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80 3. SAMPLING THE IMAGINARYHard. e practice problems here all use the data below. ese data indicate the gender(male=1, female=0) of officially reported first and second born children in 100 two-childfamilies. ese data come from a certain country in which families are restricted to twolegal children.R code3.28birth1
3.5. PRACTICE 813.5.17. Predict second borns with sisters. e model assumes that sex of first and secondbirths are independent. To check this assumption, focus now on second births that followedfemale first borns. Compare 10-thousand simulated counts of boys to only those secondbirths that followed girls. To do this correctly, you need to count the number of first bornswho were girls and simulate that many births, 10-thousand times. Compare the counts ofboys in your simulations to the actual observed count of boys following girls. How does themodel look in this light? Any guesses what is going on in these data?
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Statistical RethinkingA BAYESIAN CO
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4 CONTENTS5.5. Ordinary least squar
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PrefaceMasons, when they start upon
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HOW TO USE THIS BOOK 9least a minor
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HOW TO USE THIS BOOK 11than initial
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1 e Golem of PragueIn the 16th cent
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16 1. THE GOLEM OF PRAGUEconverses
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1.2. WRECKING PRAGUE 19model P 1B ,
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1.2. WRECKING PRAGUE 21e dominant r
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1.3. THREE TOOLS FOR GOLEM ENGINEER
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Page 29 and 30: 2 Small Worlds and Large WorldsWhen
Page 31 and 32: 2.1. PROBABILITY IS JUST COUNTING 3
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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
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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
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Page 71 and 72: 3.3. SAMPLING TO SIMULATE PREDICTIO
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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
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Page 100 and 101: 100 4. LINEAR MODELS)Note the comma
Page 102 and 103: 102 4. LINEAR MODELSpreviously obse
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Page 108 and 109: 108 4. LINEAR MODELSdata(Howell1)d
Page 110 and 111: 110 4. LINEAR MODELSRethinking: Wha
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Page 126 and 127: 126 4. LINEAR MODELS4.7.1. e1. In t
Page 129 and 130: 5 Multivariate Linear ModelsOne of
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5.1. SPURIOUS ASSOCIATION 131Divorc
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5.1. SPURIOUS ASSOCIATION 133But me
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5.1. SPURIOUS ASSOCIATION 135abmbas
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5.1. SPURIOUS ASSOCIATION 137this i
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5.1. SPURIOUS ASSOCIATION 139slower
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5.1. SPURIOUS ASSOCIATION 141Median
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5.1. SPURIOUS ASSOCIATION 143(a)(b)
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5.2. MASKED RELATIONSHIP 145N
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5.2. MASKED RELATIONSHIP 147a ~ dno
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5.2. MASKED RELATIONSHIP 149dcc$log
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5.3. WHEN ADDING VARIABLES HURTS 15
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5.4. CATEGORICAL VARIABLES 161$ wei
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5.4. CATEGORICAL VARIABLES 1635.4.2
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5.4. CATEGORICAL VARIABLES 165# sam
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168 5. MULTIVARIATE LINEAR MODELS5.
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170 5. MULTIVARIATE LINEAR MODELS5.
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6 Model Selection, Comparison, and
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6.1. THE PROBLEM WITH PARAMETERS 17
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6.2. INFORMATION THEORY AND MODEL P
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6.3. AKAIKE INFORMATION CRITERION 1
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6.4. DEVIANCE INFORMATION CRITERION
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6.5. USING AIC 203helps guard again
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6.5. USING AIC 205compare( m6.11 ,
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6.5. USING AIC 207e attitude this b
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6.5. USING AIC 209kcal.per.g0.5 0.7
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6.7. PRACTICE 211Consider by analog
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6.7. PRACTICE 2136.7.3. e deviance
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216 7. INTERACTIONSFIGURE 7.1. TOP:
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218 7. INTERACTIONSlog(rgdppc_2000)
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220 7. INTERACTIONSird, we may want
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222 7. INTERACTIONSlog GDP year 200
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226 7. INTERACTIONSInteraction mode
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228 7. INTERACTIONSthis model and t
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232 7. INTERACTIONSe main effect li
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234 7. INTERACTIONSbs -38.91 34.94s
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236 7. INTERACTIONSe primary reason
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238 7. INTERACTIONSNow for the plot
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240 7. INTERACTIONS7.4. Higher-orde
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8 Markov Chain Monte Carlo Estimati
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8.1. GOOD KING MARKOV AND HIS ISLAN
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8.2. MARKOV CHAIN MONTE CARLO 247fo
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8.2. MARKOV CHAIN MONTE CARLO 249No
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8.3. EASY HMC: MAP2STAN 251We’re
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8.3. EASY HMC: MAP2STAN 253$ cont_a
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8.3. EASY HMC: MAP2STAN 255mcmcpair
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8.3. EASY HMC: MAP2STAN 257FIGURE 8
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8.4. CARE AND FEEDING OF YOUR MARKO
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8.6. PRACTICE 265And since the chai
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268 9. BIG ENTROPY AND THE GENERALI
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270 9. BIG ENTROPY AND THE GENERALI
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10 Distance and DurationA curious t
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10.2. GAMMA 275can be quite complic
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278 11. COUNTING AND CLASSIFICATION
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304 12. MONSTERS AND MIXTUREShere?
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306 12. MONSTERS AND MIXTURESdensit
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308 12. MONSTERS AND MIXTURESfootbr
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310 12. MONSTERS AND MIXTURESWhy di
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312 12. MONSTERS AND MIXTURESm11.3
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314 12. MONSTERS AND MIXTURESResult
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316 12. MONSTERS AND MIXTURESanswer
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318 12. MONSTERS AND MIXTURESDensit
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320 12. MONSTERS AND MIXTURESBut we
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322 12. MONSTERS AND MIXTURESdisper
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324 12. MONSTERS AND MIXTURESe resu
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326 12. MONSTERS AND MIXTURESDensit
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328 12. MONSTERS AND MIXTURESR code
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330 12. MONSTERS AND MIXTURESPoisso
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332 12. MONSTERS AND MIXTURESthe ga
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334 12. MONSTERS AND MIXTURESdata=d
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336 12. MONSTERS AND MIXTURESma2 ~
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338 13. MULTILEVEL MODELSrepeat obs
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340 13. MULTILEVEL MODELSSo how do
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342 13. MULTILEVEL MODELSprobabilit
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344 13. MULTILEVEL MODELSa conseque
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346 13. MULTILEVEL MODELSCompute th
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348 13. MULTILEVEL MODELSabsolute e
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350 13. MULTILEVEL MODELS)sigma_act
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14 Multilevel Models II: Slopes14.1
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14.1. EVERYTHING CAN VARY AND PROBA
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14.1. EVERYTHING CAN VARY AND PROBA
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360 15. MISSING DATA AND OTHER OPPO
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16 Writing Statistics379
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382 ENDNOTES12. For an autopsy of t
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384 ENDNOTES45. Fisher (1925), in C
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386 ENDNOTES77. See two famous edit
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388 ENDNOTES113. Hurlbert (1984) is
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390 BibliographyFrank, S. A. (2011)
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392 BibliographyProulx, S. R. and A
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IndexAkaike (1973), 380, 383Akaike
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INDEX 397non-identifiability, 153Oc
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