You can answer all of your questions with one single multiple logistic regression model.
I have 4 categorical variables in a dataset let X1, X2, X3 and Y. Where X1,X2 and Y is a binary random variable and X3 have 3 categories.
X1,X2 and X3 are independent variables and Y is the response variable
How can I study the association of X1and Y fixing/controlling X2 and X3?
How can I study the association of X2 and Y considering the four variables?
What's the better way to do that? Boxplots? Using wald.test?
The difference between stupidity and genius is that genius has its limits.
"Albert Einstein"
You can answer all of your questions with one single multiple logistic regression model.
Stop cowardice, ban guns!
The difference between stupidity and genius is that genius has its limits.
"Albert Einstein"
Well if you want to control for the other variables they all need to be in the same model. There are plots of logodds and differences but they usually require you to set the other variables to a reference.
Keep questions coming!
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My data following this format http://www.talkstats.com/showthread....for-count-data .
I want to verify is exists any association between severity of accident and use of helmet.
I want to verify if exists any association between severity of accident and speed.
I can show this association just with graphs in R?
The difference between stupidity and genius is that genius has its limits.
"Albert Einstein"
How I can make in R barplot's for this data
I want to make a barplot of each variable doingCode:Helmet Speed Not_Severe Severe Total Yes 10-15 10 6 16 Yes 15-20 10 12 22 Yes 20-25 22 13 35 No 10-15 15 4 19 No 15-20 18 9 27 No 20-25 6 15 21
- Barplot for Helmet : With 4 bar's, where I have (Yes,Not_Severe), (Yes,Severe), (No,Not_Severe), (No,Severe)
-Barplot for speed: With 6 bar's where I have (10-15,Not_severe), (10-15,Severe), (15-20,Not_severe),(15-20,Severe), (20-25,Not_severe),(20-25,Severe)
The difference between stupidity and genius is that genius has its limits.
"Albert Einstein"
Are there any suspected interactions, mediating effects, or collinearity? I would just think about that when graphing or interpreting the graphs.
Can we see the graphs?
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