Description
Taylor & Francis Ltd Probability And Statistics With R 2Nd Edition 2015 by Maria Dolores Ugarte
Cohesively Incorporates Statistical Theory with R ImplementationSince the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, Probability and Statistics with R, Second Edition explores how some of these new packages make analysis easier and more intuitive as well as create more visually pleasing graphs.New to the Second EditionImprovements to existing examples, problems, concepts, data, and functionsNew examples and exercises that use the most modern functions Coverage probability of a confidence interval and model validationHighlighted R code for calculations and graph creationGets Students Up to Date on Practical Statistical TopicsKeeping pace with today's statistical landscape, this textbook expands your students' knowledge of the practice of statistics. It effectively links statistical concepts with R procedures, empowering students to solve a vast array of real statistical problems with R.Web ResourcesA supplementary website offers solutions to odd exercises and templates for homework assignments while the data sets and R functions are available on CRAN. What Is R? Introduction to R Downloading and Installing RVectorsMode and Class of an Object Getting Help External Editors RStudio PackagesR Data StructuresReading and Saving Data in RWorking with DataUsing Logical Operators with Data Frames Tables Summarizing Functions Probability Functions Flow Control Creating Functions Simple Imputation Using plot() Coordinate Systems and Traditional Graphic's StatesExploring DataWhat Is Statistics? Data Displaying Qualitative DataDisplaying Quantitative Data Summary Measures of Location Summary Measures of Spread Bivariate Data Complex Plot Arrangements Multivariate DataGeneral Probability and Random Variables Introduction Counting TechniquesAxiomatic ProbabilityRandom VariablesMoment Generating FunctionsUnivariate Probability Distributions Introduction Discrete Univariate DistributionsContinuous Univariate DistributionsMultivariate 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 DistributionSampling and Sampling Distributions SamplingParameters Estimators Sampling Distribution of the Sample Mean Sampling Distribution for a Statistic from an Infinite PopulationSampling Distributions Associated with the Normal DistributionPoint Estimation Introduction Properties of Point EstimatorsPoint Estimation TechniquesConfidence Intervals Introduction Confidence Intervals for Population Means Confidence Intervals for Population VariancesConfidence Intervals Based on Large SamplesHypothesis Testing Introduction Type I and Type II Errors Power Function Uniformly Most Powerful Test -Value or Critical Level Tests of Significance Hypothesis Tests for Population MeansHypothesis Tests for Population Variances Hypothesis Tests for Population ProportionsNonparametric 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 TestsExperimental Design Introduction Fixed Effects Model Analysis of Variance (ANOVA) for the One-Way Fixed Effects Model Power and the Non-Central 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 DesignRegression 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 ANOVA Approach to RegressionGeneral Linear Hypothesis Model BuildingModel 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 IntervalsAppendix A: R Commands Appendix B: Quadratic Forms and Random Vectors and Matrices Bibliography IndexProblems appear at the end of each chapter.