Главная    Ex Libris    Книги    Журналы    Статьи    Серии    Каталог    Wanted    Загрузка    ХудЛит    Справка    Поиск по индексам    Поиск    Форум   
blank
Авторизация

       
blank
Поиск по указателям

blank
blank
blank
Красота
blank
Stevens J.P. — Intermediate Statistics: A Modern Approach
Stevens J.P. — Intermediate Statistics: A Modern Approach



Обсудите книгу на научном форуме



Нашли опечатку?
Выделите ее мышкой и нажмите Ctrl+Enter


Название: Intermediate Statistics: A Modern Approach

Автор: 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.

The book offers comprehensive coverage of one-way, power, and factorial analysis of variance, repeated measures analysis, simple and multiple regression, analysis of covariance, and HLM. Power analysis is an integral part of the book. A computer example of real data integrates many of the concepts. Highlights of the Third Edition include:*a new chapter on hierarchical linear modeling using HLM6;
*a CD containing all of the book’s data sets;
*new coverage of how to cross validate multiple regression results with SPSS and a new section on model selection (Ch. 6);
*more exercises in each chapter. Intended for intermediate statistics or statistics II courses taught in departments of psychology, education, business, and other social and behavioral sciences, a prerequisite of introductory statistics is required. An Instructor’s Solutions CD is available to adopters.


Язык: en

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: 3rd Edition

Год издания: 2007

Количество страниц: 460

Добавлена в каталог: 14.05.2008

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
blank
Предметный указатель
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 $C_p$      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 $\mathbb{R}^2$      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 $\mathbb{R}^2$      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
1 2
blank
Реклама
blank
blank
HR
@Mail.ru
       © Электронная библиотека попечительского совета мехмата МГУ, 2004-2024
Электронная библиотека мехмата МГУ | Valid HTML 4.01! | Valid CSS! О проекте