Supplementary Exercise 11.15 of IPS7e:
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Minitab commands and output:

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 WOpen "R:\Chapter 11\ex11_015.mtw".

 Correlation 'GPA'-'Age' 'SC'-'C6'.

Correlations: GPA, IQ, Age, SC, C1, C2, C3, C4, C5, C6

            GPA       IQ      Age       SC       C1       C2       C3       C4      C5
IQ        0.634
          0.000

Age      -0.389   -0.382
          0.000    0.001

SC        0.542    0.493   -0.178
          0.000    0.000    0.119

C1        0.441    0.222   -0.212    0.697
          0.000    0.051    0.062    0.000

C2        0.601    0.547   -0.248    0.846    0.624
          0.000    0.000    0.028    0.000    0.000

C3        0.495    0.441   -0.111    0.800    0.368    0.692
          0.000    0.000    0.333    0.000    0.001    0.000

C4        0.267    0.234    0.006    0.781    0.382    0.592    0.609
          0.018    0.039    0.957    0.000    0.001    0.000    0.000

C5        0.472    0.347   -0.123    0.778    0.404    0.614    0.736    0.689
          0.000    0.002    0.283    0.000    0.000    0.000    0.000    0.000

C6        0.401    0.360   -0.041    0.839    0.516    0.623    0.732    0.727    0.716
          0.000    0.001    0.721    0.000    0.000    0.000    0.000    0.000    0.000

Cell Contents: Pearson correlation
               P-Value

Comments and answers to questions:
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The correlations with GPA are given in the first column of the matrix above. 
Strictly speaking it is not meaningful to compute a correlation involving 
the categorical variable Sex, although the resulting correlation can be
interpreted in terms of the R2 of a two-sample model. Among the rest, IQ 
has the strongest correlation with GPA and would therefore have the strongest 
association with GPA in a simple linear regression (recall that the tests 
for zero correlation and zero slope are identical!). The R^2 for the simple 
linear regression with IQ would be: R^2 = 0.634^2 = 0.402 = 40.2%.