The histogram of the residuals shows the distribution of the residuals for all observations.
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.
A histogram is most effective when you have approximately 20 or more data points. If the sample is too small, then each bar on the histogram does not contain enough data points to reliably show skewness or outliers.
The normal probability plot of the residuals displays the residuals versus their expected values when the distribution is normal.
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 confidence intervals and pvalues can be inaccurate.
The residuals versus fits graph plots the residuals on the yaxis and the fitted values on the xaxis.
Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed and have constant variance. 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  Nonconstant variance 
Curvilinear  A missing higherorder term 
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 
If you identify any patterns or outliers in your residual versus fits plot, consider the following solutions:
Issue  Possible solution 

Nonconstant variance  Consider using Fit Regression Model with a BoxCox transformation or weights. 
An outlier or influential point 

The residuals versus order plot displays the residuals in the order that the data were collected.
The residual 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.
If you see a nonrandom pattern in the residuals, it indicates that the variable affects the response in a systematic way. Consider including this variable in an analysis.
Because the training and test data sets are typically from the same population, you expect to see the same patterns in the residual plots for each data set. Different patterns in the residual plots could indicate a systematic difference between the observations in the training data set and the test data set.
Although the patterns are typically the same, the residual plots for the test data set can be slightly different from the plots for the training data set. For example, because the test data set is not in the model fitting process, the mean of the residuals can be nonzero.