Analyzing Linguistic Data: A Practical Introduction to Statistics using RStatistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data. |
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Analyzing Linguistic Data: A Practical Introduction to Statistics Using R R. Harald Baayen No preview available - 2008 |
Analyzing Linguistic Data: A Practical Introduction to Statistics Using R R. H. Baayen No preview available - 2008 |
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1|Subject Adjusted R-squared Age=young WrittenFreq AgeGroup AgeSubject anova binomial boxplot Chisq Chi Df Coefficients column ConsonantType Cook's distance correlation covariance cross-validation data frame data set Density Design package Df Pr(>Chisq English Error t value Estimate Std EtymAge F-statistic F-tests Factor Chi-Square d.f. Figure A.6 finalDevoicing follow a Poisson Goodness-of-fit multivariate chi-squared hare Intercept Kolmogorov-Smirnov test lambda lexdec3 lexdec3.lmerE1 lexical linear linear model lm(y lme4 package lmer(y log frequency LogFrequency logistic regression logistic regression model MeanFamiliarity meanWeight mixed-effects Multiple R-Squared multivariate chi-squared test N(nessw.spc naming latencies naming.ols NcountStem Nonlinear Nsyll NVratio Obstruent ols(y Onset2Type outliers overfitting p-value panel par(mfrow partial effects plot Poisson distribution ppois predictors Q-Q Plot quantile-quantile plots R-squared random effects rcs(WrittenFrequency Residual standard error sample quantiles scatterplot adds scatterplot matrix significant Slope Theoretical Quantiles two-tailed tests value Pr(>|t vector verbs voiced voiceless VowelType warlpiri WordOrder words WrittenFrequency WrittenSpokenRatio X2 df xlim xtabs ylab ylim