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Afifi A.A., Clark V. — Computer-Aided Multivariate Analysis
Îáñóäèòå êíèãó íà íàó÷íîì ôîðóìå Íàøëè îïå÷àòêó?
Íàçâàíèå: Computer-Aided Multivariate AnalysisÀâòîðû: Afifi A.A., Clark V. Àííîòàöèÿ: Increasingly, researchers need to perform multivariate statisticalanalyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of these advanced techniques, in part, because books focus on the theory and neglect to explain how to perform and interpret multivariate analyses on real-life data.
ßçûê: Ðóáðèêà: Computer science /Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö ed2k: ed2k stats Èçäàíèå: Third EditionÃîä èçäàíèÿ: 1996Êîëè÷åñòâî ñòðàíèö: 455Äîáàâëåíà â êàòàëîã: 09.12.2009Îïåðàöèè: Ïîëîæèòü íà ïîëêó |
Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
Ïðåäìåòíûé óêàçàòåëü
Multiple regression, descriptive 142 167 Multiple regression, dummy variables 202—209 Multiple regression, Durbin — Watson statistic 145 Multiple regression, fixed Zcase 128—130 137—143 Multiple regression, forcing variables 181 Multiple regression, forward selection 175—178 Multiple regression, general F test 153 173—175 Multiple regression, hat matrix 144 Multiple regression, hyperellipsoid 132 Multiple regression, indicator variables 202—209 Multiple regression, interactions 146—147 207—208 Multiple regression, least squares method 129 Multiple regression, leverage 144 Multiple regression, linear constraints on parameters 209—212 Multiple regression, maximum F-to-remove 178—179 180 Multiple regression, minimum F-to-enter 176—177 180 Multiple regression, missing values 197—202 Multiple regression, multicollinearity 149 212—219 345—347 Multiple regression, multiple correlation 134—135 142—143 171 Multiple regression, normal probability plots 145 Multiple regression, normality assumption 130 132 134 145 Multiple regression, outliers 144 Multiple regression, partial correlation 135—137 143 Multiple regression, partial regression coefficient 128—129 Multiple regression, partial regression plots 189—190 Multiple regression, partial residual plots 190 Multiple regression, polynomial regression 146—147 Multiple regression, prediction intervals 130 Multiple regression, predictive 167 173 Multiple regression, principal components 345—347 Multiple regression, reference group 203—205 Multiple regression, regression plane 126—127 Multiple regression, regression through origin 148 Multiple regression, residual mean square 129 172 Multiple regression, residual sums of squares 139 197 Multiple regression, residuals 144—145 148 Multiple regression, ridge regression 214—219 Multiple regression, ridge trace 217 Multiple regression, RSQUARE 139 Multiple regression, segmented curve regression 210—212 Multiple regression, serial correlation 145 Multiple regression, spline regression 210—212 Multiple regression, stagewise regression 190—191 Multiple regression, standard error of estimate 129 Multiple regression, standardized coefficients 141—142 213 Multiple regression, stepwise selection 175—181 Multiple regression, stopping rule 176—177 Multiple regression, subset regression 181—184 Multiple regression, testing regression planes 150—151 Multiple regression, tests of 139 Multiple regression, tolerance 149 Multiple regression, transformations 145—146 Multiple regression, variable selection 166—193 Multiple regression, variable-X case 130—137 140—143 Multiple regression, variance inflation factor 149 Multiple regression, weighted regression 148 Multiple regression, what to watch for 157—160 191—192 Multivariate analysis, definition 3 Multiway frequency tables 411 412—414 421—437 N number of cases, sampling units 13 Nominal variables 14—15 72—74 Normal distribution 54—55 131 Normal probability plots 57—58 108 Normality 57—58 62—63 108 Normality, normal probability plots 57—58 108 Normality, tests 62—63 Odds 284 Odds ratio 286—287 421 Ordinal variables 15 74 Outliers 38—39 104—108 144—145 331 347 376 389 Outliers in X 106—107 144 Outliers in Y 105—106 144 P number of variables 13 Package programs 23—28 Paired data 115 Partial regression plots 189 Partial residual plots 190 Percentiles 74 Poisson distribution 59 Polynomial regression 146—147 Posterior probabilities 258—259 Power transformations 53 59—62 Prediction intervals 93 130 Principal components 8 330—353 Principal components, characteristic roots 335 Principal components, coefficients 333—335 Principal components, computer programs 348—350 Principal components, correlation 338—339 Principal components, cumulative percentage variance 341—343 Principal components, definition 333 Principal components, eigenvalues 335—336 341—343 Principal components, eigenvector 336 Principal components, ellipse of concentration 335 Principal components, interpretation of coefficients 334—335 344 Principal components, latent root 335 Principal components, multicollinearity 345—347 Principal components, normality 331 Principal components, number of components 337 341—344 Principal components, outliers 331 347 Principal components, reduction of dimensionality 337 Principal components, regression analysis 345—347 Principal components, scree plots 337 Principal components, shape component 345 Principal components, size component 345 Principal components, standardized x 338—340 Principal components, tests of hypotheses 345 Principal components, variance of components 334 Principal components, what to watch for 350—354 Quartile deviation 74 RANGE 74 Ratio variables 16 75 Reflected variables 41 Regression 85—224 Regression, multiple linear 6 124—224 Regression, polynomial 146—147 Regression, principal component 345—347 Regression, ridge 214—219 Regression, segmented-curve 210—212 Regression, simple linear 6 85—123 Regression, spline 210—212 Regression, stagewise 190—191 Replacing missing data 199—201 Residuals 91 104—105 108 144—145 148 Ridge regression 214—219 ROC curves 295—296 Rotated factors 365—371 Saving data 40 Scale parameters 314 Segmented-curve regression 210—212 Selecting appropriate analysis 71—79 Serial correlation 108 145 Shape parameter 314 Shapiro — Wilk test 62 simple linear regression 6 85—123 Simple linear regression, adjusted Y 115 Simple linear regression, analysis of variance 101 Simple linear regression, assumptions 88—89 108—109 Simple linear regression, calibration 113—114 Simple linear regression, computer programs 115—116 Simple linear regression, confidence intervals 92 Simple linear regression, Cook's distance 107 Simple linear regression, correlation 94 97—99 Simple linear regression, covariance 93 Simple linear regression, descriptive 86 Simple linear regression, Durbin — Watson statistic 108 Simple linear regression, ellipse of concentration 96—98 Simple linear regression, fixed-X case 86 88—93 94—96 Simple linear regression, forecasting 114 Simple linear regression, h statistic 105—106 Simple linear regression, influence of observation 107 Simple linear regression, intercept 89—90 Simple linear regression, leverage 105—106 Simple linear regression, linearity 109 Simple linear regression, outliers 105—108 Simple linear regression, prediction intervals 93 Simple linear regression, predictive 86 Simple linear regression, regression line 88 Simple linear regression, regression through origin 111—112 Simple linear regression, residual analysis 102—106 Simple linear regression, residual degrees of freedom 91 Simple linear regression, residual mean square 91—92 Simple linear regression, residuals 91 103—104 115 Simple linear regression, robustness 108—109 Simple linear regression, serial correlation 108 Simple linear regression, slope 89 Simple linear regression, standard error 92 95 Simple linear regression, standardized coefficients 100—101 Simple linear regression, standardized residuals 105 Simple linear regression, studentized residuals 105 Simple linear regression, tests of hypotheses 94—95 101 Simple linear regression, transformations 109—111 Simple linear regression, variable-X case 86 93—94 96—100 Simple linear regression, weighted least squares 112 Simple linear regression, what to watch for 117—118 skewness 63 Spline regression 210—212 Square root transformation 53 59 Stagewise regression 190—191 Standardized regression coefficients 100—101 141 213 Standardized residuals 105 Stepwise selection 175—181 274—275 290—291 429—430 Stevens's classification system 13—14 72—78 Stevens's classification system, selecting analysis 72—78 Studentized residuals 105 Subset regression 181—184 Suggested analyses 76—78 Survival analysis 7—8 306—329 Survival analysis, accelerated life model 317—319 Survival analysis, computer programs 324—326 Survival analysis, Cox versus log-linear regression 320—322 Survival analysis, Cox versus logistic regression 322—324 Survival analysis, Cox's proportional hazards model 319—320 Survival analysis, cumulative death distribution function 311—313 Survival analysis, death density function 311—312 Survival analysis, exponential distribution 314—316 Survival analysis, hazard function 312 314—315 Survival analysis, log-linear regression model 317—319 Survival analysis, log-linear versus Cox's model 320—322 Survival analysis, proportional hazards model 319 Survival analysis, scale parameter 314 Survival analysis, shape parameter 314 Survival analysis, survival function 311—314 Survival analysis, T-year survival rate 308—309 Survival analysis, Weibull distribution 314—317 Survival analysis, what to watch for 326—327 Testing regression planes 138 150—153 Tolerance 149 Transformations 39—40 48—64 109—111 145—146 Transformations in multiple regression 145—6 Transformations in simple linear regression 109—111 Transformations, assessing need 63—64 Transformations, common transformations 48—53 109—111 Transformations, data manipulation 39—40 Transformations, graphical methods 54—62 109—111 Transformations, logarithmic 49—53 111 Transformations, normal probability plots 55—58 Transformations, power 53 59—62 Transformations, square root 50 53 Variable definition 12 Variance inflation factor 149 Weighted least squares 112 148
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