Displaying 5 results from an estimated 5 matches for "genderfemale".
2012 Dec 30
3
Odds Ratio and Logistic Regression
...Min 1Q Median 3Q Max
-2.2094 0.4269 0.4269 0.8033 1.1911
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.9656 0.1477 6.538 6.25e-11 ***
povertyBelow poverty line -0.9978 0.3246 -3.074 0.00211 **
genderFEMALE 1.3840 0.2549 5.429 5.68e-08 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 494.81 on 499 degrees of freedom
Residual deviance: 457.13 on 497 degrees of freedom
AIC: 463.1...
2011 Dec 19
2
summary vs anova
...:
1) How do the p-values for smokes* in summary(model) relate to the
Pr(>F) for smokes in anova
2) what do the p-values for each of those smokes* mean exactly?
3) the summary above shows the values for diseasestate1 and diseasestate2
how can I get the p-value for diseasecontrol? (or, e.g. genderfemale)
thanks.
2012 Nov 29
1
instrumental variables regression using ivreg (AER) or tsls (sem)
...= cd.d)
Residuals:
Min 1Q Median 3Q Max
-3.1692 -0.8294 0.1502 0.8482 3.9537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.053604 1.675314 -1.226 0.2203
urbanyes -0.013588 0.046403 -0.293 0.7697
genderfemale -0.086700 0.036909 -2.349 0.0189 *
ethnicityafam -0.566524 0.051686 -10.961 < 2e-16 ***
ethnicityhispanic -0.529088 0.048429 -10.925 < 2e-16 ***
unemp 0.145806 0.006969 20.922 < 2e-16 ***
ed.pred 0.774340 0.120372 6.433 1.38e-10 ***
---...
2011 Feb 16
1
Saturated model in binomial glm
Hi all,
Could somebody be so kind to explain to me what is the saturated model
on which deviance and degrees of freedom are calculated when fitting a
binomial glm?
Everything makes sense if I fit the model using as response a vector of
proportions or a two-column matrix. But when the response is a factor
and counts are specified via the "weights" argument, I am kind of lost
as far as
2012 Dec 10
3
Warning message: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!
...:
svyglm(formula = trust ~ gender + edu + prov, design = des.1,
family = "binomial")
Survey design:
svydesign(~0, weights = ~weight, data = mat1)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.625909 0.156560 -3.998 6.87e-05 ***
genderFemale 0.013519 0.140574 0.096 0.923
edupost-secondary -0.011569 0.141528 -0.082 0.935
provPQ -0.006614 0.172105 -0.038 0.969
provatl 0.335166 0.297860 1.125 0.261
provwest -0.053862 0.174826 -0.308 0.758
---
Signif....