Logistic Regression Standard Error
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Logit Regression
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Binary Logistic Regression Spss
takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Understanding standard errors in logistic regression up vote 2 down vote favorite logistic regression ppt I am having trouble understanding the meaning of the standard errors in my thesis analysis and whether they indicate that my data (and the estimates) are not good enough. I am performing an analysis with Stata, on immigrant-native gap in school performance (dependent variable = good / bad results) controlling for a variety of regressors. I used both logit and OLS and I adjusted for cluster at the school level. The regressors which are giving me trouble are some interaction terms logistic regression standard error of prediction between a dummy for country of origin and a dummy for having foreign friends (I included both base-variables in the model as well). In the logit estimation, more than one of the country*friend variables have a SE greater than 1 (up to 1.80 or so), and some of them are significant as well. This does not happen with the OLS. I am really confused on how to interpret this. I have always understood that high standard errors are not really a good sign, because it means that your data are too spread out. But still (some of) the coefficients are significant, which works perfect for me because it is the result I was looking for. Can I just ignore the SE? Or does it raise a red flag regarding my results? I usually just ignore the SE in regressions (I know, it is not really what one should do) but I can't recall any other example with such huge SE values. self-study logistic stata standard-error share|improve this question edited Mar 14 '14 at 5:37 Dimitriy V. Masterov 15.4k12461 asked Mar 12 '14 at 21:50 Maria 1112 1 How is it that you ran this model as both OLS and as a logistic regression? That doesn't make sense. Also, you state that you are adjusting for clustering in the data; that implies that this is a mixed-effects model, in which case it should be GLiMM or LMM, but you don't say anyth
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Logit Model Example
Local Segmented Errors-in-variables Estimation Least squares Ordinary least squares Linear (math) Partial Total Generalized Weighted Non-linear Non-negative Iteratively reweighted Ridge regression Least http://stats.stackexchange.com/questions/89810/understanding-standard-errors-in-logistic-regression absolute deviations Bayesian Bayesian multivariate Background Regression model validation Mean and predicted response Errors and residuals Goodness of fit Studentized residual Gauss–Markov theorem Statistics portal v t e "Logit model" redirects here. It is not to be confused with Logit function. In statistics, logistic regression, or https://en.wikipedia.org/wiki/Logistic_regression logit regression, or logit model[1] is a regression model where the dependent variable (DV) is categorical. This article covers the case of binary dependent variables—that is, where it can take only two values, such as pass/fail, win/lose, alive/dead or healthy/sick. Cases with more than two categories are referred to as multinomial logistic regression, or, if the multiple categories are ordered, as ordinal logistic regression.[2] Logistic regression was developed by statistician David Cox in 1958.[2][3] The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). As such it is not a classification method. It could be called a qualitative response/discrete choice model in the terminology of economics. Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables
standard errors into odds ratios is trivial in Stata: just add , or to the end of https://www.andrewheiss.com/blog/2016/04/25/convert-logistic-regression-standard-errors-to-odds-ratios-with-r/ a logit command: . use "http://www.ats.ucla.edu/stat/data/hsbdemo", clear . logit honors i.female math read, or Logistic regression Number of obs = 200 LR chi2(3) = 80.87 Prob > chi2 = 0.0000 Log likelihood = -75.209827 Pseudo R2 = 0.3496 ------------------------------------------------------------------------------ honors | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] logistic regression -------------+---------------------------------------------------------------- female | female | 3.173393 1.377573 2.66 0.008 1.35524 7.430728 math | 1.140779 .0370323 4.06 0.000 1.070458 1.21572 read | 1.078145 .029733 2.73 0.006 1.021417 1.138025 _cons | 1.99e-06 3.68e-06 -7.09 0.000 5.29e-08 .0000749 ------------------------------------------------------------------------------ Doing the same thing in R is a little trickier. Calculating odds ratios for coefficients logistic regression standard is trivial, and exp(coef(model)) gives the same results as Stata: # Load libraries library(dplyr) # Data frame manipulation library(readr) # Read CSVs nicely library(broom) # Convert models to data frames # Use treatment contrasts instead of polynomial contrasts for ordered factors options(contrasts=rep("contr.treatment", 2)) # Load and clean data df <- read_csv("http://www.ats.ucla.edu/stat/data/hsbdemo.csv") %>% mutate(honors = factor(honors, levels=c("not enrolled", "enrolled")), female = factor(female, levels=c("male", "female"), ordered=TRUE)) # Run model model <- glm(honors ~ female + math + read, data=df, family=binomial(link="logit")) summary(model) #> #> Call: #> glm(formula = honors ~ female + math + read, family = binomial(link = "logit"), #> data = df) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> -2.0055 -0.6061 -0.2730 0.4844 2.3953 #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -13.12749 1.85080 -7.093 1.31e-12 *** #> femalefemale 1.15480 0.43409 2.660 0.00781 ** #> math 0.13171 0.03246 4.058 4.96e-05 *** #> rea