Signs on logistic regression betas flip relative to observed - expected counts from chi-squared test I conduct a chi-squared analysis on some bins and conclude that an association between the bins and an event exists.  I then calculate logistic regression coefficients to validate my hypothesis. Also, I always look at the observed vs expected count in each bin (from chi-squared) to guesstimate logistic regression results.  
For example, if Bin 1 had an observed count = 152 but an expected count of 85, I would estimate that the over all chi-square result is likely to be significant and Bin 1 is likely to have a positive coefficient from the logistic regression. However, this Bin 1 has a negative coefficient from logistic regression.  Is my understanding wrong - when observed >> expected, logistic regression coefficient should be positive?(assuming here that results are significant).  
 A: That is quite clever.  Unfortunately, your understanding of what logistic regression coefficients for categorical data mean is wrong.  Logistic regression (really any form of regression) uses reference level coding by default to represent the levels of a categorical variable. (To learn more about reference level coding, see my answer here: Regression based for example on days of week.)  Thus, the beta for a given level of your categorical variable is telling you the difference between the mean level of the response (or the link-transformed mean in logistic regression) in that level of your categorical variable and the mean level of the response in the reference level.  They do not index how the mean level of the response differs from the expected count.  Moreover, they will vary if you change which level of your categorical variable is used for the reference level.  
On a different note, I don't see any need to "validate" your chi-squared test by running a logistic regression.  If the chi-squared test is appropriate for your data and hypothesis, you can run it and stop.  Nothing more is needed.  
