statisticalrethinkin..

More documents

Recommendations

Info

108 4. LINEAR MODELSdata(Howell1)d
4.4. ADDING A PREDICTOR 109height ~ dnorm( a + b*weight , sigma ) ,a ~ dnorm( 156 , 100 ) ,b ~ dnorm( 0 , 10 ) ,sigma ~ dunif( 0 , 50 )) ,data=d2 ,start=list( a=mean(d2$height) , sigma=sd(d2$height) , b=0 ) )is is exactly the same model, but with the linear definition for µ embedded in the first line of code.e form with the explicit linear model, mu
Page 1:
Statistical RethinkingA BAYESIAN CO
Page 4 and 5:
4 CONTENTS5.5. Ordinary least squar
Page 7 and 8:
PrefaceMasons, when they start upon
Page 9 and 10:
HOW TO USE THIS BOOK 9least a minor
Page 11 and 12:
HOW TO USE THIS BOOK 11than initial
Page 13 and 14:
1 e Golem of PragueIn the 16th cent
Page 16:
16 1. THE GOLEM OF PRAGUEconverses
Page 19 and 20:
1.2. WRECKING PRAGUE 19model P 1B ,
Page 21 and 22:
1.2. WRECKING PRAGUE 21e dominant r
Page 23 and 24:
1.3. THREE TOOLS FOR GOLEM ENGINEER
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
2 Small Worlds and Large WorldsWhen
Page 31 and 32:
2.1. PROBABILITY IS JUST COUNTING 3
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
2.2. COLOMBO’S FIRST BAYESIAN MOD
Page 39 and 40:
W L W W W L W L W2.2. COLOMBO’S F
Page 41 and 42:
2.3. COMPONENTS OF THE MODEL 41not
Page 43 and 44:
2.3. COMPONENTS OF THE MODEL 43You
Page 45 and 46:
2.3. COMPONENTS OF THE MODEL 45e ma
Page 47 and 48:
2.4. MAKING THE MODEL GO 47other pr
Page 49 and 50:
2.4. MAKING THE MODEL GO 495 points
Page 51 and 52:
2.4. MAKING THE MODEL GO 51just the
Page 53 and 54:
2.4. MAKING THE MODEL GO 53between
Page 55 and 56:
2.6. PRACTICE 55Medium.2.6.5. m1. R
Page 57 and 58: 2.6. PRACTICE 57implied by the equa
Page 59 and 60: 3 Sampling the ImaginaryLots of boo
Page 61 and 62: 3. SAMPLING THE IMAGINARY 61a probl
Page 63 and 64: 3.2. SAMPLING TO SUMMARIZE 63plot(
Page 65 and 66: 3.2. SAMPLING TO SUMMARIZE 65Densit
Page 67 and 68: 3.2. SAMPLING TO SUMMARIZE 67In con
Page 69 and 70: 3.2. SAMPLING TO SUMMARIZE 69Densit
Page 71 and 72: 3.3. SAMPLING TO SIMULATE PREDICTIO
Page 79 and 80: 3.5. PRACTICE 793.5. PracticeEasy.
Page 81: 3.5. PRACTICE 813.5.17. Predict sec
Page 84 and 85: 84 4. LINEAR MODELSFIGURE 4.1. e Pt
Page 86 and 87: 86 4. LINEAR MODELSexperiment with
Page 88 and 89: 88 4. LINEAR MODELS4.1.4.2. Epistem
Page 90 and 91: 90 4. LINEAR MODELSe approach above
Page 92 and 93: 92 4. LINEAR MODELS'data.frame': 54
Page 94 and 95: 94 4. LINEAR MODELSe point isn’t
Page 96 and 97: 96 4. LINEAR MODELShave the samples
Page 98 and 99: 98 4. LINEAR MODELSere’s no troub
Page 100 and 101: 100 4. LINEAR MODELS)Note the comma
Page 102 and 103: 102 4. LINEAR MODELSpreviously obse
Page 104 and 105: 104 4. LINEAR MODELS4.4. Adding a p
Page 106 and 107: 106 4. LINEAR MODELS(1) What is the
Page 110 and 111: 110 4. LINEAR MODELSRethinking: Wha
Page 112 and 113: 112 4. LINEAR MODELSheight140 150 1
Page 116 and 117: 116 4. LINEAR MODELSYou end up with
Page 118 and 119: 118 4. LINEAR MODELS(1) Use link to
Page 122 and 123: 122 4. LINEAR MODELSRethinking: Lin
Page 126 and 127: 126 4. LINEAR MODELS4.7.1. e1. In t
Page 129 and 130: 5 Multivariate Linear ModelsOne of
Page 131 and 132: 5.1. SPURIOUS ASSOCIATION 131Divorc
Page 133 and 134: 5.1. SPURIOUS ASSOCIATION 133But me
Page 135 and 136: 5.1. SPURIOUS ASSOCIATION 135abmbas
Page 137 and 138: 5.1. SPURIOUS ASSOCIATION 137this i
Page 139 and 140: 5.1. SPURIOUS ASSOCIATION 139slower
Page 141 and 142: 5.1. SPURIOUS ASSOCIATION 141Median
Page 143 and 144: 5.1. SPURIOUS ASSOCIATION 143(a)(b)
Page 145 and 146: 5.2. MASKED RELATIONSHIP 145N
Page 147 and 148: 5.2. MASKED RELATIONSHIP 147a ~ dno
Page 149 and 150: 5.2. MASKED RELATIONSHIP 149dcc$log
Page 151 and 152: 5.3. WHEN ADDING VARIABLES HURTS 15
Page 159 and 160:
5.3. WHEN ADDING VARIABLES HURTS 15
Page 161 and 162:
5.4. CATEGORICAL VARIABLES 161$ wei
Page 163 and 164:
5.4. CATEGORICAL VARIABLES 1635.4.2
Page 165:
5.4. CATEGORICAL VARIABLES 165# sam
Page 168 and 169:
168 5. MULTIVARIATE LINEAR MODELS5.
Page 170 and 171:
170 5. MULTIVARIATE LINEAR MODELS5.
Page 173 and 174:
6 Model Selection, Comparison, and
Page 175 and 176:
6.1. THE PROBLEM WITH PARAMETERS 17
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
6.2. INFORMATION THEORY AND MODEL P
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
6.3. AKAIKE INFORMATION CRITERION 1
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
6.4. DEVIANCE INFORMATION CRITERION
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
6.5. USING AIC 203helps guard again
Page 205 and 206:
6.5. USING AIC 205compare( m6.11 ,
Page 207 and 208:
6.5. USING AIC 207e attitude this b
Page 209 and 210:
6.5. USING AIC 209kcal.per.g0.5 0.7
Page 211 and 212:
6.7. PRACTICE 211Consider by analog
Page 213:
6.7. PRACTICE 2136.7.3. e deviance
Page 216 and 217:
216 7. INTERACTIONSFIGURE 7.1. TOP:
Page 218 and 219:
218 7. INTERACTIONSlog(rgdppc_2000)
Page 220 and 221:
220 7. INTERACTIONSird, we may want
Page 222 and 223:
222 7. INTERACTIONSlog GDP year 200
Page 224 and 225:
Page 226 and 227:
226 7. INTERACTIONSInteraction mode
Page 228 and 229:
228 7. INTERACTIONSthis model and t
Page 230 and 231:
Page 232 and 233:
232 7. INTERACTIONSe main effect li
Page 234 and 235:
234 7. INTERACTIONSbs -38.91 34.94s
Page 236 and 237:
236 7. INTERACTIONSe primary reason
Page 238 and 239:
238 7. INTERACTIONSNow for the plot
Page 240 and 241:
240 7. INTERACTIONS7.4. Higher-orde
Page 243 and 244:
8 Markov Chain Monte Carlo Estimati
Page 245 and 246:
8.1. GOOD KING MARKOV AND HIS ISLAN
Page 247 and 248:
8.2. MARKOV CHAIN MONTE CARLO 247fo
Page 249 and 250:
8.2. MARKOV CHAIN MONTE CARLO 249No
Page 251 and 252:
8.3. EASY HMC: MAP2STAN 251We’re
Page 253 and 254:
8.3. EASY HMC: MAP2STAN 253$ cont_a
Page 255 and 256:
8.3. EASY HMC: MAP2STAN 255mcmcpair
Page 257 and 258:
8.3. EASY HMC: MAP2STAN 257FIGURE 8
Page 259 and 260:
8.4. CARE AND FEEDING OF YOUR MARKO
Page 261 and 262:
Page 263 and 264:
Page 265:
8.6. PRACTICE 265And since the chai
Page 268 and 269:
268 9. BIG ENTROPY AND THE GENERALI
Page 270 and 271:
270 9. BIG ENTROPY AND THE GENERALI
Page 273 and 274:
10 Distance and DurationA curious t
Page 275:
10.2. GAMMA 275can be quite complic
Page 278 and 279:
278 11. COUNTING AND CLASSIFICATION
Page 280 and 281:
Page 282 and 283:
Page 284 and 285:
Page 286 and 287:
Page 288 and 289:
Page 290 and 291:
Page 292 and 293:
Page 294 and 295:
Page 296 and 297:
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
304 12. MONSTERS AND MIXTUREShere?
Page 306 and 307:
306 12. MONSTERS AND MIXTURESdensit
Page 308 and 309:
308 12. MONSTERS AND MIXTURESfootbr
Page 310 and 311:
310 12. MONSTERS AND MIXTURESWhy di
Page 312 and 313:
312 12. MONSTERS AND MIXTURESm11.3
Page 314 and 315:
314 12. MONSTERS AND MIXTURESResult
Page 316 and 317:
316 12. MONSTERS AND MIXTURESanswer
Page 318 and 319:
318 12. MONSTERS AND MIXTURESDensit
Page 320 and 321:
320 12. MONSTERS AND MIXTURESBut we
Page 322 and 323:
322 12. MONSTERS AND MIXTURESdisper
Page 324 and 325:
324 12. MONSTERS AND MIXTURESe resu
Page 326 and 327:
326 12. MONSTERS AND MIXTURESDensit
Page 328 and 329:
328 12. MONSTERS AND MIXTURESR code
Page 330 and 331:
330 12. MONSTERS AND MIXTURESPoisso
Page 332 and 333:
332 12. MONSTERS AND MIXTURESthe ga
Page 334 and 335:
334 12. MONSTERS AND MIXTURESdata=d
Page 336 and 337:
336 12. MONSTERS AND MIXTURESma2 ~
Page 338 and 339:
338 13. MULTILEVEL MODELSrepeat obs
Page 340 and 341:
340 13. MULTILEVEL MODELSSo how do
Page 342 and 343:
342 13. MULTILEVEL MODELSprobabilit
Page 344 and 345:
344 13. MULTILEVEL MODELSa conseque
Page 346 and 347:
346 13. MULTILEVEL MODELSCompute th
Page 348 and 349:
348 13. MULTILEVEL MODELSabsolute e
Page 350 and 351:
350 13. MULTILEVEL MODELS)sigma_act
Page 353 and 354:
14 Multilevel Models II: Slopes14.1
Page 355 and 356:
14.1. EVERYTHING CAN VARY AND PROBA
Page 357:
14.1. EVERYTHING CAN VARY AND PROBA
Page 360 and 361:
360 15. MISSING DATA AND OTHER OPPO
Page 362 and 363:
Page 364 and 365:
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
Page 379:
16 Writing Statistics379
Page 382 and 383:
382 ENDNOTES12. For an autopsy of t
Page 384 and 385:
384 ENDNOTES45. Fisher (1925), in C
Page 386 and 387:
386 ENDNOTES77. See two famous edit
Page 388 and 389:
388 ENDNOTES113. Hurlbert (1984) is
Page 390 and 391:
390 BibliographyFrank, S. A. (2011)
Page 392 and 393:
392 BibliographyProulx, S. R. and A
Page 395 and 396:
IndexAkaike (1973), 380, 383Akaike
Page 397:
INDEX 397non-identifiability, 153Oc
show all

statisticalrethinkin..

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?