Regressão Logística e match
Hadrizia Santos
05 de agosto de 2018
Os dados
os dados descrevem 5000 encontros relâmpagos de 4 minutos envolvendo 310 jovens americanos. Os dados originais foram coletados por professores da Columbia Business School no experimento descrito aqui. Os participantes tinham vários encontros de 4 minutos por noite. Após cada um, preenchiam fichas avaliando aqueles com quem se encontraram. Cada linha nos dados representa um desses encontros.
As perguntas
Dentre os fatores que você acha que podem ter efeito no match, quais fatores têm efeito significativo na chance de p1 decidir se encontrar novamente com p2? E como é esse efeito (positivo/negativo)?
Que fatores nos dados têm mais efeito na chance de um participante querer se encontrar novamente com outro?
datings <- readr::read_csv("https://raw.githubusercontent.com/nazareno/ciencia-de-dados-1/master/5-regressao/speed-dating/speed-dating2.csv",
col_types = cols(
.default = col_integer(),
int_corr = col_double(),
field = col_character(),
from = col_character(),
career = col_character(),
attr = col_double(),
sinc = col_double(),
intel = col_double(),
fun = col_double(),
amb = col_double(),
shar = col_double(),
like = col_double(),
prob = col_double(),
match_es = col_double(),
attr3_s = col_double(),
sinc3_s = col_double(),
intel3_s = col_double(),
fun3_s = col_double(),
amb3_s = col_double(),
dec = col_character()
)) %>%
mutate(from = as.factor(from),
gender = as.factor(gender),
samerace = as.factor(samerace),
dec = as.factor(dec),
gender = as.factor(gender))
datings <- datings %>%
select(iid, gender, order, pid, int_corr, samerace, age_o, age, field,
race, from, career, sports, tvsports, exercise, dining, museums,
art, hiking, gaming, clubbing, reading, tv, theater, movies,
concerts, music, shopping, yoga, attr, sinc, intel, fun, amb,
shar, like, prob, attr3_s, sinc3_s, intel3_s, fun3_s, amb3_s, dec)
datings %>% glimpse()Observations: 4,918
Variables: 43
$ iid <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2...
$ gender <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ order <int> 4, 3, 10, 5, 7, 6, 1, 2, 8, 9, 10, 9, 6, 1, 3, 2,...
$ pid <int> 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 11, 12, 1...
$ int_corr <dbl> 0.14, 0.54, 0.16, 0.61, 0.21, 0.25, 0.34, 0.50, 0...
$ samerace <fct> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1...
$ age_o <int> 27, 22, 22, 23, 24, 25, 30, 27, 28, 24, 27, 22, 2...
$ age <int> 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 24, 24, 2...
$ field <chr> "Law", "Law", "Law", "Law", "Law", "Law", "Law", ...
$ race <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2...
$ from <fct> Chicago, Chicago, Chicago, Chicago, Chicago, Chic...
$ career <chr> "lawyer", "lawyer", "lawyer", "lawyer", "lawyer",...
$ sports <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3...
$ tvsports <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2...
$ exercise <int> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7...
$ dining <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10,...
$ museums <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8...
$ art <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 6, 6, 6, 6, 6, 6...
$ hiking <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, 3, 3...
$ gaming <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5...
$ clubbing <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8, 8, 8...
$ reading <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 10, 10, 10, 10, 10,...
$ tv <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 1, 1, 1, 1, 1, 1, 1...
$ theater <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 9, 9, 9, 9, 9, 9...
$ movies <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, ...
$ concerts <int> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 7, 7, 7, ...
$ music <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8...
$ shopping <int> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3...
$ yoga <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
$ attr <dbl> 6, 7, 5, 7, 5, 4, 7, 4, 7, 5, 5, 8, 5, 7, 6, 8, 7...
$ sinc <dbl> 9, 8, 8, 6, 6, 9, 6, 9, 6, 6, 7, 5, 8, 9, 8, 7, 5...
$ intel <dbl> 7, 7, 9, 8, 7, 7, 7, 7, 8, 6, 8, 6, 9, 7, 7, 8, 9...
$ fun <dbl> 7, 8, 8, 7, 7, 4, 4, 6, 9, 8, 4, 6, 6, 6, 9, 3, 6...
$ amb <dbl> 6, 5, 5, 6, 6, 6, 6, 5, 8, 10, 6, 9, 3, 5, 7, 6, ...
$ shar <dbl> 5, 6, 7, 8, 6, 4, 7, 6, 8, 8, 3, 6, 4, 7, 8, 2, 9...
$ like <dbl> 7, 7, 7, 7, 6, 6, 6, 6, 7, 6, 6, 7, 6, 7, 8, 6, 8...
$ prob <dbl> 6, 5, NA, 6, 6, 5, 5, 7, 7, 6, 4, 3, 7, 8, 6, 5, ...
$ attr3_s <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ sinc3_s <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ intel3_s <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ fun3_s <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ amb3_s <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ dec <fct> yes, yes, yes, yes, yes, no, yes, no, yes, yes, n...Os dados estão distribuídos nas seguintes colunas:
iid: id do participante p1 no encontrogender: sexo do p1, 0 = mulherorder: dos vários encontros realizados em uma noite, esse foi o n-ésimo, segundo essa variávelpid: id do participante p2int_corr: correlação entre os interesses de p1 e p2samerace: p1 e p2 são da mesma raça?age_o: idade de p2age: idade de p1field: campo de estudo de p1race: raça de p1. O código é Black/African American=1; European/Caucasian-American=2; Latino/Hispanic American=3; Asian/Pacific Islander/Asian-American=4; Native American=5; Other=6from: de onde p1 écareer: que carreira p1 quer seguirsports,tvsports,exercise,dining,museums,art,hiking,gaming,clubbing,reading,tv,theater,movies,concerts,music,shopping,yoga: De 1 a 10, quão interessado p1 é em cada uma dessas atividades.
attr: quão atraente p1 achou p2sinc: quão sincero p1 achou p2intel: quão inteligente p1 achou p2
fun: quão divertido p1 achou p2amb: quão ambicioso p1 achou p2shar: quanto p1 achou que compartilha interesses e hobbies com p2like: no geral, quanto p1 gostou de p2?prob: que probabiliade p1 acha que p2 tem de querer se encontrar novamente com p- (escala 1-10)attr3_s: quanto p1 acha que é atraentesinc3_s: quanto p1 acha que é sincerointel3_s: quanto p1 acha que é inteligentefun3_s: quanto p1 acha que é divertidoamb3_s: quanto p1 acha que é ambiciosodec: p1 gostaria de se encontrar novamente com p2?
Tratamento nos dados
Com uma pequena amostra dos dados, já percebe-se o quanto de dados faltosos exitem em algumas colunas. Essa falta pode ser melhor observada no gráfico abaixo:
missmap(datings)a condição tem comprimento > 1 e somente o primeiro elemento será usadoUnknown or uninitialised column: 'arguments'.Unknown or uninitialised column: 'arguments'.Unknown or uninitialised column: 'imputations'.
As variáveis que possui mais dados faltosos são as váriaveis attr3_s, sinc3_s, intel3_s, fun3_s, amb3_s. Para lidar com isso, substituirei o dado faltando pela mediana da variável.
f = function(x){
x<-as.numeric(as.character(x))
x[is.na(x)] = median(x, na.rm=TRUE)
x
}
temp <- data.frame(apply(datings,2,f)) NAs introduzidos por coerçãoNAs introduzidos por coerçãoNAs introduzidos por coerçãoNAs introduzidos por coerçãodatings$attr3_s <- temp$attr3_s
datings$sinc3_s <- temp$sinc3_s
datings$intel3_s <- temp$intel3_s
datings$fun3_s <- temp$fun3_s
datings$amb3_s <- temp$amb3_sObservando o resultado:
missmap(datings)a condição tem comprimento > 1 e somente o primeiro elemento será usadoUnknown or uninitialised column: 'arguments'.Unknown or uninitialised column: 'arguments'.Unknown or uninitialised column: 'imputations'.
Concluída a fase de processamento desses dados, agora é hora de criar um modelo.
Seleção das variáveis
As variáveis escolhidas para servirem de preditoras para o modelo são like, attr e shar, por achar que elas são realmente decisivas para a possibilidade de um 2º encontro.
summary(model)
Call:
glm(formula = dec ~ ., family = "binomial", data = datings %>%
select(like, attr, shar, dec) %>% na.omit())
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8084 -0.7838 -0.2611 0.8068 3.2422
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.04965 0.22992 -30.661 < 2e-16 ***
like 0.54007 0.03577 15.098 < 2e-16 ***
attr 0.39564 0.02859 13.836 < 2e-16 ***
shar 0.16166 0.02339 6.912 4.77e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 5817.7 on 4263 degrees of freedom
Residual deviance: 4130.8 on 4260 degrees of freedom
AIC: 4138.8
Number of Fisher Scoring iterations: 5table(expectativa_realidade$categoria_prevista, expectativa_realidade$dec)
no yes
no_pred 1983 554
yes_pred 464 1263
1. Dentre os fatores que você acha que podem ter efeito no match, quais fatores têm efeito significativo na chance de p1 decidir se encontrar novamente com p2? E como é esse efeito (positivo/negativo)?
De acordo com o modelo e as variáveis escolhidas, todas possuem grande e positivo efeito na chance de p1 decidir se encontrar novamente com p2. São elas: like, shar e attr.
2. Que fatores nos dados têm mais efeito na chance de um participante querer se encontrar novamente com outro?
summary(model)
Call:
glm(formula = dec ~ ., family = "binomial", data = datings %>%
select(gender, order, pid, int_corr, samerace, age_o, age,
field, race, from, career, attr, sinc, intel, fun, amb,
shar, like, prob, attr3_s, sinc3_s, intel3_s, fun3_s,
amb3_s, dec) %>% na.omit())
Deviance Residuals:
Min 1Q Median 3Q Max
-8.49 0.00 0.00 0.00 8.49
Coefficients: (236 not defined because of singularities)
Estimate
(Intercept) -8.014e+16
gender1 6.368e+13
order -3.546e+12
pid -6.878e+11
int_corr -1.932e+14
samerace1 1.855e+14
age_o -6.785e+12
age 1.329e+15
fieldAmerican Studies -2.288e+14
fieldApplied Maths/Econs 2.174e+16
fieldArt Education 1.114e+16
fieldArt History 3.853e+16
fieldArt History/medicine 2.003e+16
fieldArts Administration 5.312e+15
fieldBilingual Education 5.718e+15
fieldBiochemistry 4.057e+15
fieldBiochemistry & Molecular Biophysics 3.076e+15
fieldbiology 1.604e+16
fieldBiology 1.084e+16
fieldbiomedical engineering 1.948e+16
fieldBiomedical engineering 5.567e+15
fieldBiomedical Engineering -7.045e+15
fieldBiomedical Informatics 3.963e+15
fieldbiotechnology 1.573e+16
fieldbusiness 3.237e+15
fieldBusiness -4.272e+15
fieldBusiness (Finance & Marketing) 3.138e+15
fieldBusiness & International Affairs 1.502e+15
fieldBusiness (MBA) 1.599e+16
fieldBusiness- MBA 2.003e+16
fieldBusiness, Media -8.199e+15
fieldchemistry 1.162e+16
fieldChemistry -7.385e+15
fieldClassics -1.077e+16
fieldClimate Dynamics -8.760e+14
fieldClimate-Earth and Environ. Science 7.593e+15
fieldCommunications 1.810e+16
fieldComputer Science 2.114e+16
fieldConservation biology -5.955e+15
fieldCounseling Psychology 7.841e+15
fieldCreative Writing -1.798e+16
fieldCreative Writing - Nonfiction 1.097e+16
fieldCreative Writing (Nonfiction) 1.187e+16
fieldEarth and Environmental Science 3.048e+15
fieldEconomics 8.743e+14
fieldEconomics and Political Science 8.835e+15
fieldEconomics, Sociology 8.385e+15
fieldEducation 1.597e+16
fieldEducation Administration 1.388e+15
fieldEducational Psychology 7.722e+15
fieldEducation Leadership - Public School Administration -8.667e+15
fieldEducation Policy 3.420e+15
fieldElectrical Engg. 2.966e+16
fieldelectrical engineering 2.542e+16
fieldElectrical Engineering 4.471e+15
fieldELECTRICAL ENGINEERING 1.889e+16
fieldElementary/Childhood Education (MA) 1.744e+16
fieldelementary education -2.123e+15
fieldElementary Education 1.789e+16
fieldElementary Education - Preservice 7.277e+15
fieldengineering 2.033e+16
fieldEngineering 1.231e+16
fieldEnglish 1.312e+16
fieldFilm 5.304e+15
fieldFinanace 3.374e+15
fieldFinance -1.282e+16
fieldFinance&Economics -6.186e+15
fieldFinancial Engineering 2.234e+16
fieldfinancial math 4.085e+15
fieldfrench 2.870e+16
fieldFundraising Management 8.731e+14
fieldGeneral management/finance 3.229e+15
fieldgenetics 3.787e+16
fieldGenetics 2.972e+15
fieldGenetics & Development 5.073e+15
fieldGerman Literature 3.149e+15
fieldGS Postbacc PreMed 5.853e+15
fieldIndustrial Engineering 1.617e+16
fieldIndustrial Engineering/Operations Research 2.020e+16
fieldInternational Affairs -1.158e+16
fieldInternational Affairs - Economic Policy 1.061e+16
fieldInternational Educational Development -1.913e+16
fieldInternational Politics 2.177e+16
fieldJapanese Literature 5.800e+15
fieldjournalism 1.597e+16
fieldJournalism -1.698e+15
fieldlaw 4.671e+15
fieldLaw 9.553e+15
fieldLAW 2.729e+15
fieldLaw and English Literature (J.D./Ph.D.) 3.150e+16
fieldLaw/Business 3.771e+15
fieldMarketing -1.474e+16
fieldMA Science Education 1.992e+16
fieldMasters in Public Administration 2.251e+16
fieldMasters of Industrial Engineering 2.050e+16
fieldMasters of Social Work 7.658e+15
fieldMasters of Social Work&Education 1.398e+16
fieldMath 5.233e+14
fieldMathematical Finance 8.601e+15
fieldMathematics 2.942e+16
fieldMBA 1.505e+16
fieldMBA / Master of International Affairs (SIPA) 5.425e+14
fieldMBA - Private Equity / Real Estate 7.809e+15
fieldMechanical Engineering 2.005e+16
fieldmedical informatics 4.759e+15
fieldmedicine 2.130e+16
fieldMedicine 1.758e+15
fieldmedicine and biochemistry 1.784e+15
fieldMFA Acting Program 1.778e+16
fieldMFA Creative Writing 1.055e+16
fieldMFA -Film 5.661e+15
fieldMFA Writing 1.391e+16
fieldmicrobiology 6.735e+15
fieldMolecular Biology 1.262e+16
fieldmoney 1.113e+16
fieldNonfiction writing 1.675e+16
fieldNonFiction Writing -1.091e+16
fieldnutrition 4.950e+15
fieldNutritiron 2.315e+16
fieldOperations Research 1.281e+16
fieldOperations Research (SEAS) 1.579e+16
fieldOrganizational Psychology 1.399e+16
fieldphilosophy 1.768e+16
fieldPhilosophy 1.174e+16
fieldPhilosophy and Physics 3.583e+16
fieldPhilosophy (Ph.D.) -1.600e+16
fieldPolish 2.681e+16
fieldpolitical science 4.121e+15
fieldPolitical Science 7.533e+15
fieldpsychology 5.303e+15
fieldPsychology 1.032e+16
fieldpsychology and english 2.139e+16
fieldPublic Health 1.531e+16
fieldPublic Policy -1.466e+14
fieldReligion 2.023e+16
fieldSchool Psychology 1.819e+14
fieldSIPA - Energy -1.665e+15
fieldsocial work 6.815e+15
fieldSocial Work 3.980e+15
fieldSocial Work/SIPA 1.690e+16
fieldsociology 1.584e+16
fieldSociology 1.992e+16
fieldSociology and Education 1.668e+16
fieldSpeech Language Pathology 2.131e+16
fieldStatistics 1.430e+16
fieldTC (Health Ed) 1.110e+16
fieldTheater 7.127e+15
fieldTheatre Management & Producing 1.275e+16
fieldUndergrad - GS 2.128e+15
fieldWriting: Literary Nonfiction 4.471e+15
race -1.389e+15
fromAlabama 6.205e+15
fromAlbuquerque, NM -1.075e+15
fromAnn Arbor 4.926e+15
fromAnn Arbor, MI NA
fromArgentina -5.632e+15
fromArizona NA
fromAsia, Singapore 6.529e+15
fromAtlanta, GA -2.725e+15
fromAustin, TX 1.276e+16
fromBaltimore NA
fromBangladesh -3.124e+15
fromBEIJING, CHINA NA
fromBogota, Colombia -1.055e+16
fromBorn in Iran NA
fromBorn in Montana, raised in South Jersey (nr. Philadelphia) NA
fromBoston 7.286e+15
fromboston, ma NA
fromBoston, MA 1.436e+16
fromBoulder, Colorado NA
fromBowdoin College 3.045e+14
fromBrandeis University NA
fromBrazil -1.589e+16
fromBrooklyn 1.078e+16
frombrooklyn ny 1.232e+16
frombrooklyn, ny NA
fromBrooklyn, NY 8.849e+14
fromBudapest -6.299e+15
fromBurlington, Vermont NA
fromcalifornia NA
fromCalifornia 1.170e+16
fromCanada 1.258e+16
fromChicago 2.953e+15
fromChile 1.782e+16
fromChina 1.683e+15
fromCincinnati, OH NA
fromCincinnati, Ohio 1.036e+16
fromColorado 2.743e+14
fromConnecticut 7.136e+15
fromczech republic -1.890e+16
fromEngland 7.427e+15
fromFlorida -1.530e+14
fromFlorida and Virginia -1.792e+14
fromfrance NA
fromFrance 1.530e+16
fromGenova, Italy 2.229e+15
fromGeorgia, USA 4.004e+15
fromGermany 1.872e+16
fromGreece 1.027e+14
fromGreece/Germany -1.557e+16
Std. Error
(Intercept) 7.371e+08
gender1 3.358e+07
order 2.115e+05
pid 5.498e+04
int_corr 4.127e+06
samerace1 2.533e+06
age_o 3.541e+05
age 1.091e+07
fieldAmerican Studies 1.300e+08
fieldApplied Maths/Econs 3.032e+08
fieldArt Education 2.468e+08
fieldArt History 4.611e+08
fieldArt History/medicine 2.657e+08
fieldArts Administration 1.765e+08
fieldBilingual Education 1.711e+08
fieldBiochemistry 1.381e+08
fieldBiochemistry & Molecular Biophysics 1.129e+08
fieldbiology 2.587e+08
fieldBiology 1.957e+08
fieldbiomedical engineering 2.753e+08
fieldBiomedical engineering 1.764e+08
fieldBiomedical Engineering 7.302e+07
fieldBiomedical Informatics 1.380e+08
fieldbiotechnology 2.108e+08
fieldbusiness 1.269e+08
fieldBusiness 6.325e+07
fieldBusiness (Finance & Marketing) 1.327e+08
fieldBusiness & International Affairs 6.040e+07
fieldBusiness (MBA) 2.210e+08
fieldBusiness- MBA 2.790e+08
fieldBusiness, Media 1.369e+08
fieldchemistry 2.475e+08
fieldChemistry 7.287e+07
fieldClassics 1.183e+08
fieldClimate Dynamics 1.649e+08
fieldClimate-Earth and Environ. Science 1.683e+08
fieldCommunications 2.667e+08
fieldComputer Science 2.583e+08
fieldConservation biology 9.261e+07
fieldCounseling Psychology 1.911e+08
fieldCreative Writing 2.180e+08
fieldCreative Writing - Nonfiction 2.009e+08
fieldCreative Writing (Nonfiction) 2.167e+08
fieldEarth and Environmental Science 1.434e+08
fieldEconomics 1.811e+08
fieldEconomics and Political Science 1.602e+08
fieldEconomics, Sociology 1.712e+08
fieldEducation 2.450e+08
fieldEducation Administration 1.180e+08
fieldEducational Psychology 2.007e+08
fieldEducation Leadership - Public School Administration 7.862e+07
fieldEducation Policy 1.285e+08
fieldElectrical Engg. 3.225e+08
fieldelectrical engineering 2.788e+08
fieldElectrical Engineering 1.254e+08
fieldELECTRICAL ENGINEERING 2.573e+08
fieldElementary/Childhood Education (MA) 2.959e+08
fieldelementary education 1.323e+08
fieldElementary Education 3.137e+08
fieldElementary Education - Preservice 1.792e+08
fieldengineering 2.588e+08
fieldEngineering 2.227e+08
fieldEnglish 1.882e+08
fieldFilm 1.396e+08
fieldFinanace 1.486e+08
fieldFinance 1.032e+08
fieldFinance&Economics 1.703e+08
fieldFinancial Engineering 2.898e+08
fieldfinancial math 1.597e+08
fieldfrench 3.190e+08
fieldFundraising Management 1.743e+08
fieldGeneral management/finance 1.371e+08
fieldgenetics 4.752e+08
fieldGenetics 1.417e+08
fieldGenetics & Development 2.606e+08
fieldGerman Literature 2.567e+08
fieldGS Postbacc PreMed 1.595e+08
fieldIndustrial Engineering 2.393e+08
fieldIndustrial Engineering/Operations Research 2.751e+08
fieldInternational Affairs 1.378e+08
fieldInternational Affairs - Economic Policy 1.651e+08
fieldInternational Educational Development 1.238e+08
fieldInternational Politics 2.784e+08
fieldJapanese Literature 1.249e+08
fieldjournalism 2.467e+08
fieldJournalism 1.236e+08
fieldlaw 1.306e+08
fieldLaw 1.709e+08
fieldLAW 1.371e+08
fieldLaw and English Literature (J.D./Ph.D.) 3.899e+08
fieldLaw/Business 1.269e+08
fieldMarketing 1.039e+08
fieldMA Science Education 2.113e+08
fieldMasters in Public Administration 2.876e+08
fieldMasters of Industrial Engineering 2.753e+08
fieldMasters of Social Work 2.009e+08
fieldMasters of Social Work&Education 2.357e+08
fieldMath 1.315e+08
fieldMathematical Finance 1.743e+08
fieldMathematics 3.305e+08
fieldMBA 1.774e+08
fieldMBA / Master of International Affairs (SIPA) 1.712e+08
fieldMBA - Private Equity / Real Estate 1.903e+08
fieldMechanical Engineering 2.926e+08
fieldmedical informatics 1.732e+08
fieldmedicine 2.704e+08
fieldMedicine 1.241e+08
fieldmedicine and biochemistry 1.031e+08
fieldMFA Acting Program 3.225e+08
fieldMFA Creative Writing 1.993e+08
fieldMFA -Film 1.443e+08
fieldMFA Writing 2.320e+08
fieldmicrobiology 2.002e+08
fieldMolecular Biology 1.907e+08
fieldmoney 2.033e+08
fieldNonfiction writing 2.606e+08
fieldNonFiction Writing 1.255e+08
fieldnutrition 1.433e+08
fieldNutritiron 3.540e+08
fieldOperations Research 1.955e+08
fieldOperations Research (SEAS) 2.594e+08
fieldOrganizational Psychology 2.236e+08
fieldphilosophy 2.565e+08
fieldPhilosophy 2.035e+08
fieldPhilosophy and Physics 4.926e+08
fieldPhilosophy (Ph.D.) 1.260e+08
fieldPolish 3.106e+08
fieldpolitical science 1.942e+08
fieldPolitical Science 6.349e+07
fieldpsychology 1.568e+08
fieldPsychology 1.854e+08
fieldpsychology and english 2.762e+08
fieldPublic Health 2.517e+08
fieldPublic Policy 1.371e+08
fieldReligion 2.653e+08
fieldSchool Psychology 9.451e+07
fieldSIPA - Energy 8.341e+07
fieldsocial work 2.330e+08
fieldSocial Work 1.330e+08
fieldSocial Work/SIPA 2.745e+08
fieldsociology 1.951e+08
fieldSociology 2.292e+08
fieldSociology and Education 2.665e+08
fieldSpeech Language Pathology 2.802e+08
fieldStatistics 2.319e+08
fieldTC (Health Ed) 2.104e+08
fieldTheater 1.151e+08
fieldTheatre Management & Producing 2.192e+08
fieldUndergrad - GS 9.089e+07
fieldWriting: Literary Nonfiction 1.104e+08
race 1.610e+07
fromAlabama 1.554e+08
fromAlbuquerque, NM 9.756e+07
fromAnn Arbor 9.731e+07
fromAnn Arbor, MI NA
fromArgentina 1.094e+08
fromArizona NA
fromAsia, Singapore 1.678e+08
fromAtlanta, GA 1.051e+08
fromAustin, TX 2.306e+08
fromBaltimore NA
fromBangladesh 1.136e+08
fromBEIJING, CHINA NA
fromBogota, Colombia 1.027e+08
fromBorn in Iran NA
fromBorn in Montana, raised in South Jersey (nr. Philadelphia) NA
fromBoston 1.542e+08
fromboston, ma NA
fromBoston, MA 2.397e+08
fromBoulder, Colorado NA
fromBowdoin College 1.179e+08
fromBrandeis University NA
fromBrazil 1.788e+08
fromBrooklyn 1.424e+08
frombrooklyn ny 1.617e+08
frombrooklyn, ny NA
fromBrooklyn, NY 8.700e+07
fromBudapest 1.035e+08
fromBurlington, Vermont NA
fromcalifornia NA
fromCalifornia 1.823e+08
fromCanada 1.748e+08
fromChicago 1.308e+08
fromChile 2.176e+08
fromChina 1.302e+08
fromCincinnati, OH NA
fromCincinnati, Ohio 1.301e+08
fromColorado 9.882e+07
fromConnecticut 1.740e+08
fromczech republic 2.494e+08
fromEngland 1.362e+08
fromFlorida 1.150e+08
fromFlorida and Virginia 8.728e+07
fromfrance NA
fromFrance 1.947e+08
fromGenova, Italy 1.226e+08
fromGeorgia, USA 1.562e+08
fromGermany 1.689e+08
fromGreece 1.806e+08
fromGreece/Germany 1.586e+08
z value
(Intercept) -108714219
gender1 1895992
order -16766409
pid -12511216
int_corr -46814083
samerace1 73257802
age_o -19160327
age 121766997
fieldAmerican Studies -1759852
fieldApplied Maths/Econs 71717872
fieldArt Education 45131126
fieldArt History 83569961
fieldArt History/medicine 75365427
fieldArts Administration 30096317
fieldBilingual Education 33410005
fieldBiochemistry 29370299
fieldBiochemistry & Molecular Biophysics 27247237
fieldbiology 61996628
fieldBiology 55385855
fieldbiomedical engineering 70758499
fieldBiomedical engineering 31558892
fieldBiomedical Engineering -96482613
fieldBiomedical Informatics 28711977
fieldbiotechnology 74612856
fieldbusiness 25500563
fieldBusiness -67541638
fieldBusiness (Finance & Marketing) 23657092
fieldBusiness & International Affairs 24870112
fieldBusiness (MBA) 72351163
fieldBusiness- MBA 71771003
fieldBusiness, Media -59908709
fieldchemistry 46968111
fieldChemistry -101336387
fieldClassics -91039035
fieldClimate Dynamics -5313669
fieldClimate-Earth and Environ. Science 45124221
fieldCommunications 67876695
fieldComputer Science 81833628
fieldConservation biology -64310025
fieldCounseling Psychology 41036027
fieldCreative Writing -82465762
fieldCreative Writing - Nonfiction 54587624
fieldCreative Writing (Nonfiction) 54771854
fieldEarth and Environmental Science 21252955
fieldEconomics 4828402
fieldEconomics and Political Science 55154397
fieldEconomics, Sociology 48977925
fieldEducation 65204765
fieldEducation Administration 11765336
fieldEducational Psychology 38473698
fieldEducation Leadership - Public School Administration -110245451
fieldEducation Policy 26615843
fieldElectrical Engg. 91956828
fieldelectrical engineering 91185441
fieldElectrical Engineering 35643602
fieldELECTRICAL ENGINEERING 73430161
fieldElementary/Childhood Education (MA) 58928351
fieldelementary education -16049201
fieldElementary Education 57016567
fieldElementary Education - Preservice 40615722
fieldengineering 78557052
fieldEngineering 55277789
fieldEnglish 69739360
fieldFilm 37985493
fieldFinanace 22703955
fieldFinance -124212985
fieldFinance&Economics -36317271
fieldFinancial Engineering 77108531
fieldfinancial math 25573726
fieldfrench 89953379
fieldFundraising Management 5009361
fieldGeneral management/finance 23557252
fieldgenetics 79697817
fieldGenetics 20970457
fieldGenetics & Development 19469329
fieldGerman Literature 12267892
fieldGS Postbacc PreMed 36698877
fieldIndustrial Engineering 67567644
fieldIndustrial Engineering/Operations Research 73443549
fieldInternational Affairs -84066907
fieldInternational Affairs - Economic Policy 64252619
fieldInternational Educational Development -154485470
fieldInternational Politics 78178280
fieldJapanese Literature 46420981
fieldjournalism 64753147
fieldJournalism -13735748
fieldlaw 35771853
fieldLaw 55891021
fieldLAW 19905784
fieldLaw and English Literature (J.D./Ph.D.) 80792335
fieldLaw/Business 29712823
fieldMarketing -141777796
fieldMA Science Education 94260853
fieldMasters in Public Administration 78294321
fieldMasters of Industrial Engineering 74461775
fieldMasters of Social Work 38119547
fieldMasters of Social Work&Education 59304541
fieldMath 3978891
fieldMathematical Finance 49337473
fieldMathematics 89040343
fieldMBA 84821149
fieldMBA / Master of International Affairs (SIPA) 3168903
fieldMBA - Private Equity / Real Estate 41036259
fieldMechanical Engineering 68520191
fieldmedical informatics 27474377
fieldmedicine 78773347
fieldMedicine 14162388
fieldmedicine and biochemistry 17294036
fieldMFA Acting Program 55137012
fieldMFA Creative Writing 52921070
fieldMFA -Film 39238741
fieldMFA Writing 59972226
fieldmicrobiology 33641925
fieldMolecular Biology 66155124
fieldmoney 54763044
fieldNonfiction writing 64285594
fieldNonFiction Writing -86941963
fieldnutrition 34544104
fieldNutritiron 65391872
fieldOperations Research 65492516
fieldOperations Research (SEAS) 60883213
fieldOrganizational Psychology 62571376
fieldphilosophy 68927042
fieldPhilosophy 57691713
fieldPhilosophy and Physics 72749626
fieldPhilosophy (Ph.D.) -127018840
fieldPolish 86335448
fieldpolitical science 21218466
fieldPolitical Science 118647393
fieldpsychology 33815509
fieldPsychology 55632538
fieldpsychology and english 77454158
fieldPublic Health 60836041
fieldPublic Policy -1069196
fieldReligion 76282743
fieldSchool Psychology 1924409
fieldSIPA - Energy -19967724
fieldsocial work 29254913
fieldSocial Work 29918778
fieldSocial Work/SIPA 61566531
fieldsociology 81205518
fieldSociology 86905733
fieldSociology and Education 62584834
fieldSpeech Language Pathology 76068128
fieldStatistics 61666100
fieldTC (Health Ed) 52741568
fieldTheater 61929238
fieldTheatre Management & Producing 58169624
fieldUndergrad - GS 23409079
fieldWriting: Literary Nonfiction 40497511
race -86247294
fromAlabama 39934628
fromAlbuquerque, NM -11015731
fromAnn Arbor 50617085
fromAnn Arbor, MI NA
fromArgentina -51461691
fromArizona NA
fromAsia, Singapore 38922811
fromAtlanta, GA -25917237
fromAustin, TX 55356553
fromBaltimore NA
fromBangladesh -27505566
fromBEIJING, CHINA NA
fromBogota, Colombia -102744124
fromBorn in Iran NA
fromBorn in Montana, raised in South Jersey (nr. Philadelphia) NA
fromBoston 47258547
fromboston, ma NA
fromBoston, MA 59910648
fromBoulder, Colorado NA
fromBowdoin College 2582772
fromBrandeis University NA
fromBrazil -88856837
fromBrooklyn 75731291
frombrooklyn ny 76236867
frombrooklyn, ny NA
fromBrooklyn, NY 10171818
fromBudapest -60841460
fromBurlington, Vermont NA
fromcalifornia NA
fromCalifornia 64217902
fromCanada 71964024
fromChicago 22573314
fromChile 81882244
fromChina 12929607
fromCincinnati, OH NA
fromCincinnati, Ohio 79617915
fromColorado 2776067
fromConnecticut 41019603
fromczech republic -75795039
fromEngland 54539072
fromFlorida -1330210
fromFlorida and Virginia -2052601
fromfrance NA
fromFrance 78584185
fromGenova, Italy 18187040
fromGeorgia, USA 25635679
fromGermany 110790501
fromGreece 568809
fromGreece/Germany -98181567
Pr(>|z|)
(Intercept) <2e-16
gender1 <2e-16
order <2e-16
pid <2e-16
int_corr <2e-16
samerace1 <2e-16
age_o <2e-16
age <2e-16
fieldAmerican Studies <2e-16
fieldApplied Maths/Econs <2e-16
fieldArt Education <2e-16
fieldArt History <2e-16
fieldArt History/medicine <2e-16
fieldArts Administration <2e-16
fieldBilingual Education <2e-16
fieldBiochemistry <2e-16
fieldBiochemistry & Molecular Biophysics <2e-16
fieldbiology <2e-16
fieldBiology <2e-16
fieldbiomedical engineering <2e-16
fieldBiomedical engineering <2e-16
fieldBiomedical Engineering <2e-16
fieldBiomedical Informatics <2e-16
fieldbiotechnology <2e-16
fieldbusiness <2e-16
fieldBusiness <2e-16
fieldBusiness (Finance & Marketing) <2e-16
fieldBusiness & International Affairs <2e-16
fieldBusiness (MBA) <2e-16
fieldBusiness- MBA <2e-16
fieldBusiness, Media <2e-16
fieldchemistry <2e-16
fieldChemistry <2e-16
fieldClassics <2e-16
fieldClimate Dynamics <2e-16
fieldClimate-Earth and Environ. Science <2e-16
fieldCommunications <2e-16
fieldComputer Science <2e-16
fieldConservation biology <2e-16
fieldCounseling Psychology <2e-16
fieldCreative Writing <2e-16
fieldCreative Writing - Nonfiction <2e-16
fieldCreative Writing (Nonfiction) <2e-16
fieldEarth and Environmental Science <2e-16
fieldEconomics <2e-16
fieldEconomics and Political Science <2e-16
fieldEconomics, Sociology <2e-16
fieldEducation <2e-16
fieldEducation Administration <2e-16
fieldEducational Psychology <2e-16
fieldEducation Leadership - Public School Administration <2e-16
fieldEducation Policy <2e-16
fieldElectrical Engg. <2e-16
fieldelectrical engineering <2e-16
fieldElectrical Engineering <2e-16
fieldELECTRICAL ENGINEERING <2e-16
fieldElementary/Childhood Education (MA) <2e-16
fieldelementary education <2e-16
fieldElementary Education <2e-16
fieldElementary Education - Preservice <2e-16
fieldengineering <2e-16
fieldEngineering <2e-16
fieldEnglish <2e-16
fieldFilm <2e-16
fieldFinanace <2e-16
fieldFinance <2e-16
fieldFinance&Economics <2e-16
fieldFinancial Engineering <2e-16
fieldfinancial math <2e-16
fieldfrench <2e-16
fieldFundraising Management <2e-16
fieldGeneral management/finance <2e-16
fieldgenetics <2e-16
fieldGenetics <2e-16
fieldGenetics & Development <2e-16
fieldGerman Literature <2e-16
fieldGS Postbacc PreMed <2e-16
fieldIndustrial Engineering <2e-16
fieldIndustrial Engineering/Operations Research <2e-16
fieldInternational Affairs <2e-16
fieldInternational Affairs - Economic Policy <2e-16
fieldInternational Educational Development <2e-16
fieldInternational Politics <2e-16
fieldJapanese Literature <2e-16
fieldjournalism <2e-16
fieldJournalism <2e-16
fieldlaw <2e-16
fieldLaw <2e-16
fieldLAW <2e-16
fieldLaw and English Literature (J.D./Ph.D.) <2e-16
fieldLaw/Business <2e-16
fieldMarketing <2e-16
fieldMA Science Education <2e-16
fieldMasters in Public Administration <2e-16
fieldMasters of Industrial Engineering <2e-16
fieldMasters of Social Work <2e-16
fieldMasters of Social Work&Education <2e-16
fieldMath <2e-16
fieldMathematical Finance <2e-16
fieldMathematics <2e-16
fieldMBA <2e-16
fieldMBA / Master of International Affairs (SIPA) <2e-16
fieldMBA - Private Equity / Real Estate <2e-16
fieldMechanical Engineering <2e-16
fieldmedical informatics <2e-16
fieldmedicine <2e-16
fieldMedicine <2e-16
fieldmedicine and biochemistry <2e-16
fieldMFA Acting Program <2e-16
fieldMFA Creative Writing <2e-16
fieldMFA -Film <2e-16
fieldMFA Writing <2e-16
fieldmicrobiology <2e-16
fieldMolecular Biology <2e-16
fieldmoney <2e-16
fieldNonfiction writing <2e-16
fieldNonFiction Writing <2e-16
fieldnutrition <2e-16
fieldNutritiron <2e-16
fieldOperations Research <2e-16
fieldOperations Research (SEAS) <2e-16
fieldOrganizational Psychology <2e-16
fieldphilosophy <2e-16
fieldPhilosophy <2e-16
fieldPhilosophy and Physics <2e-16
fieldPhilosophy (Ph.D.) <2e-16
fieldPolish <2e-16
fieldpolitical science <2e-16
fieldPolitical Science <2e-16
fieldpsychology <2e-16
fieldPsychology <2e-16
fieldpsychology and english <2e-16
fieldPublic Health <2e-16
fieldPublic Policy <2e-16
fieldReligion <2e-16
fieldSchool Psychology <2e-16
fieldSIPA - Energy <2e-16
fieldsocial work <2e-16
fieldSocial Work <2e-16
fieldSocial Work/SIPA <2e-16
fieldsociology <2e-16
fieldSociology <2e-16
fieldSociology and Education <2e-16
fieldSpeech Language Pathology <2e-16
fieldStatistics <2e-16
fieldTC (Health Ed) <2e-16
fieldTheater <2e-16
fieldTheatre Management & Producing <2e-16
fieldUndergrad - GS <2e-16
fieldWriting: Literary Nonfiction <2e-16
race <2e-16
fromAlabama <2e-16
fromAlbuquerque, NM <2e-16
fromAnn Arbor <2e-16
fromAnn Arbor, MI NA
fromArgentina <2e-16
fromArizona NA
fromAsia, Singapore <2e-16
fromAtlanta, GA <2e-16
fromAustin, TX <2e-16
fromBaltimore NA
fromBangladesh <2e-16
fromBEIJING, CHINA NA
fromBogota, Colombia <2e-16
fromBorn in Iran NA
fromBorn in Montana, raised in South Jersey (nr. Philadelphia) NA
fromBoston <2e-16
fromboston, ma NA
fromBoston, MA <2e-16
fromBoulder, Colorado NA
fromBowdoin College <2e-16
fromBrandeis University NA
fromBrazil <2e-16
fromBrooklyn <2e-16
frombrooklyn ny <2e-16
frombrooklyn, ny NA
fromBrooklyn, NY <2e-16
fromBudapest <2e-16
fromBurlington, Vermont NA
fromcalifornia NA
fromCalifornia <2e-16
fromCanada <2e-16
fromChicago <2e-16
fromChile <2e-16
fromChina <2e-16
fromCincinnati, OH NA
fromCincinnati, Ohio <2e-16
fromColorado <2e-16
fromConnecticut <2e-16
fromczech republic <2e-16
fromEngland <2e-16
fromFlorida <2e-16
fromFlorida and Virginia <2e-16
fromfrance NA
fromFrance <2e-16
fromGenova, Italy <2e-16
fromGeorgia, USA <2e-16
fromGermany <2e-16
fromGreece <2e-16
fromGreece/Germany <2e-16
(Intercept) ***
gender1 ***
order ***
pid ***
int_corr ***
samerace1 ***
age_o ***
age ***
fieldAmerican Studies ***
fieldApplied Maths/Econs ***
fieldArt Education ***
fieldArt History ***
fieldArt History/medicine ***
fieldArts Administration ***
fieldBilingual Education ***
fieldBiochemistry ***
fieldBiochemistry & Molecular Biophysics ***
fieldbiology ***
fieldBiology ***
fieldbiomedical engineering ***
fieldBiomedical engineering ***
fieldBiomedical Engineering ***
fieldBiomedical Informatics ***
fieldbiotechnology ***
fieldbusiness ***
fieldBusiness ***
fieldBusiness (Finance & Marketing) ***
fieldBusiness & International Affairs ***
fieldBusiness (MBA) ***
fieldBusiness- MBA ***
fieldBusiness, Media ***
fieldchemistry ***
fieldChemistry ***
fieldClassics ***
fieldClimate Dynamics ***
fieldClimate-Earth and Environ. Science ***
fieldCommunications ***
fieldComputer Science ***
fieldConservation biology ***
fieldCounseling Psychology ***
fieldCreative Writing ***
fieldCreative Writing - Nonfiction ***
fieldCreative Writing (Nonfiction) ***
fieldEarth and Environmental Science ***
fieldEconomics ***
fieldEconomics and Political Science ***
fieldEconomics, Sociology ***
fieldEducation ***
fieldEducation Administration ***
fieldEducational Psychology ***
fieldEducation Leadership - Public School Administration ***
fieldEducation Policy ***
fieldElectrical Engg. ***
fieldelectrical engineering ***
fieldElectrical Engineering ***
fieldELECTRICAL ENGINEERING ***
fieldElementary/Childhood Education (MA) ***
fieldelementary education ***
fieldElementary Education ***
fieldElementary Education - Preservice ***
fieldengineering ***
fieldEngineering ***
fieldEnglish ***
fieldFilm ***
fieldFinanace ***
fieldFinance ***
fieldFinance&Economics ***
fieldFinancial Engineering ***
fieldfinancial math ***
fieldfrench ***
fieldFundraising Management ***
fieldGeneral management/finance ***
fieldgenetics ***
fieldGenetics ***
fieldGenetics & Development ***
fieldGerman Literature ***
fieldGS Postbacc PreMed ***
fieldIndustrial Engineering ***
fieldIndustrial Engineering/Operations Research ***
fieldInternational Affairs ***
fieldInternational Affairs - Economic Policy ***
fieldInternational Educational Development ***
fieldInternational Politics ***
fieldJapanese Literature ***
fieldjournalism ***
fieldJournalism ***
fieldlaw ***
fieldLaw ***
fieldLAW ***
fieldLaw and English Literature (J.D./Ph.D.) ***
fieldLaw/Business ***
fieldMarketing ***
fieldMA Science Education ***
fieldMasters in Public Administration ***
fieldMasters of Industrial Engineering ***
fieldMasters of Social Work ***
fieldMasters of Social Work&Education ***
fieldMath ***
fieldMathematical Finance ***
fieldMathematics ***
fieldMBA ***
fieldMBA / Master of International Affairs (SIPA) ***
fieldMBA - Private Equity / Real Estate ***
fieldMechanical Engineering ***
fieldmedical informatics ***
fieldmedicine ***
fieldMedicine ***
fieldmedicine and biochemistry ***
fieldMFA Acting Program ***
fieldMFA Creative Writing ***
fieldMFA -Film ***
fieldMFA Writing ***
fieldmicrobiology ***
fieldMolecular Biology ***
fieldmoney ***
fieldNonfiction writing ***
fieldNonFiction Writing ***
fieldnutrition ***
fieldNutritiron ***
fieldOperations Research ***
fieldOperations Research (SEAS) ***
fieldOrganizational Psychology ***
fieldphilosophy ***
fieldPhilosophy ***
fieldPhilosophy and Physics ***
fieldPhilosophy (Ph.D.) ***
fieldPolish ***
fieldpolitical science ***
fieldPolitical Science ***
fieldpsychology ***
fieldPsychology ***
fieldpsychology and english ***
fieldPublic Health ***
fieldPublic Policy ***
fieldReligion ***
fieldSchool Psychology ***
fieldSIPA - Energy ***
fieldsocial work ***
fieldSocial Work ***
fieldSocial Work/SIPA ***
fieldsociology ***
fieldSociology ***
fieldSociology and Education ***
fieldSpeech Language Pathology ***
fieldStatistics ***
fieldTC (Health Ed) ***
fieldTheater ***
fieldTheatre Management & Producing ***
fieldUndergrad - GS ***
fieldWriting: Literary Nonfiction ***
race ***
fromAlabama ***
fromAlbuquerque, NM ***
fromAnn Arbor ***
fromAnn Arbor, MI
fromArgentina ***
fromArizona
fromAsia, Singapore ***
fromAtlanta, GA ***
fromAustin, TX ***
fromBaltimore
fromBangladesh ***
fromBEIJING, CHINA
fromBogota, Colombia ***
fromBorn in Iran
fromBorn in Montana, raised in South Jersey (nr. Philadelphia)
fromBoston ***
fromboston, ma
fromBoston, MA ***
fromBoulder, Colorado
fromBowdoin College ***
fromBrandeis University
fromBrazil ***
fromBrooklyn ***
frombrooklyn ny ***
frombrooklyn, ny
fromBrooklyn, NY ***
fromBudapest ***
fromBurlington, Vermont
fromcalifornia
fromCalifornia ***
fromCanada ***
fromChicago ***
fromChile ***
fromChina ***
fromCincinnati, OH
fromCincinnati, Ohio ***
fromColorado ***
fromConnecticut ***
fromczech republic ***
fromEngland ***
fromFlorida ***
fromFlorida and Virginia ***
fromfrance
fromFrance ***
fromGenova, Italy ***
fromGeorgia, USA ***
fromGermany ***
fromGreece ***
fromGreece/Germany ***
[ reached getOption("max.print") -- omitted 346 rows ]
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 5434.1 on 3983 degrees of freedom
Residual deviance: 33520.6 on 3674 degrees of freedom
AIC: 34141
Number of Fisher Scoring iterations: 25table(expectativa_realidade$categoria_prevista, expectativa_realidade$dec)
no yes
no_pred 2060 236
yes_pred 229 1459A partir dos dados, percebemos que alguns níveis das variáveis from e field se tornaram importantes preditoras; além destas, também se destacam pid, int_corr, samerace1, age_o e age. Sendo assim, percebemos que estes fatores têm efeito bastante significativo para a ocorrência de um 2º encontro.
---
title: 'Regressão Logística e match'
author: "Hadrizia Santos"
date: "05 de agosto de 2018"
output: 
  html_notebook:
    theme: readable
    fig_width: 8
    toc: true
    toc_float: true
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(here)
library(readr)
library(tidyverse)
library(Amelia)
library(GGally)
library(pscl)
library(broom)
library(tidyr)

theme_set(theme_bw())
```

## Os dados
os dados descrevem 5000 encontros relâmpagos de 4 minutos envolvendo 310 jovens americanos. Os dados originais foram coletados por professores da Columbia Business School [no experimento descrito aqui](http://faculty.chicagobooth.edu/emir.kamenica/documents/genderDifferences.pdf). Os participantes tinham vários encontros de 4 minutos por noite. Após cada um, preenchiam fichas avaliando aqueles com quem se encontraram. Cada linha nos dados representa um desses encontros.

## As perguntas

1. Dentre os fatores que você acha que podem ter efeito no match, quais fatores têm efeito significativo na chance de p1 decidir se encontrar novamente com p2? E como é esse efeito (positivo/negativo)?

2. Que fatores nos dados têm mais efeito na chance de um participante querer se encontrar novamente com outro?

```{r warning=FALSE, message=FALSE}
datings <- readr::read_csv("https://raw.githubusercontent.com/nazareno/ciencia-de-dados-1/master/5-regressao/speed-dating/speed-dating2.csv",
                           col_types = cols(
                             .default = col_integer(),
                            int_corr = col_double(),
                            field = col_character(),
                            from = col_character(),
                            career = col_character(),
                            attr = col_double(),
                            sinc = col_double(),
                            intel = col_double(),
                            fun = col_double(),
                            amb = col_double(),
                            shar = col_double(),
                            like = col_double(),
                            prob = col_double(),
                            match_es = col_double(),
                            attr3_s = col_double(),
                            sinc3_s = col_double(),
                            intel3_s = col_double(),
                            fun3_s = col_double(),
                            amb3_s = col_double(),
                            dec = col_character()
                           )) %>%
  mutate(from = as.factor(from),
         gender = as.factor(gender),
         samerace = as.factor(samerace),
         dec = as.factor(dec), 
         gender = as.factor(gender))

datings <- datings %>%
  select(iid, gender, order, pid, int_corr, samerace, age_o, age, field,
         race, from, career, sports, tvsports, exercise, dining, museums,
         art, hiking, gaming, clubbing, reading, tv, theater, movies, 
         concerts, music, shopping, yoga, attr, sinc, intel, fun, amb, 
         shar, like, prob, attr3_s, sinc3_s, intel3_s, fun3_s, amb3_s, dec)

datings %>% glimpse()
```
Os dados estão distribuídos nas seguintes colunas:

- `iid` : id do participante p1 no encontro
- `gender` : sexo do p1, 0 = mulher
- `order` : dos vários encontros realizados em uma noite, esse foi o n-ésimo, segundo essa variável
- `pid` : id do participante p2
- `int_corr` : correlação entre os interesses de p1 e p2
- `samerace` : p1 e p2 são da mesma raça?
- `age_o` : idade de p2
- `age` : idade de p1
- `field` : campo de estudo de p1
- `race` : raça de p1. O código é Black/African American=1; European/Caucasian-American=2; Latino/Hispanic American=3; Asian/Pacific Islander/Asian-American=4;	Native American=5; 	Other=6
- `from` : de onde p1 é
- `career` : que carreira p1 quer seguir
- `sports`, `tvsports`, `exercise`, `dining`, `museums`, `art`, `hiking`, `gaming`, `clubbing`, `reading`, `tv`, `theater`, `movies`, `concerts`, `music`, `shopping`, `yoga` : De 1 a 10, quão interessado p1 é em cada uma dessas atividades.  
- `attr` : quão atraente p1 achou p2
- `sinc` : quão sincero p1 achou p2
- `intel` : quão inteligente p1 achou p2    
- `fun` : quão divertido p1 achou p2
- `amb` : quão ambicioso p1 achou p2
- `shar` : quanto p1 achou que compartilha interesses e hobbies com p2
- `like` : no geral, quanto p1 gostou de p2?
- `prob` : que probabiliade p1 acha que p2 tem de querer se encontrar novamente com p- (escala 1-10)
- `attr3_s` : quanto p1 acha que é atraente
- `sinc3_s` : quanto p1 acha que é sincero
- `intel3_s` : quanto p1 acha que é inteligente
- `fun3_s` : quanto p1 acha que é divertido
- `amb3_s` : quanto p1 acha que é ambicioso
- `dec`: p1 gostaria de se encontrar novamente com p2?

<br>

#### Tratamento nos dados

Com uma pequena amostra dos dados, já percebe-se o quanto de dados faltosos exitem em algumas colunas. Essa falta pode ser melhor observada no gráfico abaixo:
```{r}
missmap(datings)
```

As variáveis que possui mais dados faltosos são as váriaveis attr3_s, sinc3_s, intel3_s, fun3_s, amb3_s. Para lidar com isso, substituirei o dado faltando pela mediana da variável.
```{r}
f = function(x){
   x<-as.numeric(as.character(x)) 
   x[is.na(x)] = median(x, na.rm=TRUE) 
   x
}

temp <- data.frame(apply(datings,2,f)) 

datings$attr3_s <- temp$attr3_s
datings$sinc3_s <- temp$sinc3_s
datings$intel3_s <- temp$intel3_s
datings$fun3_s <- temp$fun3_s
datings$amb3_s <- temp$amb3_s

```

Observando o resultado:

```{r}
missmap(datings)
```

Concluída a fase de processamento desses dados, agora é hora de criar um modelo.

#### Seleção das variáveis

As variáveis escolhidas para servirem de preditoras para o modelo são like, attr e shar, por achar que elas são realmente decisivas para a possibilidade de um 2º encontro.

```{r warning=FALSE, message=FALSE}
model = glm(dec ~ ., 
            data = datings %>%
              select(like, attr, shar, dec) %>%
              na.omit(), 
            family = "binomial")

tidy(model, conf.int = TRUE, exponentiate = TRUE)
glance(model)
pR2(model)

summary(model)
```

```{r}
expectativa_realidade = augment(model, 
                                type.predict = "response") 

expectativa_realidade = expectativa_realidade %>% 
  mutate(categoria_prevista = ifelse(.fitted > .5, "yes_pred", "no_pred"))

table(expectativa_realidade$categoria_prevista, expectativa_realidade$dec)
```

```{r}
m = datings %>%
  select(like, attr, shar, dec) %>%
  na.omit() %>% 
  modelr::data_grid(like, attr, shar, dec)
mm = augment(model, 
             newdata = m, 
             type.predict = "response")

ggplot(mm, aes(x = like, colour = shar, size = attr)) + 
  geom_line(aes(y = .fitted)) + 
  facet_grid(.~like) +
  labs(x = "Like", y = "Valor predito para dec")

```

### 1. Dentre os fatores que você acha que podem ter efeito no match, quais fatores têm efeito significativo na chance de p1 decidir se encontrar novamente com p2? E como é esse efeito (positivo/negativo)?

De acordo com o modelo e as variáveis escolhidas, todas possuem grande e positivo efeito na chance de p1 decidir se encontrar novamente com p2. São elas: **like**, **shar** e **attr**.

### 2. Que fatores nos dados têm mais efeito na chance de um participante querer se encontrar novamente com outro?

```{r}
model = glm(dec ~ ., 
            data = datings %>%
              select(gender, order, pid, int_corr, samerace, age_o, age,
                     field, race, from, career, attr, sinc, intel, fun,
                     amb, shar, like, prob, attr3_s, sinc3_s, intel3_s,
                     fun3_s, amb3_s, dec) %>%
              na.omit(), 
            family = "binomial")

tidy(model, conf.int = TRUE, exponentiate = TRUE)
glance(model)
pR2(model)

summary(model)
```

```{r}
expectativa_realidade = augment(model, 
                                type.predict = "response") 

expectativa_realidade = expectativa_realidade %>% 
  mutate(categoria_prevista = ifelse(.fitted > .5, "yes_pred", "no_pred"))

table(expectativa_realidade$categoria_prevista, expectativa_realidade$dec)
```

A partir dos dados, percebemos que alguns níveis das variáveis **from** e **field** se tornaram importantes preditoras; além destas, também se destacam **pid**, **int_corr**, **samerace1**, **age_o** e **age**. Sendo assim, percebemos que estes fatores têm efeito bastante significativo para a ocorrência de um 2º encontro.

