269 views

### Clear explanation of dummy variable trap [duplicate]

I have a confusion in multiple regression about dummy variable trap, so far I had seen tutorials explaining about dummy variable trap and multicollinearity but I'm unable to understand it fully.
318 views

### How to overcome Coefficients: (4 not defined because of singularities) [duplicate]

Stats is not my strong point but trying to run a regression. I'm aware that it happens because some of these variables are perfectly collinear. However, I do not know how to fix this? Any help would ...
40 views

### What happens in a regression setting if you code n dummy variables for a categorical variable with n categories? [duplicate]

I understand the usual procedure to code categorical variables is to convert n categories into n-1 coded variables. For example, the categorical variable colour with levels red/green/blue could be ...
69k views

### What correlation makes a matrix singular and what are implications of singularity or near-singularity?

I am doing some calculations on different matrices (mainly in logistic regression) and I commonly get the error "Matrix is singular", where I have to go back and remove the correlated variables. My ...
16k views

### Regression based for example on days of week

I need a little help to move in the right direction. It's a long time since I studied any stats and the jargon seems to have changed. Imagine that I have a set of car-related data such as Journey ...
52k views

### Understanding dummy (manual or automated) variable creation in GLM

If a factor variable (e.g. gender with levels M and F) is used in the glm formula, dummy variable(s) are created, and can be found in the glm model summary along with their associated coefficients (e....
1k views

### How can logistic regression have a factorial predictor and no intercept?

I tried a regression in the form ${\rm logit}(Y) = {\rm coefficient}\times X + 0 + e$, where $Y$ is a binomial variable and $X$ is a factor variable with $n$ levels. I noticed that removing the ...
832 views

### Removing attributes with few observations in R

I have roughly 15 variables / attributes characterizing 6k customers in my data set. As they are categorical I have transformed them into 1 attribute for each possible value (1-out-of-K coding). An ...
2k views

### Multicollinearity and the intercept term with categorial variables

We're given a regression equation with two dummy variables which are perfectly collinear. $$y_i = \beta_1 D1_i + \beta_2 D2_i + e_i$$ where $D2_i = 1-D1_i$. Can we estimate this model using least ...
492 views

### Is there something called “mean coding” (like dummy coding & effect coding) in regression models?

When we perform a regression analysis with categorical predictors, we can use (1, 0), called "dummy coding". The coefficients in this case represent the deviation of the groups' means from the mean of ...
846 views

### NA produced in linear regression model

I have read similar posts to this but my problem is not resolved by the answers given. I want to do a v simple linear regression to see if bite incidence is related to district, zone (vacc or control) ...