Probability and Statistics with R

ویرایش: Online-Ausg 
نویسندگان: Arnholt. Alan T.,  Militino. Ana F.,  Ugarte. Maria Dolores  
سری: EBL Schweitzer 
ISBN (شابک) : 9781584888925, 158488892X 
ناشر: CRC Press 
سال نشر: 2008 
تعداد صفحات: 710 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 10 مگابایت 

قیمت کتاب (تومان) : 59,000

Front cover; Chapter 1: A Brief Introduction to S; The Basics of S; Using S; Data Sets; Data Manipulation; S Structures; Mathematical Operations; Vectors; Sequences; Reading Data; Using scan(); Using read.table(); Using write(); Using dump() and source(); Logical Operators and Missing Values; Matrices; Vector and Matrix Operations; Arrays; Lists; Data Frames; Tables; Functions Operating on Factors and Lists; Probability Functions; Creating Functions; Programming Statements; Graphs; Problems; Chapter 2: Exploring Data; What Is Statistics?; Data; Displaying Qualitative Data; Tables; Barplots.

Dot ChartsPie Charts; Displaying Quantitative Data; Stem-and-Leaf Plots; Strip Charts (R Only); Histograms; Summary Measures of Location; The Mean; The Median; Quantiles; Hinges and Five-Number Summary; Boxplots; Summary Measures of Spread; Range; Interquartile Range; Variance; Bivariate Data; Two-Way Contingency Tables; Graphical Representations of Two-Way Contingency Tables; Comparing Samples; Relationships between Two Numeric Variables; Correlation; Sorting a Data Frame by One or More of Its Columns; Fitting Lines to Bivariate Data; Multivariate Data (Lattice and Trellis Graphs).

Arranging Several Graphs on a Single PagePanel Functions; Problems; Chapter 3: General Probability and Random Variables; Introduction; Counting Rules; Sampling With Replacement; Sampling Without Replacement; Combinations; Probability; Sample Space and Events; Set Theory; Interpreting Probability; Relative Frequency Approach to Probability; Axiomatic Approach to Probability; Conditional Probability; The Law of Total Probability and Bayes' Rule; Independent Events ; Random Variables; Discrete Random Variables; Mode, Median, and Percentiles; Expected Values of Discrete Random Variables; Moments.

VarianceRules of Variance; Continuous Random Variables; Numerical Integration with S; Mode, Median, and Percentiles; Expectation of Continuous Random Variables; Markov's Theorem and Chebyshev's Inequality; Weak Law of Large Numbers; Skewness; Moment Generating Functions; Problems; Chapter 4: Univariate Probability Distributions; Introduction; Discrete Univariate Distributions; Discrete Uniform Distribution; Bernoulli and Binomial Distributions; Poisson Distribution; Geometric Distribution; Negative Binomial Distribution; Hypergeometric Distribution; Continuous Univariate Distributions.

Uniform Distribution (Continuous)Exponential Distribution; Gamma Distribution; Hazard Function, Reliability Function, and Failure Rate; Weibull Distribution; Beta Distribution; Normal (Gaussian) Distribution; Problems; Chapter 5: Multivariate Probability Distributions; Joint Distribution of Two Random Variables; Joint pdf for Two Discrete Random Variables; Joint pdf for Two Continuous Random Variables; Independent Random Variables; Several Random Variables; Conditional Distributions; Expected Values, Covariance, and Correlation; Expected Values; Covariance; Correlation.

-Technometrics, May 2009, Vol. 51, No. 2 The book is comprehensive and well written. The notation is clear and the mathematical derivations behind nontrivial equations and computational implementations are carefully explained. Rather than presenting a collection of R scripts together with a summary of relevant theoretical results, this book offers a well-balanced mix of theory, examples and R code.-Raquel Prado, University of California, Santa Cruz, The American Statistician, February 2009... an impressive book ... Overall, this is a good reference book with comprehensive coverage of the details. Read more...
Abstract: Front cover; Chapter 1: A Brief Introduction to S; The Basics of S; Using S; Data Sets; Data Manipulation; S Structures; Mathematical Operations; Vectors; Sequences; Reading Data; Using scan(); Using read.table(); Using write(); Using dump() and source(); Logical Operators and Missing Values; Matrices; Vector and Matrix Operations; Arrays; Lists; Data Frames; Tables; Functions Operating on Factors and Lists; Probability Functions; Creating Functions; Programming Statements; Graphs; Problems; Chapter 2: Exploring Data; What Is Statistics?; Data; Displaying Qualitative Data; Tables; Barplots.

Dot ChartsPie Charts; Displaying Quantitative Data; Stem-and-Leaf Plots; Strip Charts (R Only); Histograms; Summary Measures of Location; The Mean; The Median; Quantiles; Hinges and Five-Number Summary; Boxplots; Summary Measures of Spread; Range; Interquartile Range; Variance; Bivariate Data; Two-Way Contingency Tables; Graphical Representations of Two-Way Contingency Tables; Comparing Samples; Relationships between Two Numeric Variables; Correlation; Sorting a Data Frame by One or More of Its Columns; Fitting Lines to Bivariate Data; Multivariate Data (Lattice and Trellis Graphs).

Arranging Several Graphs on a Single PagePanel Functions; Problems; Chapter 3: General Probability and Random Variables; Introduction; Counting Rules; Sampling With Replacement; Sampling Without Replacement; Combinations; Probability; Sample Space and Events; Set Theory; Interpreting Probability; Relative Frequency Approach to Probability; Axiomatic Approach to Probability; Conditional Probability; The Law of Total Probability and Bayes' Rule; Independent Events ; Random Variables; Discrete Random Variables; Mode, Median, and Percentiles; Expected Values of Discrete Random Variables; Moments.

VarianceRules of Variance; Continuous Random Variables; Numerical Integration with S; Mode, Median, and Percentiles; Expectation of Continuous Random Variables; Markov's Theorem and Chebyshev's Inequality; Weak Law of Large Numbers; Skewness; Moment Generating Functions; Problems; Chapter 4: Univariate Probability Distributions; Introduction; Discrete Univariate Distributions; Discrete Uniform Distribution; Bernoulli and Binomial Distributions; Poisson Distribution; Geometric Distribution; Negative Binomial Distribution; Hypergeometric Distribution; Continuous Univariate Distributions.

Uniform Distribution (Continuous)Exponential Distribution; Gamma Distribution; Hazard Function, Reliability Function, and Failure Rate; Weibull Distribution; Beta Distribution; Normal (Gaussian) Distribution; Problems; Chapter 5: Multivariate Probability Distributions; Joint Distribution of Two Random Variables; Joint pdf for Two Discrete Random Variables; Joint pdf for Two Continuous Random Variables; Independent Random Variables; Several Random Variables; Conditional Distributions; Expected Values, Covariance, and Correlation; Expected Values; Covariance; Correlation.

-Technometrics, May 2009, Vol. 51, No. 2 The book is comprehensive and well written. The notation is clear and the mathematical derivations behind nontrivial equations and computational implementations are carefully explained. Rather than presenting a collection of R scripts together with a summary of relevant theoretical results, this book offers a well-balanced mix of theory, examples and R code.-Raquel Prado, University of California, Santa Cruz, The American Statistician, February 2009... an impressive book ... Overall, this is a good reference book with comprehensive coverage of the details

Content: A Brief Introduction to S   The Basics of S  Using S  Data Sets  Data Manipulation  Probability Functions   Creating Functions   Programming Statements  Graphs    Exploring Data   What Is Statistics?   Data   Displaying Qualitative Data  Displaying Quantitative Data  Summary Measures of Location  Summary Measures of Spread  Bivariate Data  Multivariate Data (Lattice and Trellis Graphs)    General Probability and Random Variables   Introduction   Counting Rules   Probability  Random Variables    Univariate Probability Distributions   Introduction  Discrete Univariate Distributions  Continuous Univariate Distributions    Multivariate Probability Distributions   Joint Distribution of Two Random Variables   Independent Random Variables   Several Random Variables   Conditional Distributions   Expected Values, Covariance, and Correlation   Multinomial Distribution   Bivariate Normal Distribution    Sampling and Sampling Distributions   Sampling  Parameters  Estimators  Sampling Distribution of the Sample Mean   Sampling Distribution for a Statistic from an Infinite Population  Sampling Distributions Associated with the Normal Distribution    Point Estimation   Introduction  Properties of Point Estimators  Point Estimation Techniques    Confidence Intervals   Introduction  Confidence Intervals for Population Means  Confidence Intervals for Population Variances  Confidence Intervals Based on Large Samples    Hypothesis Testing   Introduction   Type I and Type II Errors   Power Function   Uniformly Most Powerful Test  â  -Value or Critical Level   Tests of Significance   Hypothesis Tests for Population Means  Hypothesis Tests for Population Variances   Hypothesis Tests for Population Proportions    Nonparametric Methods   Introduction   Sign Test  Wilcoxon Signed-Rank Test   The Wilcoxon Rank-Sum or the Mann-Whitney U-Test   The Kruskal-Wallis Test  Friedman Test for Randomized Block Designs  Goodness-of-Fit Tests   Categorical Data Analysis   Nonparametric Bootstrapping   Permutation Tests    Experimental Design   Introduction   Fixed-Effects Model  Analysis of Variance (ANOVA) for the One-Way Fixed-Effects Model  Power and the Noncentral F Distribution  Checking Assumptions  Fixing Problems   Multiple Comparisons of Means   Other Comparisons among the Means  Summary of Comparisons of Means   Random-Effects Model (Variance Components Model)   Randomized Complete Block Design   Two-Factor Factorial Design    Regression   Introduction   Simple Linear Regression  Multiple Linear Regression   Ordinary Least Squares  Properties of the Fitted Regression Line   Using Matrix Notation with Ordinary Least Squares   The Method of Maximum Likelihood   The Sampling Distribution of ss   ANOVA Approach to Regression  General Linear Hypothesis  Model Selection and Validation  Interpreting a Logarithmically Transformed Model   Qualitative Predictors  Estimation of the Mean Response for New Values Xh   Prediction and Sampling Distribution of New Observations Yh(new)  Simultaneous Confidence Intervals    Appendix A: S Commands     Appendix B: Quadratic Forms and Random Vectors and Matrices   Quadratic Forms   Random Vectors and Matrices   Variance of Random Vectors     References     Index    Problems appear at the end of each chapter.