# Dealing with a large number of predictors in Logistic Regression

Let's say I have a logistic regression model which predicts whether a consumer will buy an item based on about 10 consumer characteristics.

$$\begin{array}{rcl}Buy &=& B_0 + B_1\times Gender + B_2\times CreditType + B_3\times Education + B_4\times OwnsHome \\\phantom{Buy} && + B_5\times CarMake + B_6\times CarYear + B_7\times State + B_8\times Income + B_9\times Insurance \\ \phantom{Buy} &&+ B_{10}\times CarAccidents\end{array}$$

1. Is there ever an issue with including too many predictors in a logistic regression model? I'm not talking about insignificant variables or ones that may be related, but just the sheer number of variables included in a model.

2. With a larger number of predictors, how should one present the regression results in a meaningful manner? Is it just a matter of plotting the probability curve for $Y=1$, or are there "better" ways of doing this. I'd be doing this in R, so any help on that end would be appreciated.