Step 1: Recall Spearman’s rank correlation formula.
For \(n\) observations, Spearman’s rank correlation coefficient is
\[
\rho
=
1-\frac{6\sum d_i^2}{n(n^2-1)}
\]
where \(d_i\) denotes the difference in ranks for the \(i\)-th contestant.
Here,
\[
n=10
\]
and the initially computed value is
\[
\rho=0.6
\]
Step 2: Find the originally used value of \(\sum d_i^2\).
Using the formula,
\[
0.6
=
1-\frac{6\sum d_i^2}{10(10^2-1)}
\]
\[
0.6
=
1-\frac{6\sum d_i^2}{10(99)}
\]
\[
0.4
=
\frac{6\sum d_i^2}{990}
\]
\[
6\sum d_i^2
=
396
\]
\[
\sum d_i^2
=
66
\]
Step 3: Correct the value of \(\sum d_i^2\).
One difference was incorrectly taken as
\[
2
\]
instead of the correct value
\[
5
\]
So, the correction in \(\sum d_i^2\) is
\[
5^2-2^2
=
25-4
=
21
\]
Therefore, the corrected value is
\[
\sum d_i^2=66+21=87
\]
Step 4: Compute the corrected rank correlation coefficient.
\[
\rho
=
1-\frac{6(87)}{10(99)}
\]
\[
=
1-\frac{522}{990}
\]
\[
=
1-0.527272\ldots
\]
\[
=
0.472727\ldots
\]
Rounded to two decimal places,
\[
\rho\approx 0.47
\]
Step 5: Final conclusion.
Hence, the corrected value of Spearman’s rank correlation coefficient is
\[
\boxed{0.47}
\]