Statistics with R: A Beginner's Guide
Autor Robert Stinerocken Limba Engleză Paperback – 9 dec 2022
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, practice your skills using the data sets and R scripts from the book with handy screencasts to accompany you.
This book is ideal for anyone looking to:
• Complete an introductory course in statistics
• Prepare for more advanced statistical courses
• Gain the transferable analytical skills needed to interpret research from across the social sciences
• Learn the technical skills needed to present data visually
• Acquire a basic competence in the use of R.
This edition also includes a gentle introduction to Bayesian methods integrated throughout.
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 369.10 lei 17-23 zile | +39.03 lei 7-13 zile |
| SAGE Publications – 9 dec 2022 | 369.10 lei 17-23 zile | +39.03 lei 7-13 zile |
| Hardback (1) | 1032.40 lei 6-8 săpt. | |
| SAGE Publications – 9 dec 2022 | 1032.40 lei 6-8 săpt. |
Preț: 369.10 lei
Nou
Puncte Express: 554
Preț estimativ în valută:
70.66€ • 73.45$ • 58.59£
70.66€ • 73.45$ • 58.59£
Carte disponibilă
Livrare economică 11-17 ianuarie 25
Livrare express 01-07 ianuarie 25 pentru 49.02 lei
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781529753523
ISBN-10: 152975352X
Pagini: 448
Dimensiuni: 170 x 242 x 29 mm
Greutate: 0.77 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications Ltd
Locul publicării:London, United Kingdom
ISBN-10: 152975352X
Pagini: 448
Dimensiuni: 170 x 242 x 29 mm
Greutate: 0.77 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications Ltd
Locul publicării:London, United Kingdom
Cuprins
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
Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example
Chapter 10: Hypothesis Tests about Means and Proportions: Applications
Chapter 11: Comparisons of Means and Proportions
Chapter 12: Simple Linear Regression
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
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
Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example
Chapter 10: Hypothesis Tests about Means and Proportions: Applications
Chapter 11: Comparisons of Means and Proportions
Chapter 12: Simple Linear Regression
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
Notă biografică
Robert Stinerock has more than 35 years of experience teaching statistics and probability to students at both the undergraduate and graduate level. He currently teaches statistics at two different universities: the Executive MBA Program at Baruch College of The City University of New York and the Quantitative Finance Program at the Stevens Institute of Technology in Hoboken, New Jersey.
He has received several awards for excellence in the classroom: the Stevens Howe School Outstanding Undergraduate Teacher Award; the Stevens Alumni Association Outstanding Teacher Award; and the Fairleigh Dickinson Distinguished Faculty Award for Teaching.
He has published numerous research articles in academic journals, most recently in the Journal of Macromarketing, the Journal of Business Research, and Geoforum.
The first edition of Statistics with R was the Choice Outstanding Academic Title Award Winner.
He earned his Bachelor¿s, Master¿s, and Ph.D. degrees, all from Columbia University.
He and his wife, Jyoti, live in New York City.
He has received several awards for excellence in the classroom: the Stevens Howe School Outstanding Undergraduate Teacher Award; the Stevens Alumni Association Outstanding Teacher Award; and the Fairleigh Dickinson Distinguished Faculty Award for Teaching.
He has published numerous research articles in academic journals, most recently in the Journal of Macromarketing, the Journal of Business Research, and Geoforum.
The first edition of Statistics with R was the Choice Outstanding Academic Title Award Winner.
He earned his Bachelor¿s, Master¿s, and Ph.D. degrees, all from Columbia University.
He and his wife, Jyoti, live in New York City.
Descriere
With a clear step-by-step approach explained using real world examples, this book provides the practical skills you need to use statistical methods in your research from an expert with over 30 years of teaching experience.