## Solved – Overall significance of a categorical variables in logistic regression

I have seen two approaches in binary logistic regression with categorical independent variables (IV) with more than two levels. In one approach, a reference category for the IV is defined and the rest of the categories are tested regarding this reference category,thus obtaining p-values for each category compared to the reference category (which is what … Read more

## Solved – model for continuous dependent variable bounded between 0 and 1

I'm attempting a multiple regression model where the predicted variable is runoff ratio – the ratio of watershed discharge to the precipitation input. This should generally be bounded [0,1], however, due to measurement error some values > 1 occur. Originally, I modeled this with the predicted variable un-transformed, but logistic regression has been suggested to … Read more

## Solved – How to fit a generalized logistic function

I'm trying to fit models that predict probabilities: \$h(X,B) rightarrow (0,1)\$ It struck me that for for a great many cases logistic regression seems like a bad link function, I expect an S-curve, but I also expect bounds on the predicted probability to be much larger than 0, and much less than 1. For example, … Read more

## Solved – binary logit regression – which test apply for detecting heteroskedasticity

After reading a lot of different papers and a lot of different posts on the internet I still don't have a clue how to test on heteroskedasticity with my logistic regression (binary). The White test works only for OLS regression right? Best Answer Heteroscedasticity as such is not the main worry in logistic regression. For … Read more

## Solved – How does the power of a logistic regression and a t-test compare

Is the power of a logistic regression and a t-test equivalent? If so, they should be "data density equivalent" by which I mean that the same number of underlying observations yields the same power given a fixed alpha of .05. Consider two cases: [The parametric t-test]: 30 draws from a binomial observation are made and … Read more

## Solved – Difference in output between SAS’s proc genmod and R’s glm

I'm trying to replicate a model fitted in SAS in R but the fit I'm getting gives me slightly different coefficients and standard errors. Data: testdata <- data.frame(matrix(c("f","Test", 1.75, 16, 0, 16, 0, 1, 1, "m", "Test", 1.75, 15, 1, 16, 6.25, 1, 0, "f", "Test", 2.75, 4, 12, 16, 75, 1, 1, "m", "Test", … Read more

## Solved – Difference in output between SAS’s proc genmod and R’s glm

I'm trying to replicate a model fitted in SAS in R but the fit I'm getting gives me slightly different coefficients and standard errors. Data: testdata <- data.frame(matrix(c("f","Test", 1.75, 16, 0, 16, 0, 1, 1, "m", "Test", 1.75, 15, 1, 16, 6.25, 1, 0, "f", "Test", 2.75, 4, 12, 16, 75, 1, 1, "m", "Test", … Read more

## Solved – How to interpret interaction continuous variables in logistic regression

I am struggling to understand and interpret the interaction term in a logistic regression. The explanatory variables are temperature (categorical), gonad weight (continuous) and nnd (continuous). Below the reduced model: model2012nnd = glm(fullyspawned ~ temperature + gonad + nnd+gonad:nnd, family=quasibinomial(link = logit), data=spaw) summary(model2012nnd) # # Call: # glm(formula = fullyspawned ~ temperature + gonad … Read more

## Solved – How to interpret interaction continuous variables in logistic regression

I am struggling to understand and interpret the interaction term in a logistic regression. The explanatory variables are temperature (categorical), gonad weight (continuous) and nnd (continuous). Below the reduced model: model2012nnd = glm(fullyspawned ~ temperature + gonad + nnd+gonad:nnd, family=quasibinomial(link = logit), data=spaw) summary(model2012nnd) # # Call: # glm(formula = fullyspawned ~ temperature + gonad … Read more

## Solved – Observed vs predicted values from a logit model

I have a logit model and am trying to understand and compare the predicted and observed values generated by the model. Let's say data set had 100 values and I generate all the predicted probabilities, and then I find the actual probabilities from the data set. If I'm comparing the predicted vs observed values, I'm … Read more