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218 7. INTERACTIONSlog(rgdppc_2000)6 7 8 9Africalog(rgdppc_2000)7 8 9 10 11not Africa0 1 2 3 4 5 6rugged0 1 2 3 4 5ruggedFIGURE 7.2. Separate linear regressions inside and outside of Africa, for log-GDP against terrain ruggedness. e slope is positive inside Africa, butnegative outside. How can we recover this reversal of the slope, using thecombined data?# extract countries with GDP datadd
7.1. BUILDING AN INTERACTION 219# non-African nationsm7.2
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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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2 Small Worlds and Large WorldsWhen
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2.1. PROBABILITY IS JUST COUNTING 3
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2.2. COLOMBO’S FIRST BAYESIAN MOD
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W L W W W L W L W2.2. COLOMBO’S F
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2.3. COMPONENTS OF THE MODEL 41not
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2.3. COMPONENTS OF THE MODEL 43You
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2.3. COMPONENTS OF THE MODEL 45e ma
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2.4. MAKING THE MODEL GO 47other pr
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2.4. MAKING THE MODEL GO 495 points
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2.4. MAKING THE MODEL GO 51just the
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2.4. MAKING THE MODEL GO 53between
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2.6. PRACTICE 55Medium.2.6.5. m1. R
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2.6. PRACTICE 57implied by the equa
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3 Sampling the ImaginaryLots of boo
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3. SAMPLING THE IMAGINARY 61a probl
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3.2. SAMPLING TO SUMMARIZE 63plot(
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3.2. SAMPLING TO SUMMARIZE 65Densit
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3.2. SAMPLING TO SUMMARIZE 67In con
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3.2. SAMPLING TO SUMMARIZE 69Densit
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3.3. SAMPLING TO SIMULATE PREDICTIO
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3.5. PRACTICE 793.5. PracticeEasy.
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3.5. PRACTICE 813.5.17. Predict sec
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84 4. LINEAR MODELSFIGURE 4.1. e Pt
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86 4. LINEAR MODELSexperiment with
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88 4. LINEAR MODELS4.1.4.2. Epistem
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90 4. LINEAR MODELSe approach above
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92 4. LINEAR MODELS'data.frame': 54
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94 4. LINEAR MODELSe point isn’t
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96 4. LINEAR MODELShave the samples
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98 4. LINEAR MODELSere’s no troub
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100 4. LINEAR MODELS)Note the comma
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102 4. LINEAR MODELSpreviously obse
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104 4. LINEAR MODELS4.4. Adding a p
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106 4. LINEAR MODELS(1) What is the
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108 4. LINEAR MODELSdata(Howell1)d
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110 4. LINEAR MODELSRethinking: Wha
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112 4. LINEAR MODELSheight140 150 1
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116 4. LINEAR MODELSYou end up with
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118 4. LINEAR MODELS(1) Use link to
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122 4. LINEAR MODELSRethinking: Lin
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126 4. LINEAR MODELS4.7.1. e1. In t
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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
Page 168 and 169: 168 5. MULTIVARIATE LINEAR MODELS5.
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Page 173 and 174: 6 Model Selection, Comparison, and
Page 175 and 176: 6.1. THE PROBLEM WITH PARAMETERS 17
Page 183 and 184: 6.2. INFORMATION THEORY AND MODEL P
Page 189 and 190: 6.3. AKAIKE INFORMATION CRITERION 1
Page 197 and 198: 6.4. DEVIANCE INFORMATION CRITERION
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
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Page 211 and 212: 6.7. PRACTICE 211Consider by analog
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Page 216 and 217: 216 7. INTERACTIONSFIGURE 7.1. TOP:
Page 220 and 221: 220 7. INTERACTIONSird, we may want
Page 222 and 223: 222 7. INTERACTIONSlog GDP year 200
Page 226 and 227: 226 7. INTERACTIONSInteraction mode
Page 228 and 229: 228 7. INTERACTIONSthis model and t
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
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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
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Page 257 and 258: 8.3. EASY HMC: MAP2STAN 257FIGURE 8
Page 259 and 260: 8.4. CARE AND FEEDING OF YOUR MARKO
Page 265: 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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