Linked Questions

2
votes
1answer
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.
0
votes
1answer
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 ...
0
votes
0answers
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 ...
66
votes
1answer
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 ...
11
votes
2answers
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 ...
12
votes
2answers
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....
3
votes
1answer
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 ...
5
votes
3answers
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 ...
1
vote
2answers
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 ...
4
votes
1answer
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 ...
0
votes
1answer
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) ...
3
votes
2answers
575 views

Predicting an output based on whether a variable is above or below a threshold

I want to create a linear regression model to predict an output that uses two different coefficients based on some threshold within the data. For example: df: <...
0
votes
2answers
332 views

Binary logistic regression: Wrong labels for the regression coefficients [duplicate]

I carried out a binary logistic regression using glm. Below you can see the (modified) output. I included -1 to display all values, even the baseline which is ...