Visually inspecting normality of variables I'm using R to plot some graphs for visually inspecting the normality of variables that will go into a linear regression model. Except for a histogram and a QQ plot, what other graphics could I use?
 A: As suggested by @Peter Flom a third option could be the boxplot.
The picture below shows two distributions (with n=1000):


*

*On the left column a Normal distribution with $\mu = 4$ and $\sigma = 1$ and,

*On the right a two parameter Weibull distribution, with shape = 1.5 and scale = 2.


The first line shows the histograms and the second line illustrates a quantile-quantile plot (with the normal distribution as the baseline for comparison). The third line contains the boxplot plots). Note how the Weibull presents non symmetric whiskers in the boxplot.

Here is the R code to reproduce the picture
set.seed(77)  
x=rnorm(1000,4,1)
y=rweibull(1000,shape=1.5,scale=2)

par(mfrow=c(3,2),mar=c(5,4,1.5,2))

hist   (x,prob=T, main="Normal ~ (4,1)  "                  , ylab="Density"          , xlab="Quantile"          , ylim=c(0,0.6), xlim=c(0,8))
hist   (y,prob=T, main="Weibull ~ (1.5,2)"                 , ylab="Density"          , xlab="Quantile"          , ylim=c(0,0.6), xlim=c(0,8))
qqplot (x,x     , main="Normal ~ (4,1) x Normal ~ (4,1)"   , ylab="Normal quantiles" , xlab="Normal quantiles"                              )
qqplot (x,y     , main="Normal ~ (4,1) x Weibull ~ (1.5,2)", ylab="Weibull quantiles", xlab="Normal ~ quantiles"                            )
boxplot(x       , main="Normal ~ (4,1)  "                  , ylab="Quantile"         , xlab=""                  , ylim=c(0,8               ))
boxplot(y       , main="Weibull ~ (1.5,2)"                 , ylab="Quantile"         , xlab=""                  , ylim=c(0,8               ))  

Lastly, just to emphasize the hint provided by Peter, regression assumption of normality is observed over the residuals' distribution and not the predictors'. 
