Disadvantages of Ordination.Classification . Cluster Analysis. Choosing a Clustering Method. Discriminant Analysis. Advantages and Disadvantages of Classification. Multivariate Multiple Regression. Redundancy Analysis.

Download or read book Data Mining written by Richard J. Roiger and published by CRC Press. This book was released on 2017-01-06 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides in-depth coverage of basic and advanced topics in data mining and knowledge discovery Presents the most popular data mining algorithms in an easy to follow format Includes instructional tutorials on applying the various data mining algorithms Provides several interesting datasets ready to be mined Offers in-depth coverage of RapidMiner Studio and Weka’s Explorer interface Teaches the reader (student,) hands-on, about data mining using RapidMiner Studio and Weka Gives instructors a wealth of helpful resources, including all RapidMiner processes used for the tutorials and for solving the end of chapter exercises. Instructors will be able to get off the starting block with minimal effort Extra resources include screenshot sequences for all RapidMiner and Weka tutorials and demonstrations, available for students and instructors alike The latest version of all freely available materials can also be downloaded at: http://krypton.mnsu.edu/~sa7379bt/

Download or read book Causal Inference in Statistics written by Judea Pearl and published by John Wiley & Sons. This book was released on 2016-01-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.