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                    Stevens J.P. — Intermediate Statistics: A Modern Approach 
                  
                
                    
                        
                            
                                
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                                    Название:   Intermediate Statistics: A Modern ApproachАвтор:   Stevens J.P.  Аннотация:  James Stevens’ best-selling text is written for those who use, rather than develop, statistical techniques. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving the results. Definitional formulas are used on small data sets to provide conceptual insight into what is being measured. The assumptions underlying each analysis are emphasized, and the reader is shown how to test the critical assumptions using SPSS or SAS. Printouts with annotations from SAS or SPSS show how to process the data for each analysis. The annotations highlight what the numbers mean and how to interpret the results. Numerical, conceptual, and computer exercises enhance understanding. Answers are provided for half of the exercises.
Язык:  Рубрика:  Математика /Статус предметного указателя:  Готов указатель с номерами страниц ed2k:   ed2k stats Издание:  3rd EditionГод издания:  2007Количество страниц:  460Добавлена в каталог:  14.05.2008Операции:  Положить на полку  |
	 
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                        Actual alpha 57 323 Adelman, H. 230 Agresti, A. 230 275 282 361 391 392 393 Analysis of covariance (Ancova) 286 Analysis of covariance (ANCOVA), adjusted means 289—291 Analysis of covariance (ANCOVA), alternative analyses 305—306 Analysis of covariance (ANCOVA), assumptions 297 300 Analysis of covariance (ANCOVA), by multiple regression 297 Analysis of covariance (ANCOVA), choice of covariates 293 Analysis of covariance (ANCOVA), computer example 304—305 Analysis of covariance (ANCOVA), computer example with 2 covariates       312—314 Analysis of covariance (ANCOVA), covariate by treatment interaction 300 Analysis of covariance (ANCOVA), homogeneity of regression slopes 297—300 306 308 Analysis of covariance (ANCOVA), null hypothesis 294 Analysis of covariance (ANCOVA), numerical example 293—294 Analysis of covariance (ANCOVA), purposes 287—288 Analysis of covariance (ANCOVA), reduction of error variance 288 292—293 Analysis of variance (ANOVA) examples 45 Analysis of variance (ANOVA) examples, assumptions 56 Analysis of variance (ANOVA) examples, computer example on SAS and SPSS with Tukey procedure 62 Analysis of variance (ANOVA) examples, computer example with unequal variances and Games — Howell and Tamhane procedures 77 93 Analysis of variance (ANOVA) examples, expected mean squares 53 Analysis of variance (ANOVA) examples, F test 51 Analysis of variance (ANOVA) examples, linear model 55 Analysis of variance (ANOVA) examples, numerical example between group variation 49—50 Analysis of variance (ANOVA) examples, numerical example within group variation 50—51 Anderson , N.H. 303 Anscombe, F. 228 229 Aptitude by treatment interaction research (ATI) 124 ascii file 39—40 Baker, F 189 Balanced design 136 Barcikowski, R. 61 189 Becker, B. 110 Berry , J. 97 Bloom, B. 59 Bock, R.D. 57 182 212 390 Bonferroni inequality 80—81 Bonferroni inequality, improved Bonferroni type procedure 162—163 Bosker, R. 323 324 343 Bounds, W. 182 317 318 323 Box, G.P. 24 25 168 188 207 208 209 214 Breen, L. 189 Brown, M.B. 73 389 Browne, W. 358 Bryan , T. 174 285 314 317 Bryant, J.L. 285 314 317 Bryk, A.S. 59 303 322 324 325 327 328 329 337 338 339 341 342 343 Burstein, L. 321 Carlson, J. 144 Central limit theorem 57 Chase, C. 21 160 161 329 Cheong, Y.F. 325 329 337 338 Circularity 187 Cochran, W.G. 58 165 287 319 Coggeshall, P. 390 Cohen, J. 3 76 108 109 113 116 118 166 167 236 258 Cohen, P. 76 258 Collier, R. 189 Compact disk 39—40 Compound symmetry 187 Congdon, R. 325 329 337 338 Conservative 58 68 contrast 71 82 Cook, R.D. 47 227 240 246 261 262 263 264 266 273 277 Copenhaver, M.D. 162 Cormier, W. 182 Counterbalancing 185 213 Cradler, J. 146 Cronbach, L. 111 124 150 172 Crowder, R. 255 Crystal, G. 230 Dance, K. 124 315 Daniels, R. 125 145 data files 21 23 Dataset editing 25—28 Davenport , J. 59 323 Davidson, M.L. 189 de Leeuw, J. 323 324 326 342 343 358 Delaney, H.D. 353 Dizney, H. 234 Draper, D. 358 Draper, N. 232 249 250 254 Dudycha, A. 263 265 273 Dummy coding (group membership) 267 270 Dunnett procedure 68 92 Dunnett, C.W. 68 69 92 98 99 Effect size, factorial ANOVA 168 Effect size, one way ANOVA 168 Effect size, t test 168 Elashoff, J. 203 204 217 287 303 Empathy model data 355 Epsilon, Greenhouse — Geisser 215 Epsilon, Huynh — Feldt 189 Eta squared 78 Excel (spreadsheet program) 23 Expected mean squares 53 Factorial analysis of variance, advantages 124 Factorial analysis of variance, balanced design 136 Factorial analysis of variance, four way 160—161 Factorial analysis of variance, interpretation of effects 146 Factorial analysis of variance, numerical example for two way 127—133 Factorial analysis of variance, on SAS and SPSS 153 Factorial analysis of variance, three way 144—157 Factorial analysis of variance, two computer examples 138—142 Factorial analysis of variance, unbalanced design 136—137 Feldt, L. 187 189 215 217 Feshbach, S. 230 Fixed effects 169 Fixed factor 169—170 Forsythe, A.B. 73 Frane, J. 164 Games, P. 41 73 74 77 93 Geisser, S. 188 204 215 217 Glasnapp, D. 222 304 Glass, G. 57 60 79 102 182 Goldstein, H. 358 Goodwin, D. 146 Greenhouse, S. 188 204 215 217 Gromen, L. 234 Hagen, E. 307 Hand, D.J. 141 Harmonic mean 69 Harrington, S. 113 Hayes, T. 189 Hays, W. 76 80 85 Hayter, A. 68 Herzberg, P.A. 239 254 258 Heterogeneous variances and unequal group sizes 73 Hierarchical Linear Modeling (HLM), adding predictors 340 348 Hierarchical Linear Modeling (HLM), data analysis of 329 Hierarchical Linear Modeling (HLM), datasets 330—331 Hierarchical Linear Modeling (HLM), empathy model data 355 Hierarchical Linear Modeling (HLM), estimating parameters 335—338 Hierarchical Linear Modeling (HLM), evaluating efficacy 351 Hierarchical Linear Modeling (HLM), MDM file 331—335 Hierarchical Linear Modeling (HLM), multilevel data, single-level analysis 323 Hierarchical Linear Modeling (HLM), multilevel model, formulation of 325 Hierarchical Linear Modeling (HLM), two-level example 329 Hierarchical Linear Modeling (HLM), two-level model, formulation of 325—328 Hierarchical Linear Modeling (HLM), two-level unconditional model 335 Higher order designs see “Factorial ANOVA” HLM software output 338—339 344—345 347—348 356—357 HLM6 329 Hoaglin, D. 261 Holland, B.S. 162 Holm, S. 162 163 Homogeneity of variance assumption statistical tests for 58 Hopkins, K. 60 79 102 182 Howell, J.K. 73 74 77 93 Hox, J.J. 323 324 328 348 359 Huberty, C.J. 68 238 Huck, S. 182 305 307 314 317 318 Huitema, B. 287 293 304 308 310 Huynh, H. 187 189 215 217 Hyman, R. 173 Importing datasets 23 Independence of observations 59 Influential points 227 Interaction, disordinal 125 Interaction, ordinal 125 Intraclass correlation 59 323—324 Jennings, E. 305 314 Johnson, P.O. 145 Johnson, R. 164 Johnson—Neyman technique 287 303 308 Jones, L.V. 40 95 153 246 390 Judd, C. 61 321 322 Kaiser, M. 189 Kenny, D. 61 321 322 Keppel, G. 183 185 Kerlinger, F. 76 Keselman, H.J. 189 197 201 Keselman, J.C. 197 201 Kirk, R. 76 318 Kreft, I. 321 323 324 326 342 343 358 Lepine, D. 187 level of significance 47 Levin, J. 97 98 Liberal 58 Light, R. 109 Lindzey, G. 246 390 Littell, R.C. 358 Locus of control 16 Longford, N.T. 358 Lord, F.M. 236 303 Lotus 1-2-3 (spreadsheet program) 23 Main effects 128 130 Mallows, C.L. 238 240 247 263 272 278 Mandeville, C.K 189 Marwit, S. 145 Maxwell, S.E. 193 194 201 353 McCabe, G. 250 McCormick, C. 97 McLean, R.A. 305 307 314 Measures of association 75 Mendoza, J. 189 Merging files 28 Miller, G. 97 Milliken, G.A. 358 Missing data 31 Moore, D. 250 Morrison, D.F. 239 240 241 244 250 259 261 262 264 282 Multiple regression, ANOVA as a special case of regression analysis 265 Multiple regression, computer examples 239 Multiple regression, controlling order with SAS and SPSS 257 Multiple regression, examples of 230 Multiple regression, Mallow’s        238 Multiple regression, mathematical maximization procedure 231 Multiple regression, MAXR (from SAS) 246 Multiple regression, model selection procedures, multicollinearity 234 Multiple regression, model selection procedures, multiple correlation 231 233 Multiple regression, model selection procedures, substantive knowledge 236 Multiple regression, model selection procedures, variance inflation factor 235 Multiple regression, model validation, adjusted        254 Multiple regression, model validation, data splitting 239 252—253 Multiple regression, model validation, Press statistic 283 Multiple regression, order of predictors 255 Multiple regression, positive bias of        258 Multiple regression, preselection of predictors 257 Multiple regression, sample size (for a reliable prediction equation) 258 Multiple regression, sequential procedures, backward selection 237 Multiple regression, sequential procedures, forward selection 237 Multiple regression, stepwise 237 Multivariate analysis of variance (Manova) 89 Murray, R.M. 69 Myers, J. 144 175 185 195 198 293 294 295 297 298 301 Myers, R. 233 235 238 277 283 Neufeld, R. 124 Neumann, G. 145 Neyman, J. 285 287 300 308 311 314 315 Normality 9 187 Notebook computer 16 Novick, M. 236 Novince, L. 315 Nunnally, J. 231 257 Omega squared 76 Orthogonal comparisons 83 Outliers 12 Outliers in regression analysis 260 Outliers, detecting 13 Outliers, effect on correlation 14 Output navigator (SPSS) 31 Overall alpha 197 Overall, J. 138 O’Brien, R. 189 O’Grady, K. 76 79 96 p values 67—68 Park, C. 263 265 273 Partial correlation 237 Partial eta squared 116 Paulson, A.S. 285 314 317 Peckham, P. 57 Pedhazur, E. 76 144 304 Pillimer, D. 109 Planned comparisons 79—87 212 Planned comparisons on SAS and SPSS 87 Planned comparisons, test statistic 84—85 Platykurtosis 57 Plewis, I. 358 Poggio, J. 222 304 Porter, A. 304 Power on SPSS MANOVA 116 Power, a priori determination of sample size 111 Power, factors dependent on 106 Power, post hoc estimation of 111 Power, ways of improving 115 Presley, M. 97 Pukulski, J. 172 Random factor 169—170 Rasbash, J. 358 Raudenbush, S.W. 322 324 325 327 328 329 337 338 339 341 342 343 358 Regression, multiple see “Multiple regression” Regression,simple 219—225 Reichardt, C.S. 303 Repeated measures analysis, advantages and disadvantages 184—185 Repeated measures analysis, advantages and disadvantages, single group, univariate approach 186 Repeated measures analysis, assumptions 187 Repeated measures analysis, computer analysis on SAS and SPSS 190 Repeated measures analysis, one between and one within (trend analysis) 194 Repeated measures analysis, one between and two within 203—208 210 Repeated measures analysis, planned comparisons 212 Repeated measures analysis, SPSS syntax setup for Helmert contrasts 212 Repeated measures analysis, totally within designs 209 211 Repeated measures analysis, univariate and multivariate approaches compared 189 Residual plots 250 Robey, R. 189 Robust 57 Rogan, J. 69 189 Rogosa, D. 300 Rosenthal, R. 79 Rosnow, R. 79 Rounet, H. 187 Sample variance 2 Sanders, J. 57 SAS (selected printouts), ANCOVA 295—296 SAS (selected printouts), MAXR regression 248 SAS (selected printouts), one between and two within repeated measures 206 SAS (selected printouts), one way ANOVA 64 SAS (selected printouts), planned comparisons 91 
                            
                     
                  
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