The histogram of the deviance residuals shows the distribution of the residuals for all observations.
The interpretation of the plot is the same whether you use deviance residuals or Pearson residuals. When the model uses the logit link function, the distribution of the deviance residuals is closer to the distribution of residuals from a least squares regression model. The deviance residuals and the Pearson residuals become more similar as the number of trials for each combination of predictor settings increases.
Pattern  What the pattern may indicate 

A long tail in one direction  Skewness 
A bar that is far away from the other bars  An outlier 
Because the appearance of a histogram depends on the number of intervals used to group the data, don't use a histogram to assess the normality of the residuals. Instead, use a normal probability plot.
The normal probability plot of the residuals displays the residuals versus their expected values when the distribution is normal.
The interpretation of the plot is the same whether you use deviance residuals or Pearson residuals. When the model uses the logit link function, the distribution of the deviance residuals is closer to the distribution of residuals from a least squares regression model. The deviance residuals and the Pearson residuals become more similar as the number of trials for each combination of predictor settings increases.
Use the normal probability plot of the residuals to verify the assumption that the residuals are normally distributed. The normal probability plot of the residuals should approximately follow a straight line.
If you see a nonnormal pattern, use the other residual plots to check for other problems with the model, such as missing terms or a time order effect. If the residuals do not follow a normal distribution, the normal approximation confidence intervals and Wald test pvalues can be inaccurate.
In binary logistic regression, Minitab does not provide this plot when the data are Binary Response/Frequency format (single trial per row).
The interpretation of the plot is the same whether you use deviance residuals or Pearson residuals. When the model uses the logit link function, the distribution of the deviance residuals is closer to the distribution of residuals from a least squares regression model. The deviance residuals and the Pearson residuals become more similar as the number of trials for each combination of predictor settings increases.
Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed. Ideally, the points should fall randomly on both sides of 0, with no recognizable patterns in the points.
Pattern  What the pattern may indicate 

Fanning or uneven spreading of residuals across fitted values  An inappropriate link function 
Curvilinear  A missing higherorder term or an inappropriate link function 
A point that is far away from zero  An outlier 
A point that is far away from the other points in the xdirection  An influential point 
Issue  Possible solution 

Nonconstant variance  Consider using different terms in the model, a different link function, or weights. 
An outlier or influential point 

The residuals versus order plot displays the residuals in the order that the data were collected.
The interpretation of the plot is the same whether you use deviance residuals or Pearson residuals. When the model uses the logit link function, the distribution of the deviance residuals is closer to the distribution of residuals from a least squares regression model. The deviance residuals and the Pearson residuals become more similar as the number of trials for each combination of predictor settings increases.
The residuals versus variables plot displays the residuals versus another variable. The variable could already be included in your model. Or, the variable may not be in the model, but you suspect it affects the response.
The interpretation of the plot is the same whether you use deviance residuals or Pearson residuals. When the model uses the logit link function, the distribution of the deviance residuals is closer to the distribution of residuals from a least squares regression model. The deviance residuals and the Pearson residuals become more similar as the number of trials for each combination of predictor settings increases.
If the variable is already included in the model, use the plot to determine whether you should add a higherorder term of the variable. If the variable is not already included in the model, use the plot to determine whether the variable is affecting the response in a systematic way.
Pattern  What the pattern may indicate 

Pattern in residuals  The variable affects the response in a systematic way. If the variable is not in your model, include a term for that variable and refit the model. 
Curvature in the points  A higherorder term of the variable should be included in the model. For example, a curved pattern indicates that you should add a squared term. 