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

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Testing the statistical significance of regression coefficients in a logistic regression

Are only the p-values relevant when testing the regression coefficients of a logistic regression? Does the z-value of a coefficient give any further information about the significance of the ...
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Logistic Regression with significant variables and bad predictions

Can someone explain to me how my stepwise logistic regression model has variables with very low p-values but does not predict very well?
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How to do pooled logistic regression in Stata? [on hold]

How do you go about doing pooled logistic regression in Stata? Can't find any info online!
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Effect of 4 options on one column of variables

Column 1 Column 2 1 56 3 18 3 27 2 31 4 22 1 32 2 65 4 39 How do I find out how much a 1,2,3 or 4 ...
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When do coefficients estimated by logistic and logit-linear regression differ?

When modelling continuous proportions (e.g. proportional vegetation cover at survey quadrats, or proportion of time engaged in an activity), logistic regression is considered inappropriate (e.g. ...
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Cross validation for logistic regression in R [on hold]

I am doing a K-fold cross validation for a logistic regression. I first used the PCA to reduce the dimensionality. Then I used those principal components to build the regression model. My problem is ...
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Should outliers be remove first before identifying influential observations?

I have constructed a logistic regression model. I used half-normal probability plot and detected two outliers, which I removed. Then I want to identify influential observation, in order to improve the ...
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18 views

Calculating Standard Deviation when given sample size, mean difference & p value

I am trying to pool data in my meta-analysis and i need MEAN & SD. However the study has reported sample size (27), before (11.8) & after mean (11.9), and p value (0.540). I need the SD. ...
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29 views

compare muliptle probabilities

I have built three decision tree model to predict the response for three offers (One model per offer). I want to find the best offer for each customer based on the predicted probabilities. All offers ...
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Why does logistic regression not work in p > n setting?

I have started working on a wide (p > n) genetic data set. It is recommended to me to use a regularization technique (such as LASSO or Ridge regression) in order to reduce the number of genes to be ...
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7 views

Regression with both binary independent and dependent variables? [duplicate]

Is it okay to do a logistic regression where both independent and dependent variables are binary? They are both 0 or 1. Does it matter how many independent variables? If it is okay to do, how can I do ...
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main effect in logistic regression with the presence of interaction

I just have a question about how to get the main effect in the presence of interaction effect. I have two cohort: say cohort A and cohort B . For cohort A, I have this code as 1. Zero for cohort B. ...
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How should I handle measurement error in logistic/probit regression and what are its effects?

I am concerned with the problem where dependent discrete variable $R$ is to be modelled by continuous predictor $X^*$, which is subject to measurement error $u$ of the form $$X^*=X+u$$ ($X$ being the ...
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How to determine if the occurrence of two events is temporally connected?

I'm working on a dataset where I have dates as the main unit of analysis. I'm trying to see if two events are related; that is, if the first event happens, will the second event happen within a month ...
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Ordinal Logistic Regression using Fractional Polynomials in SAS

I am looking for Case Studies / Examples where Ordinal Logistic Regression Models ( using Fractional Polynomials for Continuous Predictor Variables ) are implemented in SAS ( SAS Macros )
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25 views

Values distribution issue in logistic regression [duplicate]

I have a problem with logistic regression. I have the following variables: Var1: 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 0 NA 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 ...
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Discriminant, logistic approach to attrition analytics

I have a few variables for each employee like tenure, age, marital status, certification, working away from home, #of OThours in an year, last promotion, performance rating, etc... I have these ...
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logistic regression prediction: changing interpretiation with changing prior

The data exist of 3 equally sized subsets: A, B and C belonging to two classes. A belongs to class 1. B and C belongs to class 2. So the prior probability of an observation coming out of class 1 or ...
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Gelman & Hill ARM - Question 5.11

Gelman Hill textbook has a question using election / voting data (http://goo.gl/ff8ryn); After fitting a logistic regression model for year 1964 using inncome, race, gender as a covariate, ...
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Larger p-values but less misclassification error in Logistic Regression

I was doing logistic regression in R on 'Smarket' data set available in the ISLR library. Since correlation between variables were less, I used all variables in my model and I was getting the ...
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19 views

Conflicting significance results in Exact Logistic Regression in Stata [closed]

Exact logistic regression in Stata where the X is nominal with 6 categories, and Y is binary, is giving me an overall significant model but with no significant individual Wald tests. Why is this ...
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38 views

Interpretation of Odds Ratio of Zero

In logistic regression, does an odds ratio of zero make sense, and if so, what's the interpretation. I've only been able to locate one reference that specifically refers to OR of 0: Making Sense of ...
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Visualizing variability from graph

It is written in the book Applied Logistic Regression, Second Edition. By David W. Hosmer and Stanley Lemeshow , p.2 that a problem with the above graph is that the ...
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Does the position at which maximum distance occurs in a KS test make a difference?

From my understanding of the KS test, fromt the CDF of two datasets, it measures the distance between the two distributions at various points and and compares the 'maximum distance' to a predefined ...
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plot logistic regression line over heat plot

My data is binary with two linear independent variables. For both predictors, as they get bigger, there are more positive responses. I have plotted the data in a heatplot showing density of positive ...
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Characteristics Stability in Logistic Regression

Could someone please help me understand calculation of characteristics stability in logistic regression? What is a model point value in the formula?
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76 views

Logistic regression and dependent variables in R

I’m trying to do logistic regression, I utilize the following command: mylogit <- glm(Var0 ~Var1, data = mydata, family = "binomial") And I obtain a p-value ...
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how to measure awareness ,find estimates and analysis usingthe given lenior regressin equation [closed]

Want to measure awareness of energy efficiency , and star rating of appliances , sample Questions Do you know energy efficiency of appliances, Ans: yes(1)/ No(2)type, 2.Do you know star rating ...
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Observation period for risk modeling [closed]

In many risk models, researchers generally use 12 months of historical data to develop a logistic regression model. What are the points one should consider while choosing a period? Which statistical ...
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21 views

Classification Algorithm For Small Sample Sizes

I am looking at a problem now where I need to train a classification algorithm. There are only 2 classes, lets call them A and B, and I want a value between zero and one indicating the probability ...
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82 views

From the Perceptron rule to Gradient Descent: How are Perceptrons with a sigmoid activation function different from Logistic Regression?

Essentially, my question is that in multilayer Perceptrons, perceptrons are used with a sigmoid activation function. So that in the update rule $\hat{y}$ is calculated as $$\hat{y} = ...
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How to fix dummy variables when I calculate predicted probability on logistic regression?

My question is about predicted probabilities in logistic regression. Let me make an example, analyze the relationship marriage (1: married, 0: single) as dependent variable and sex (1: male, 0: ...
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census data in predictive models

This may be a trivial question but I just wanted to confirm my thoughts on it. If I wanted to predict customer habits (just assume predicting a 0 or 1) and use various data at the customer level but ...
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How to compute bias mean squared error and standard error in penalized logistic regression

I am working on my Ph.D. research on penalized logistic regression. In my simulation using R, I have run the following code: ...
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Should I split my independent variable into 2 groups?

I want to study if atheists/non-believers are having a harder time gaining results from 12-step facilitation treatment for alcohol dependence. My hypothesis is that a higher degree of belief in ...
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Interpretation of Odds in Probit Regression

Logistic regression is concerned about modelling log-odds, i.e. logits. Hence, the odds of the computed probabilities can be interpreted accordingly. However, when estimating a probit model, one could ...
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How to select observation window and performance window for churn prediction?

I have to built a customer churn model for of a teleco. The churn rate is 15 %. There is no particular campaign conducted because the dedicated sales reps go and try to retain customers whenever they ...
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Propensity score nearest neighbor matching with replacement and caliper

I am using the package MatchIt in R to perform propensity score matching. I have chosen to use nearest neighbor matching with a caliper of 0.2 and since in my case i have more cases than controls i ...
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effect of modifiers- case crossover

I am studying the effect of air pollution (PM) on health outcomes, and the characteristics that might modify this effect, using the case crossover analysis based on the conditional logistic ...
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What inputs, ideas or insight the community can offer on the subject “A simulation study of sample size for multilevel logistic regression.” [closed]

I have been assigned a topic on "A simulation study of sample size for multilevel logistic regression." I have searched the topic but found little reference on it. Could you please offer some ...
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What is out of time validation in logistic regression model?

I understand out of sample validation very well. Can you explain what is out of time validation? Context A team in my organization has build a churn model for a teleco. Churn rate is 27%. The models ...
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Does conditional logistic regression effectively “throw away” concordant pairs like McNemar's test?

We are interested in analyzing pre-post data from a randomized control trial. The outcome is binary and I am trying to explain the heuristics of the model formulation for conditional logistic ...
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Nonlinearity in OLS-models

I have a question connected to the OLS-Model's assumption of Linearity between parameters. What should be done if the assumption is not fulfilled? My second question is if I can use multinomial ...
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Relationship between $\beta_1$ and odds in simple logistic regression

I am taking a course in logistic regression, and currently my class is about to finish our discussion about simple logistic regression. My professor said that the following statement is correct: ...
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Comparison of log-likelihood of two non-nested models

I know I can only use the log-likelihoods of two models as selection criterion if they are nested. However, I don't understand this completely. Why isn't it possible to apply this reasoning to ...
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Standardizing variables and interpreting coefficient estimates

I standardized my explanatory variables so that each variable has a mean of 0 and standard deviation of 1 to improve convergence of the fitting algorithm and putting the estimated coefficients on the ...
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glmer and odds ratios [migrated]

Is there a way to calculate odds ratios for a glmer model? My model is defined as follows: ...
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Estimating width of logistic prediction interval from value of fitted coefficients only

On page 1530, left column, of a study by Redline, Tishler, Schluchter, Aylor, Clark and Graham (1999) (ungated version), one reads: Because there is uncertainty regarding the optimal cutoff values ...
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Change in order of predictors breaks logistic model estimation (glm, R)

I am fitting a binomial logistic regression in R using glm. By chance, I have found out that if I change the order of my predictor variables, glm fails to estimate the model. The message I get is ...
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Calculation of effect size for a mediation analysis with a dichotomous DV

I am using Haye's PROCESS macro to calculate the indirect effect (mediation) using a continuous IV, continuous mediator and a dichotomous DV. Haye's macro does not calculate an effect size for ...