1. Section 7.7: Checking for Normality and Normalizing Transformations

2. Normal Probability Plot – is a scatterplot of the (normal score, observed value) pairs.
A substantial linear pattern in a normal probability plot suggests that population normality is plausible. On the other hand, a systematic departure from a straight-line pattern (such as curvature in the plot) casts doubt on the reasonableness of assuming a normal population distribution.

3. Example
The following 10 observations are widths of contact windows in integrated circuit chips:
10 pairs are:
(-1.539, 2.49), (-1.001, 2.85), (-0.656, 2.94), (-0.376, 3.21), (-0.123, 3.30), (0.123, 3.34), (0.376, 3.62), (0.656, 3.81), (1.001, 4.02), (1.539, 4.38)

4. The normal probability plot

5. Values to which r can be compared to check for normality*
If r < critical r for corresponding n considerable doubt is cast on the assumption of population normality.
Values to which r can be compared to check for normality* N 5 10 15 20 25 30 40 50 60 75
Critical R
.832
.880
.911
.929
.941
.949
.960
.966
.971
.976