Statistics with R : a beginner's guide /: a beginner's guide. (2022)

• Record Type: Book
• Further Information: Note: Robert Stinerock.
• Authors: Stinerock, Robert Noel
• Contents: Chapter 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 Adjusting the Sample Size to Control the Size of β 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;
• Edition: Second edition
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 519.5; Statistics; R (Computer program language)
• Languages: English
• ISBNs: 9781529788211
• Related ISBNs: 9781529753530; 9781529753523
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.