Statistics with R (eBook)

A Beginner′s Guide

Robert Stinerock (Autor)

eBook Download: EPUB

2022 | 2., Second Edition
SAGE Publications Ltd (Verlag)
978-1-5297-8623-1 (ISBN)

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 5.11.2022
• Verlagsort: London
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Naturwissenschaften
• Themenwelt: Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung
• Schlagworte: Bayesian Methods • beginner statistics • Business Statistics • introduction to statistics • Intro Stats • intro to r • intro to statistics • r basics • R software guide • statistics for dummies • statistics for health • statistics for nursing • statistics for psychology • statistics for sociology • statistics using r • using r
• ISBN-10: 1-5297-8623-1 / 1529786231
• ISBN-13: 978-1-5297-8623-1 / 9781529786231