# Model Assumption Visualizations [closed]

I have two data sets: one has a continuous response variable whereas the other discrete. I will use a linear model for the continuous data and a logit and probit model for the discrete data. What I want to do is to conduct some exploratory data analysis for both data sets. I am wondering what plots can be used to assess the model assumptions. I am also open to new thoughts. Any help would be greatly appreciated.

## closed as too broad by mkt, Michael Chernick, mdewey, whuber♦Jun 22 at 20:16

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

• Sorry, It should have been exploratory. Have corrected it – Günal Jan 18 '13 at 10:46
• What does your covariate data look like? e.g. continuous, ordinal, categorical (some mix of these). Furthermore you haven't really made any assumptions so it's hard to know what checks are useful, e.g. if you are doing OLS you are really just concerned about the error term having mean zero and constant variance (note that I have not made normality a requirement). – Jonathan Lisic Jan 18 '13 at 14:28
• I have 20 explanatory variable which are binary (0 or 1) and my response variable is continuous. Here is my model y_i = 1- Xbeta + delta_i + e_i, where delta_i is the i-th random error term and e_i is the i-th usual error term. I used maximum likelihood estimation. – Günal Jan 18 '13 at 14:33
• Thanks, you might want to update your question with this information though. So you have 20 covariates or potential covariates, and you also have a set of random effects? something of the form $Y_i = x_i \beta + Z_k \alpha + \epsilon$ where $Z_k$ is your random effect $\sim iid(0,\sigma_{\alpha}^2)$ and $\epsilon_i$ is distributed $\sim iid(0,\sigma^2)$ independent of $Z_k$. – Jonathan Lisic Jan 18 '13 at 15:07