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306 12. MONSTERS AND MIXTURESdensity function. I’ll present a working function definition here, and then I’ll step through its piecesand explain the important parts.R code12.1dordlogit
12.1. ORDERED CATEGORICAL OUTCOMES 30712.1.3. Example: Moral intuition. e data for this example come from a series of experimentsconducted by philosophers, lead by Cushman. 109 Yes, philosophers do sometimesconduct experiments. In this case, the experiments aim to collect empirical evidence relevantto debates about moral intuition, the forms of reasoning through which people developjudgments about the moral goodness and badness of actions. ese debates are relevant toall of the social sciences, because they touch on broader issues of reasoning, the role of emotionsin decision making, and the theories of moral development, both in individuals andgroups.Specifically, these experiments address scenarios sometimes known as “trolley problems”that have proved vexing or paradoxical to moral philosophers. e reason these scenarioscan be vexing is that the analytical context of them can be identical, and yet people reliablyreach different judgments about the goodness or badness of the actions. Previous researchhas lead to at least three important principles of unconscious reasoning that may explainvariations in judgment. ese principles are:e action principle: Harm caused by action is morally worse than equivalent harmcaused by omission.e intention principle: Harm intended as the means to a goal is morally worse thanequivalent harm foreseen as the side effect of a goal.e contact principle: Using physical contact to cause harm to a victim is morallyworse than causing equivalent harm to a victim without using physical contact.e experimental context we’ll explore these principles within comprises stories thatvary these principles, while keeping many of the basic objects and actors the same. For example,the most renown story is about a speeding boxcar. Here is the version of boxcar storythat implies the action principle, but not the others:Standing by the railroad tracks, Dennis sees an empty, out-of-control boxcar aboutto hit five people. Next to Dennis is a lever that can be pulled, sending the boxcardown a side track and away from the five people. But pulling the lever will alsolower the railing on a footbridge spanning the side track, causing one person to falloff the footbridge and onto the side track, where he will be hit by the boxcar. IfDennis pulls the lever the boxcar will switch tracks and not hit the five people, andthe one person to fall and be hit by the boxcar. If Dennis does not pull the lever theboxcar will continue down the tracks and hit five people, and the one person willremain safe above the side track.Since the actor (Dennis) had to do something to create the outcome, rather than remainpassive, this is an action scenario. However, the harm caused to the one man who will fallis not necessary, or intended, in order to save the five. us it is not an example of theintention principle. And there is no direct contact, so it is also not an example of the contactprinciple. Now, how morally permissible is it to pull the lever? e data we’ll be interestedin predicting are responses to that question, rated on a scale from 1 (never permissible) to 7(always permissible).You can contrast the above story with the same outline, but now with both the actionprinciple and the intention principle. at is, in this version, the actor both does somethingto change the outcome and the action must cause harm to the one person in order to savethe other five:Standing by the railroad tracks, Evan sees an empty, out-of-control boxcar aboutto hit five people. Next to Evan is a lever that can be pulled, lowering the railingon a footbridge that spans the main track, and causing one person to fall off the
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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
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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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Page 257 and 258: 8.3. EASY HMC: MAP2STAN 257FIGURE 8
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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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