Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression

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Interpreting the direction of an interaction effect in Binary Logistic Regression

I have reviewed prior posts in the forum and can't seem to find an answer to a problem I am having with interpreting an interaction: The goal: interpret the direction of the interaction term. ...
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15 views

Factors for binary variables [on hold]

Is it necessary to convert binary variables to factors? Or should I do it only if I have categorical (>2 levels) vars? Also, does it depend on whether a binary variable is a predictor or a response ...
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61 views

Multicollinearity with highly safe t-statistics but VIF of 13

If all of my coefficients in my logsitic model have really perfect t-statistics that all show sufficiently high significance but have two coefficients that have high VIF like 13-14 with sample size of ...
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How do I interpret the mice worm plot used for diagnostic?

I am using the mice package to impute missing data for a logistic model (relative to credit risk). In my case missing data are present only on covariates and not in the independent variable (composed ...
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What is maximum likelihood estimation in logistic regression? [duplicate]

Can you please explain in simple way. Is it so important in logistic regression?
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Logistic regression Effect using R, moderation plot

So, im in a bit of trouble here. I am using R (i'm very new at this), and i'm trying to plot the probability effects of a interaction effect, using the effects package. This is what the plot shows ...
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51 views

Manually calculating logistic regression coefficient

I am taking this short example from wiki:https://en.wikipedia.org/wiki/Logistic_regression ...
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correlation between constant and explanatory variable [duplicate]

I have tried to find earlier question but have not been able to find discussions about this issue: If you build a multivariate logistic regression and see strong correlations (0.8) between one of the ...
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33 views

What happens when GLM (in R) is given 2 correlated variables?

I have this data where I chose the feature as - Amount of credit and the variable to be predicted is the Credit Rating (good or bad). I ran the logistic regression and I got an accuracy of around 67%...
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Is multicolinearity problem ignorable under this situation?

When I run the logistic regression, two independent variables have VIF values greater than 10 like 13 or so. Logistic regression is the one I will use to measure the overall change in the dependent ...
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Logistic Regression or Linear Regression

Say I have the following hypothetical scenario: I have a material that I can freely configure, starting with 10 different strength properties. ...
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multicollinearity in logistic regression

Which is the best way to check for multicollinearity between two binary explanatory variables in logistic regression..? I use SPSS, if anyone could answer with special regards to that programme it ...
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Is it possible to use ordered categorical independent variables in logistic regression?

I'm trying to model a logistic regression in R between two simple variables: Rating: An independent ordered categorical one, ranging from 1 to 99 (1, 2, 3, 4, 5, 99 in particular, 1 is the best) ...
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Appropriate logistic regression model and log- likelihood

Are two simple independent causal samples from two distributions of bernoulli medium respectively: $i=1,...,10$ $ π_1 = e^{B_1} / (1 + e^{B_1})$ $i=1,...,10$ $ π_2 = e^{B_1 + B_2} / (1 + e^{B_1 +...
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Lasso logistic regression with GLMNET and fixed effects

I have a pretty general question. Suppose one collected data on investments in companies. Further, one wants to find out if some investors are better than others based on investment success (1/0 | ...
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41 views

Hypothesis testing with quotient of regression coefficients

Suppose we have the following multiple logistic regression model $\beta_0 + \beta_1 X_1 + \beta_2 X_2$, where $X_1$ and $X_2$ are binary variables, and $\theta = \beta_1 / \beta_2$. Then I have two ...
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Does AUC for multiple logistic regression make sense if prediction is not the goal?

Does it makes sense to calculate the AUC if I do not want to use my multiple logistic regression model for predictions? I only want to calculate some odds ratios and test if the variables in my model ...
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56 views

Properties of conditional maximum likelihood estimators

I am trying to find a source that describes the properties of conditional likelihood estimates like those obtained from conditional logistic regression?
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Missing Value in SPSS Output

I'm running a multinomial regression and I can't figure out why SPSS is giving me a missing value under ACountry. Any thoughts? Thanks!
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Why are the estimated probabilities of event from a multivariate logistic regression model equivalent to the crude event rates?

I have a large dataset (19k) and I am using logistic regression to estimate probabilities of experiencing an event at the patient level. I am interested in looking at the effect of a facility ...
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Is Logistic Regression Appropriate for this Question?

I am attempting to predict the ranking of NBA teams next season based on the number of games they won this season. To do this, I thought I could use a logistic regression with historical data. As ...
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Interpretation of coefficients in logistic regression output

I am doing logistic regression in R on a binary dependent variable with only one independent variable. I found the odd ratio as 0.99 for an outcomes. This can be shown in following. Odds ratio is ...
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linear and logistic regression

how are relationship between coefficient of linear regression and logistic regression (or odds ratio)? I want compare coefficient of linear regression with odds ratios of logistic regression. Is there ...
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Where does the conditional part of conditional logistic regression come from?

I am trying to fully understand where the conditional part of the term conditional logistic regression comes from. In particular, I can't seem to find a source in the literature that emphasizes that ...
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Is it possible to run multiple logistic regression for small sample size?

I have collected data, there are 300 non-injury and only 17 injury. Four categorical variables are significant according to Chi-squire, then I used Multiple logistic regression for significant ...
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Variables derived from factor analysis as a response variable in logistic regression

I have a dataset derived from a questionnaire filled out by high school students, with 7 variables (one continuous and six binary). Three variables(binary) are related to "interest in physics". Is ...
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aggregating outliers [closed]

Will someone, anyone (with statistical expertise) explain to me in simple plain english what exactly the statistical term aggregating outliers mean. I have a PhD and I had to take my fair share of ...
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135 views

Normalizing logistic regression coefficients?

With my limited understanding of the logistic regression, I understand that the coefficients in logistic regression are the odds ratios. Does it make send to normalize them (divide each one over the ...
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Relative contribution of variables in explaining the dependent variable in a logistic model

we are using a logistic regression and I want to know the relative contribution of each explanatory variable in explaining the dependent variable. that is basically rank the variables according to ...
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Multinomial Model of Discrete Choice

Can anyone explain me the differences between Multinomial Logit Model and Conditional Multinomial Logit Model? Multinomial Logit Model $$P(y_n=j|z_n=z)=\frac{exp(z'a_j)}{1+\sum exp(z'a)}$$ ...
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How to create a regression model if data points are structure as seen in the graph?

This is a graph of revenues for different products with the Y-axis showing normalized revenues (mean of 3 and SD of 1) and X-axis is weeks. I need do a regression analysis of sorts on this data and am ...
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Independence of observations violated

I discovered a (probably) major flaw in my research design. I am examining investors and their investment performance. Or in other words, I want to investigate if some investors are better than ...
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42 views

multilevel model with categorical outcome in R?

I am examining social interaction data in individuals within two groups. Each social encounter has been coded to one of 4 categories, and these encounters are nested within individual, whom are nested ...
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Investigating main effect after controlling for guessing

I'm doing a multilevel logistic regression with the dependent variable being word learning scores (Score). There are two independent variables: Condition (Experimental / Control) and WordType (Cognate ...
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Removing highly correlated variables in logistic regression in r

I am developing a logistic regression model on a large dataset consisting of 15 variables and 200k observations. In initial model fitting, I find variables - "Purchase Frequency" and "Average Payment ...
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Incorporate an external estimate of probability as predictor in a logistic regression model

I am predicting a binary outcome (e.g., credit default) with logistic regression. For each observation, in addition to my own observed predictors, I have obtained a probability estimate from an ...
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in SAS: Why are the CI for OR and RR in proc freq more narrow than in a logistic regression for a 2x2 table

I want to estimate risk for a certain outcome. For example age on disease X. I coded dummy variables from ordinal and nominal data (age, gender, etc) to create 2x2 tables. So, I turn my three age ...
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Can I use hierarchical logistic regression for a dependent variable composed of multiple levels

My dependent variable is disclosure of a company. It is measured using an index that is composed of 50 questions scored "0" or "1" and three sub-indices (financial disclosure, social and environmental ...
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Can I use hierarchical logistic regression to run a regression using a dependent variable with different levels?

My dependent variable is disclosure of a company. It is measured using an index that is composed of 50 questions scored "0" or "1" and three sub-indices (financial disclosure, social and environmental ...
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Logistic regression result contradicts intuitive reading of visual data

I just ran the following on SMA.sav file in SAS. The data file can be accessed here. ...
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Confidence interval sum fitted probabilities GLM

Let CI$_i:=logit^{-1}(\hat{\mathcal S}_i \pm 1.96*$ SE$(\hat{\mathcal S}_i))$ denote the $n$ Wald confidence intervals of the fitted probabilities $\{\hat{p}_i\}_{i=1,\dots,n}, \hat{p}_i:=\hat{p}_i(x|\...
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Can I flip the null and alternative hypotheses for logit regression in R?

I would like to accept the hypothesis that a variable is insignificant in determining the dependent variable of a logistic regression, but in R the glm summary which contains the p-values for each ...
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analysing interaction between binary categorical factors which don't explain the whole variation

I have a large dataset with 2 binary variables relating to gender & ethnicity, and one output variable, also binary. The overall incidence of the output variable is roughly 70% true. Running ...
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Logistic regression and latent data

Assume a simple logistic regression model: given binary data $y_1,\ldots,y_N$ where for each $1 \leq i \leq N$ the outcome of $y_i$ depends on one variable. The succes probability is $p_i = \mathbb{P}(...