Logistic Regression - Only Dummy Variables I'm working on a problem where all my variables are dummy variables (i.e. I have 5 dummy variables and a binary dependent variable). I'm exploring how each variable affects my dependent variable. My dataset is from a questionnaire with a sample size of around 2000. All the responses are either yes (= 1) or no (= 0). Therefore, I'm only able to use dummy variables for both my dependent and independent variables.
I came across the logistic regression and this model seems to fit my dataset (my data has independence of observations, my data does not follow a normal distribution and the dependent variable is mutually exclusive). However, I do have a concern with regard to the fact that I do not have any continuous variables and I have yet to come across an example of a logistic regression where all the variables are dummies.
Is a logistic regression model appropriate for my data?
Thank you!

As @John Madden said, there is nothing wrong with that.
Basically, your model will look like this:
$$\mathbb{P}(Y_i=1)=f(X_i)$$
Usually, $f$ is defined like this:
$$f(X_i)=\frac{e^{\sum_{j=0}^k \beta_j x_{i,j}}}{1+e^{\sum_{j=0}^k \beta_j x_{i,j}}}$$ where $x_{i,j}$ is the $i^\text{th}$ observation of the $k^\text{th}$ variable.
Having $x_{i,j}\in \{0,1\}$ is not an issue.
