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.
If the batch factor is random, use the conditional residuals to evaluate normality.
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 nonconstant variance or a time order effect. If the residuals do not follow a normal distribution and the data have fewer than 15 observations, then confidence intervals for predictions, confidence intervals for coefficients, and pvalues for coefficients can be inaccurate.
If you identify any patterns or outliers in your residual versus fits plot, consider the following solutions:
Issue  Possible solution 

Nonconstant variance  Consider using a BoxCox transformation. For more information, go to Perform BoxCox transformation for Stability Study. 
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.