{"product_id":"statistics-with-r-a-beginner-s-guide-9781529753523","title":"Statistics with R: A Beginner′s Guide","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStatistics is made simple with this award-winning guide to using R and applied statistical methods. \u003cbr\u003e \u003cbr\u003e With a clear step-by-step approach explained using real world examples, learn the practical skills you need to use statistical methods in your research from an expert with over 30 years of teaching experience. With a wealth of hands-on exercises and online resources created by the author, practice your skills using the data sets and R scripts from the book with detailed screencasts that accompany each script.\u003cbr\u003e  \u003cbr\u003e This book is ideal for anyone looking to:\u003cbr\u003e • Complete an introductory course in statistics\u003cbr\u003e • Prepare for more advanced statistical courses\u003cbr\u003e • Gain the transferable analytical skills needed to interpret research from across the social sciences\u003cbr\u003e • Learn the technical skills needed to present data visually\u003cbr\u003e • Acquire a basic competence in the use of R and RStudio. \u003cbr\u003e \u003cbr\u003e This edition also includes a gentle introduction to Bayesian methods integrated throughout.\u003cbr\u003e \u003cbr\u003e The author has created a wide range of online resources, including: over 90 R scripts, 36 datasets, 37 screen casts, complete solutions for all exercises, and 130 multiple-choice questions to test your knowledge. \u003cbr\u003e  \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eThis book is a treasure for both instructors and students. It is written by a master, award-winning teacher with an unparalleled expertise of getting difficult concepts across in a deceptively simple fashion. Written in clear functional English, it both teaches the usual applied statistical methods, as well as provides a gentle introduction to Bayesian methods throughout the book. This is, in essence, more of a new book than just a new edition of an existing one. However, the features that made the first edition so successful have been retained: a student needs only basic algebra to understand the conceptual formulations that are illustrated with hands-on real-life examples that will appeal to students and motivate them to understand the importance of statistics in their daily lives.  -- Swati Mukerjee\u003cbr\u003eIntroduction to statistics is a busy field, and Stinerock explains the subject in a careful and friendly manner. The inclusion of Bayesian methods in the second edition is an important contribution — when it is encountered at the beginning of the statistical journey, it allows the reader to appreciate the richness of the Bayesian approach without dealing with the analytical and computational complexities of the subject. -- Eric Novik\u003cbr\u003eThis book is a wonderful primer for learning both statistics and introductory R programming. It is clearly written, provides straightforward explanations of traditional and Bayesian methods, has a lot of supporting material for instructors and students including numerous practice data sets and solved exercises.  -- Saroja Subrahmanyan\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: Introduction and R Instructions    Basic Terminology    Data: Qualitative or Quantitative    Data: Cross-Sectional or Longitudinal    Descriptive Statistics    Probability    Statistics: Estimation and Inference Chapter 2: Descriptive Statistics: Tabular and Graphical Methods    Methods of Summarizing and Displaying Qualitative Data    Methods of Summarizing and Displaying Quantitative Data    Cross Tabulations and Scatter Plots Chapter 3: Descriptive Statistics: Numerical Methods    Measures of Central Tendency    Measures of Location    Exploratory Data Analysis: The Box Plot Display    Measures of Variability    The z-Score: A Measure of Relative Location    Measures of Association: The Bivariate Case    The Geometric Mean Chapter 4: Introduction to Probability    Some Important Definitions    Counting Rules    Assigning Probabilities    Events and Probabilities    Probabilities of Unions and Intersections of Events    Conditional Probability    Bayes′ Theorem and Events Chapter 5: Discrete Probability Distributions    The Discrete Uniform Probability Distribution    The Expected Value and Standard Deviation of a Discrete Random Variable    The Binomial Probability Distribution    The Poisson Probability Distribution    The Hypergeometric Probability Distribution    The Hypergeometric Probability Distribution: The General Case    Bayes′ Theorem and Discrete Random Variables Chapter 6: Continuous Probability Distributions    Continuous Uniform Probability Distribution    Normal Probability Distribution    Exponential Probability Distribution    Optional Material: Derivation of the Cumulative Exponential Probability Func- tion    Bayes′ Theorem and Continuous Random Variables Chapter 7: Point Estimation and Sampling Distributions    Populations and Samples    The Simple Random Sample    The Sample Statistic: x, s, and p    The Sampling Distribution of x    The Sampling Distribution of p    Some Other Commonly Used Sampling Methods    Bayes′ Theorem: Approximate Bayesian Computation Chapter 8: Confidence Interval Estimation    Interval Estimate of µ When σ Is Known    Interval Estimate of µ When σ  Is Unknown    Sample Size Determination in the Case of µ    Interval Estimate of p    Sample Size Determination in the Case of p    Bayes’ Theorem: Confidence Intervals or Credible Intervals Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example Chapter 10:  Hypothesis Tests about Means and Proportions: Applications    The Lower-Tail Hypothesis Test about μ: σ Is Known    The Two-Tail Hypothesis Test about μ: σ Is Known    The Upper-Tail Hypothesis Test about μ: σ Is Unknown    The Two-Tail Hypothesis Test about μ: σ is Unknown    Hypothesis Tests about p    Calculating the Probability of a Type II Error: β\t    Adjusting the Sample Size to Control the Size of β\t    Bayes’ Theorem and an Inferential Approach to p Chapter 11: Comparisons of Means and Proportions    The Difference between μ1 and μ2: Independent Samples    The Difference between μ1 and μ2: Paired Samples    The Difference between p1 and p2: Independent Samples    Bayes’ Theorem and the Difference between p1 and p2 Chapter 12: Simple Linear Regression    Simple Linear Regression: The Model    The Estimated Regression Equation    Goodness of Fit: The Coefficient of Determination, r2    The Hypothesis Test about β1    Alternative Approaches to Testing Significance    So Far, We Have Tested Only b1. Will We Also Test b0?    Assumptions: What Are They?    Assumptions: How Are They Validated?    Optional Material: Derivation of the Expressions for the Least-Squares Estimates of β0 and β1    Bayes’ Theorem: Using Stan to Estimate the Relationship between Two Variables Chapter 13: Multiple Regression    Simple Linear Regression: A Reprise    Multiple Regression: The Model    Multiple Regression: The Multiple Regression Equation    The Estimated Multiple Regression Equation    Multiple Regression: The 2 Independent Variable Case    Assumptions: What Are They? Can We Validate Them?    Tests of Significance: The Overall Regression Model    Tests of Signicance: The Independent Variables    There Must Be An Easier Way Than This, Right?    Using the Estimated Regression Equation for Prediction    Independent Variable Selection: The Best-Subsets Method    Logistic Regression: The Zero-One Dependent Variable    Bayes′ Theorem: Stan and Multiple Regression Analysis","brand":"Sage Publications Ltd","offers":[{"title":"Default Title","offer_id":48886014214487,"sku":"9781529753523","price":999.99,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/statistics-with-r-a-beginner-s-guide-9781529753523","provider":"Book Curl","version":"1.0","type":"link"}