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Statistics for linguists : an introduction using R

Statistics for linguists : an introduction using R (2회 대출)

자료유형
단행본
개인저자
Winter, Bodo.
서명 / 저자사항
Statistics for linguists : an introduction using R / Bodo Winter.
발행사항
New York, NY :   Routledge,   2020.  
형태사항
xvi, 310 p. : ill. ; 23 cm.
ISBN
9781138056084 (hbk) 9781138056091 (pbk) 9781315165547 (ebk)
요약
"Statistics for Linguists: An introduction using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields including Psychology, Cognitive Science, and Data Science"--
서지주기
Includes bibliographical references (p. [281]-289) and index.
일반주제명
Linguistics --Statistical methods. R (Computer program language). Mathematical linguistics.
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008 200228s2020 nyua b 001 0 eng d
010 ▼a 2019029350
020 ▼a 9781138056084 (hbk)
020 ▼a 9781138056091 (pbk)
020 ▼a 9781315165547 (ebk)
035 ▼a (KERIS)REF000019048286
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
050 0 0 ▼a P138.5 ▼b .W56 2019
082 0 0 ▼a 410.1/5195 ▼2 23
084 ▼a 410.15195 ▼2 DDCK
090 ▼a 410.15195 ▼b W784s
100 1 ▼a Winter, Bodo.
245 1 0 ▼a Statistics for linguists : ▼b an introduction using R / ▼c Bodo Winter.
260 ▼a New York, NY : ▼b Routledge, ▼c 2020.
300 ▼a xvi, 310 p. : ▼b ill. ; ▼c 23 cm.
504 ▼a Includes bibliographical references (p. [281]-289) and index.
520 ▼a "Statistics for Linguists: An introduction using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields including Psychology, Cognitive Science, and Data Science"-- ▼c Provided by publisher.
650 0 ▼a Linguistics ▼x Statistical methods.
650 0 ▼a R (Computer program language).
650 0 ▼a Mathematical linguistics.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 410.15195 W784s 등록번호 111825068 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

Table of contents





0. Preface: Approach and how to use this book


0.1. Strategy of the book


0.2. Why R?


0.3. Why the tidyverse?


0.4. R packages required for this book


0.5. What this book is not


0.6. How to use this book


0.7. Information for teachers





1. Introduction to base R


1.1. Introduction


1.2. Baby steps: simple math with R


1.3. Your first R script


1.4. Assigning variables


1.5. Numeric vectors


1.6. Indexing


1.7. Logical vectors


1.8. Character vectors


1.9. Factor vectors


1.10. Data frames


1.11. Loading in files


1.12. Plotting


1.13. Installing, loading, and citing packages


1.14. Seeking help


1.15. A note on keyboard shortcuts


1.16. Your R journey: The road ahead





2. Tidy functions and reproducible R workflows


2.1. Introduction


2.2. tibble and readr


2.3. dplyr


2.4. ggplot2


2.5. Piping with magrittr


2.6. A more extensive example: iconicity and the senses


2.7. R markdown


2.8. Folder structure for analysis projects


2.9. Readme files and more markdown


2.10. Open and reproducible research





3. Models and distributions


3.1. Models


3.2. Distributions


3.3. The normal distribution


3.4. Thinking of the mean as a model


3.5. Other summary statistics: median and range


3.6. Boxplots and the interquartile range


3.7. Summary statistics in R


3.8. Exploring the emotional valence ratings


3.9. Chapter conclusions





4. Introduction to the linear model: Simple linear regression


4.1. Word frequency effects


4.2. Intercepts and slopes


4.3. Fitted values and residuals


4.4. Assumptions: Normality and constant variance


4.5. Measuring model fit with


4.6. A simple linear model in R


4.7. Linear models with tidyverse functions


4.8. Model formula notation: Intercept placeholders


4.9. Chapter conclusions





5. Correlation, linear, and nonlinear transformations


5.1. Centering


5.2. Standardizing


5.3. Correlation


5.4. Using logarithms to describe magnitudes


5.5. Example: Response durations and word frequency


5.6. Centering and standardization in R


5.7. Terminological note on the term ''normalizing''


5.8. Chapter conclusions





6. Multiple regression


6.1. Regression with more than one predictor


6.2. Multiple regression with standardized coefficients


6.3. Assessing assumptions


6.4. Collinearity


6.5. Adjusted


6.6. Chapter conclusions





7. Categorical predictors


7.1. Introduction


7.2. Modeling the emotional valence of taste and smell words


7.3. Processing the taste and smell data


7.4. Treatment coding in R


7.5. Doing dummy coding ''by hand''


7.6. Changing the reference level


7.7. Sum coding in R


7.8. Categorical predictors with more than two levels


7.9. Assumptions again


7.10. Other coding schemes


7.11. Chapter conclusions





8. Interactions and nonlinear effects


8.1. Introduction


8.2. Categorical * continuous interactions


8.3. Categorical * categorical interactions


8.4. Continuous * continuous interactions


8.5. Continuous interactions and regression planes


8.6. Higher-order interactions


8.7. Chapter conclusions





9. Inferential statistics 1: Significance testing


9.1. Introduction


9.2. Effect size: Cohen''s


9.3. Cohen''s in R


9.4. Standard errors and confidence intervals


9.5. Null hypotheses


9.6. Using to measure the incompatibility with the null hypothesis


9.7. Using the -distribution to compute -values


9.8. Chapter conclusions





10. Inferential statistics 2: Issues in significance testing


10.1. Common misinterpretations of -values


10.2. Statistical power and Type I, II, M, and S errors


10.3. Multiple testing


10.4. Stopping rules


10.5. Chapter conclusions





11. Inferential statistics 3: Significance testing in a regression context


11.1. Introduction


11.2. Standard errors and confidence intervals for regression coefficients


11.3. Significance tests with multi-level categorical predictors


11.4. Another example: the absolute valence of taste and smell words


11.5. Communicating uncertainty for categorical predictors


11.6. Communicating uncertainty for continuous predictors


11.7. Chapter conclusions





12. Generalized linear models: Logistic regression


12.1. Motivating generalized linear models


12.2. Theoretical background: Data-generating processes


12.3. The log odd function and interpreting logits


12.4. Speech errors and blood alcohol concentration


12.5. Predicting the dative alternation


12.6. Analyzing gesture perception: Hassemer & Winter (2016)


12.6.1. Exploring the dataset


12.6.2. Logistic regression analysis


12.7. Chapter conclusions





13. Generalized linear models 2: Poisson regression


13.1. Motivating Poisson regression


13.2. The Poisson distribution


13.3. Analyzing linguistic diversity using Poisson regression


13.4. Adding exposure variables


13.5. Negative binomial regression for overdispersed count data


13.6. Overview and summary of the generalized linear model framework


13.7. Chapter conclusions





14. Mixed models 1: Conceptual introduction


14.1. Introduction


14.2. The independence assumption


14.3. Dealing with non-independence via experimental design and averaging


14.4. Mixed models: Varying intercepts and varying slopes


14.5. More on varying intercepts and varying slopes


14.6. Interpreting random effects and random effect correlations


14.7. Specifying mixed effects models: lme4 syntax


14.8. Reasoning about your mixed model: The importance of varying slopes


14.9. Chapter conclusions





15. Mixed models 2: Extended example, significance testing, convergence issues


15.1. Introduction


15.2. Simulating vowel durations for a mixed model analysis


15.3. Analyzing the simulated vowel durations with mixed models


15.4. Extracting information out of lme4 objects


15.5. Messing up the model


15.6. Likelihood ratio tests


15.7. Remaining issues


15.7.1. -squared for mixed models


15.7.2. Predictions from mixed models


15.7.3. Convergence issues


15.8. Mixed logistic regression: Ugly selfies


15.9. Shrinkage and individual differences


15.10. Chapter conclusions





16. Outlook and strategies for model building


16.1. What you have learned so far


16.2. Model choice


16.3. The cookbook approach


16.4. Stepwise regression


16.5. A plea for subjective and theory-driven statistical modeling


16.6. Reproducible research


16.7. Closing words





References





Appendix A. Correspondences between significance tests and linear models


Appendix B. Reading recommendations

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