Muutke küpsiste eelistusi

E-raamat: Statistics for Linguists: An Introduction Using R

4.35/5 (52 hinnangut Goodreads-ist)
(Lecturer in Cognitive Linguistics at the University of Birmingham, UK.)
  • Formaat: 326 pages
  • Ilmumisaeg: 30-Oct-2019
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781351677431
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 57,19 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Statistics for Linguists: An Introduction Using RSuurem pilt
  • Formaat: 326 pages
  • Ilmumisaeg: 30-Oct-2019
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781351677431
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Statistics for Linguistics: 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.

Acknowledgments x
0 Preface: Approach and How to Use This Book xii
0.1 Strategy of the Book
xii
0.2 Why R?
xiii
0.3 Why the Tidyverse?
xiv
0.4 R Packages Required for This Book
xv
0.5 What This Book Is Not
xv
0.6 How to Use This Book
xv
0.7 Information for Teachers
xvi
1 Introduction to R 1(26)
1.1 Introduction
1(1)
1.2 Baby Steps: Simple Math with R
2(2)
1.3 Your First R Script
4(1)
1.4 Assigning Variables
5(2)
1.5 Numeric Vectors
7(2)
1.6 Indexing
9(1)
1.7 Logical Vectors
10(1)
1.8 Character Vectors
11(1)
1.9 Factor Vectors
12(1)
1.10 Data Frames
13(3)
1.11 Loading in Files
16(3)
1.12 Plotting
19(1)
1.13 Installing Loading and Citing Packages
20(1)
1.14 Seeking Help
21(1)
1.15 A Note on Keyboard Shortcuts
22(1)
1.16 Your R Journey: The Road Ahead
23(4)
2 The Tidyverse and Reproducible R Workflows 27(26)
2.1 Introduction
27(1)
2.2 tibble and readr
28(2)
2.3 dplyr
30(4)
2.4 ggplot2
34(2)
2.5 Piping with magrittr
36(1)
2.6 A More Extensive Example: Iconicity and the Senses
37(7)
2.7 R Markdown
44(1)
2.8 Folder Structure for Analysis Projects
45(1)
2.9 Readme Files and More Markdown
46(1)
2.10 Open and Reproducible Research
47(6)
3 Descriptive Statistics, Models, and Distributions 53(16)
3.1 Models
53(1)
3.2 Distributions
53(1)
3.3 The Normal Distribution
54(3)
3.4 Thinking of the Mean as a Model
57(1)
3.5 Other Summary Statistics: Median and Range
58(1)
3.6 Boxplots and the Interquartile Range
59(1)
3.7 Summary Statistics in R
60(4)
3.8 Exploring the Emotional Valence Ratings
64(3)
3.9
Chapter Conclusions
67(2)
4 Introduction to the Linear Model: Simple Linear Regression 69(17)
4.1 Word Frequency Effects
69(2)
4.2 Intercepts and Slopes
71(1)
4.3 Fitted Values and Residuals
72(2)
4.4 Assumptions: Normality and Constant Variance
74(1)
4.5 Measuring Model Fit with R2
74(3)
4.6 A Simple Linear Model in R
77(5)
4.7 Linear Models with Tidyverse Functions
82(1)
4.8 Model Formula Notation: Intercept Placeholders
83(1)
4.9
Chapter Conclusions
84(2)
5 Correlation, Linear, and Nonlinear Transformations 86(17)
5.1 Centering
86(1)
5.2 Standardizing
87(2)
5.3 Correlation
89(1)
5.4 Using Logarithms to Describe Magnitudes
90(4)
5.5 Example: Response Durations and Word Frequency
94(4)
5.6 Centering and Standardization in R
98(3)
5.7 Terminological Note on the Term 'Normalizing'
101(1)
5.8
Chapter Conclusions
101(2)
6 Multiple Regression 103(14)
6.1 Regression with More Than One Predictor
103(2)
6.2 Multiple Regression with Standardized Coefficients
105(4)
6.3 Assessing Assumptions
109(3)
6.4 Collinearity
112(3)
6.5 Adjusted R2
115(1)
6.6
Chapter Conclusions
116(1)
7 Categorical Predictors 117(16)
7.1 Introduction
117(1)
7.2 Modeling the Emotional Valence of Taste and Smell Words
117(2)
7.3 Processing the Taste and Smell Data
119(3)
7.4 Treatment Coding in R
122(1)
7.5 Doing Dummy Coding By Hand
123(1)
7.6 Changing the Reference Level
124(1)
7.7 Sum-coding in R
125(2)
7.8 Categorical Predictors with More Than Two Levels
127(2)
7.9 Assumptions Again
129(1)
7.10 Other Coding Schemes
130(1)
7.11
Chapter Conclusions
131(2)
8 Interactions and Nonlinear Effects
8.1 Introduction
133(1)
8.2 Categorical * Continuous Interactions
134(5)
8.3 Categorical * Categorical Interactions
139(7)
8.4 Continuous * Continuous Interactions
146(4)
8.5 Nonlinear Effects
150(5)
8.6 Higher-Order Interactions
155(1)
8.7
Chapter Conclusions
156(1)
9 Inferential Statistics 1: Significance Testing 157(14)
9.1 Introduction
157(2)
9.2 Effect Size: Cohen d
159(2)
9.3 Cohens d in R
161(1)
9.4 Standard Errors and Confidence Intervals
162(3)
9.5 Null Hypotheses
165(1)
9.6 Using t to Measure the Incompatibility with the Null Hypothesis
166(1)
9.7 Using the t-Distribution to Compute p-Values
167(2)
9.8
Chapter Conclusions
169(2)
10 Inferential Statistics 2: Issues in Significance Testing 171(9)
10.1 Common Misinterpretations of p-Values
171(1)
10.2 Statistical Power and Type I, II, M, and S Errors
171(4)
10.3 Multiple Testing
175(2)
10.4 Stopping rules
177(1)
10.5
Chapter Conclusions
178(2)
11 Inferential Statistics 3: Significance Testing in a Regression Context 180(18)
11.1 Introduction
180(1)
11.2 Standard Errors and Confidence Intervals for Regression Coefficients
180(4)
11.3 Significance Tests with Multilevel Categorical Predictors
184(4)
11.4 Another Example: The Absolute Valence of Taste and Smell Words
188(2)
11.5 Communicating Uncertainty for Categorical Predictors
190(4)
11.6 Communicating Uncertainty for Continuous Predictors
194(3)
11.7
Chapter Conclusions
197(1)
12 Generalized Linear Models 1: Logistic Regression 198(20)
12.1 Motivating Generalized Linear Models
198(1)
12.2 Theoretical Background: Data-Generating Processes
198(4)
12.3 The Log Odds Function and Interpreting Logits
202(2)
12.4 Speech Errors and Blood Alcohol Concentration
204(3)
12.5 Predicting the Dative Alternation
207(3)
12.6 Analyzing Gesture Perception
210(6)
12.7
Chapter Conclusions
216(2)
13 Generalized Linear Models 2: Poisson Regression 218(14)
13.1 Motivating Poisson Regression
218(1)
13.2 The Poisson Distribution
218(2)
13.3 Analyzing Linguistic Diversity Using Poisson Regression
220(5)
13.4 Adding Exposure Variables
225(2)
13.5 Negative Binomial Regression for Overdispersed Count Data
227(2)
13.6 Overview and Summary of the Generalized Linear Model Framework
229(1)
13.7
Chapter Conclusions
230(2)
14 Mixed Models 1: Conceptual Introduction 232(13)
14.1 Introduction
232(1)
14.2 The Independence Assumption
232(1)
14.3 Dealing with Non-independence via Experimental Design and Averaging
233(1)
14.4 Mixed Models: Varying Intercepts and Varying Slopes
234(3)
14.5 More on Varying Intercepts and Varying Slopes
237(1)
14.6 Interpreting Random Effects and Random Effect Correlations
238(2)
14.7 Specifying Mixed Effects Models: lme4 syntax
240(1)
14.8 Reasoning About Your Mixed Model: The Importance of Varying Slopes
241(3)
14.9
Chapter Conclusions
244(1)
15 Mixed Models 2: Extended Example, Significance Testing, Convergence Issues 245(29)
15.1 Introduction
245(1)
15.2 Simulating Vowel Durations for a Mixed Model Analysis
245(8)
15.3 Analyzing the Simulated Vowel Durations with Mixed Models
253(2)
15.4 Extracting Information out of lme4 Objects
255(2)
15.5 Messing up the Model
257(3)
15.6 Likelihood Ratio Tests
260(4)
15.7 Remaining Issues
264(3)
15.8 Mixed Logistic Regression: Ugly Selfies
267(4)
15.9 Shrinkage and Individual Differences
271(1)
15.10
Chapter Conclusions
272(2)
16 Outlook and Strategies for Model Building 274(7)
16.1 What You Have Learned So Far
274(1)
16.2 Model Choice
275(1)
16.3 The Cookbook Approach
275(1)
16.4 Stepwise Regression
276(1)
16.5 A Plea for Subjective and Theory-Driven Statistical Modeling
277(2)
16.6 Reproducible Research
279(1)
16.7 Closing Words
280(1)
References 281(9)
Appendix A. Correspondences Between Significance Tests and Linear Models 290(11)
A1 t-Tests
290(5)
A2 Tests for Categorical Data
295(4)
A3 Other Tests
299(2)
Appendix B. Reading Recommendations 301(3)
B1 Book Recommendations
301(1)
B2 Article Recommendations
302(1)
B3 Staying Up-to-Date
303(1)
Index 304(4)
Index of R Functions 308
Bodo Winter is Lecturer in Cognitive Linguistics in the Department of English Language and Applied Linguistics at the University of Birmingham, UK.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97813516774312e.html
Märksõnad: