# Modeling techniques for dichotomous data

I have dichotomous data where some of my independent variables are categorical, some are continuous and some are binary (0/1).

My dependent is a binary response (Fail/NoFail, 0/1).

The data is some readings collected every day over a period of time.

The goal is to use this data and see if we can figure out cause of failure, the end response.

Example data format

Date, Type, Mileage, S1, S2, S3, ... , Response

03/02/2013,A,32000,1,0,1,..., 1

03/03/2013,B,32400,0,0,0,...,0

03/04/2013,C,45000,0,1,1,...,1

Can we do time series modeling? Any other modeling techniques that can be used?Any suggestions on what type of other exploratory analysis can be used to figure out patterns in data?

• Dichotomous is just another word for binary. So, you have a binary response. The question still seems very broad to me. Yes, given a date variable time series might make sense, but an aim of establishing cause implies emphasis on variables related to cause. Commented Feb 27, 2014 at 14:42
• Any Packages in R that can be used to model the data using time series, since the input variables are a mixture of continuous,Binary and categorical.
– shi
Commented Feb 27, 2014 at 14:48
• This sounds like some sort of survival analysis to me. Commented Feb 27, 2014 at 15:09
• @RioRaider's guess looks good to me. If you are following individuals to failure, keeping track of those individuals is essential. Commented Feb 27, 2014 at 15:57

Logit model would work here. Look up "logit" command in your statistical package. Your dependent variable would be converted to odds ratio. Independent continuous variables are obvious. Categorical variables will be converted into dummy/indicator variables. If you have a variable $x_i$ with M categories (unordered), you'll end up with M-1 dummies. You can find this all in the help for your command, such as the one in Stata.