.

0 (hodnocen0 x )

(2) Půjčeno:2x 

BK

1st ed.

Cambridge : Cambridge University, 1993

xii, 322 s. : il.

ISBN 0-521-27312-9 (brož.)

Cambridge textbooks in linguistics

Obsahuje tabulky, resumé, předmluvu, dodatky, rejstřík

Bibliografie: s. 316-318

Lingvistika matematická - učebnice vysokošk.

Statistika - jazykověda - učebnice vysokošk.

000100352

CONTENTS // Preface // I Why do linguists need statistics? // Page // xi // 2 Tables and graphs 8 // 2.I Categorical data 8 // 2.2 Numerical data 13 // 2.3 Multi-way tables 19 // 2.4 Special cases 20 // Summary 22 // Exercises 23 // 3 // 3-1 // 3.2 // 3-3 // 3-4 // 3-5 // 3.6 // 3-7 // 3.8 // Summary measures // The median // The arithmetic mean // The mean and the median compared // Means of proportions and percentages // Variability or dispersion // Central intervals // The variance and the standard deviation // Standardising test scores // Summary // Exercises // 25 // 27 // 29 // 30 // 34 // 37 // 37 // 40 // 43 // 45 // 46 // 4 // 4.1 // 4.2 // 4-3 // 4.4 // Statistical inference // The problem // Populations // The theoretical solution // The pragmatic solution // Summary // Exercises // 48 // 48 // 49 // 52 // 54 // 57 // 58 // Contents // vi // CH O U1 Ln U1 // • • •• • // O - % N H U // 6 // 6.1 // 6.2 // 6.3 // 6.4 // 7 // 7.1 // 7.2 // 7-3 // 7-4 // 7-5 // 7-5.1 // 7-5.2 // 7-5-3 // 7-5-4 // 7-5-5 // 7.6 // 8 // 8.1 // 8.2 // 8.3 // 8.4 // 00 // Probability // Probability // Statistical independence and conditional probability // Probability and discrete numerical random variables // Probability and continuous random variables // Random sampling and random number tables // Summary // Exercises // Modelling statistical populations // A simple statistical model // The sample mean and the importance of sample size // A model of random variation:

the normal distribution // Using tables of the normal distribution // Summary // Exercises // Estimating from samples // Point estimators for population parameters // Confidence intervals // Estimating a proportion // Confidence intervals based on small samples // Sample size // Central Limit Theorem // When the data are not independent // Confidence intervals // More than one level of sampling // Sample size to obtain a required precision // Different confidence levels // Summary // Exercises // Testing hypotheses about population values // Using the confidence interval to test a hypothesis // The concept of a test statistic // The classical hypothesis test and an example // How to use statistical tests of hypotheses: is significance // significant? // The value of the test statistic is significant at the 1% // level // 59 // 59 // 61 // 66 // 68 // 72 // 75 // 75 // 77 // 77 // 80 // 86 // 89 // 93 // 93 // 95 // 95 // 96 // 99 // 101 // 103 // 103 // 104 // 105 // 106 // 107 // IIO // III // 112 // 113 // 113 // 117 // 120 // 127 // 129 // Contents // // The value of the test statistic is not significant // Summary // Exercises // 288 // 9 // 9.1 // 9.2 // 9.3 // 9.4 // 9.4.1 // 9.4.2 // 9.4-3 // 9-4-4 // 9.4.5 // Testing the fit of models to data // Testing how well a complete model fits the data // Testing how well a type of model fits the data // Testing the model of independence // Problems and pitfalls of the chi-squared test // Small expected frequencies // The 2X2 contingency

table // Independence of the observations // Testing several tables from the same study // The use of percentages // Summary // Exercises // 132 // 132 // 137 // 139 // 144 // 144 // 146 // 147 // 149 // 150 // 151 // 152 // IO // IO.I // 10.2 // 10.3 // 10.4 // 10.5 // 10.6 // 10.7 // Measuring the degree of interdependence between // two variables // The concept of covariance // The correlation coefficient // Testing hypotheses about the correlation coefficient // A confidence interval for a correlation coefficient // Comparing correlations // Interpreting the sample correlation coefficient // Rank correlations // Summary // Exercises // 154 // 154 // 160 // 162 // 163 // 165 // 167 // 169 // 174 // 174 // 11.1 // II.2 // 11.3 // 11.4 // 11.5 // II.6 // Testing for differences between two populations // Independent samples: testing for differences between // means // Independent samples: comparing two variances // Independent samples: comparing two proportions // Paired samples: comparing two means // Relaxing the assumptions of normality and equal variance: nonparametric tests // The power of different tests // Summary // Exercises // 176 // 176 // 182 // 182 // 184 // 188 // 191 // 192 // 193 // • • // vn // Contents // 12 Analysis of variance - ANOVA // 12.1 Comparing several means simultaneously: one-way // ANOVA 194 // 12.2 Two-way ANOVA: randomised blocks 200 // 12.3 Two-way ANOVA: factorial experiments 202 // 12.4 ANOVA: main effects only 206 // 12.5 ANOVA:

factorial experiments 211 // 12.6 Fixed and random effects 212 // 12.7 Test score reliability and ANOVA 215 // 12.8 Further comments on ANOVA 219 // 12.8.1 Transforming the data 220 // 12.8.2 �Within-subject’ ANOVAs 221 // Summary 222 // Exercises 222 // N H O N ONOUNC // Linear regression // The simple linear regression model // Estimating the parameters in a linear regression // The benefits from fitting a linear regression // Testing the significance of a linear regression // Confidence intervals for predicted values // Assumptions made when fitting a linear regression // Extrapolating from linear models // Using more than one independent variable: multiple // regression // Deciding on the number of independent variables // The correlation matrix and partial correlation // Linearising relationships by transforming the data // Generalised linear models // Summary // Exercises // 224 // 226 // 229 // 230 // 233 // 234 // 235 // 237 // 237 // 242 // 244 // 245 // 247 // 247 // 248 // 14 Searching for groups and clusters 249 // 14.1 Multivariate analysis 249 // 14.2 The dissimilarity matrix 252 // 14.3 Hierarchical cluster analysis 254 // 14.4 General remarks about hierarchical clustering 259 // 14.5 Non-hierarchical clustering 261 // 14.6 Multidimensional scaling 262 // 14.7 Further comments on multidimensional scaling 265 // 9 00 // V111 // Contents // 14.8 // 14.9 // 14.IO // Linear discriminant analysis // The linear discriminant function for two groups // Probabilities

of misclassification // Summary // Exercises // 265 // 268 // 269 // 271 // 271 // L H N CO T 1 I 0 // 7 t th t w t w w t // Principal components analysis and factor analysis // Reducing the dimensionality of multivariate data // Principal components analysis // A principal components analysis of language test scores // Deciding on the dimensionality of the data // Interpreting the principal components // Principal components of the correlation matrix // Covariance matrix or correlation matrix? // Factor analysis // Summary // 273 // 273 // 275 // 278 // 282 // 284 // 287 // 287 // 290 // 295 // Appendix A Statistical tables 296 // Appendix B Statistical computation 307 // Appendix C Answers to some of the exercises 314 // References 316 // Index 319