Consider the multiple linear regression model
\[
Y_i = \beta_0 + \beta_1 x_{1,i} + \beta_2 x_{2,i} + \dots + \beta_{22} x_{22,i} + \epsilon_i, \quad i = 1, 2, \dots, 123,
\]
where, for \( j = 0, 1, 2, \dots, 22 \), \( \beta_j \)'s are unknown parameters and \( \epsilon_i \)'s are independent and identically distributed \( N(0, \sigma^2) \), \( \sigma>0 \), random variables.
If the sum of squares due to regression is 338.92, the total sum of squares is 522.30 and \( R^2_{\text{adj}} \) denotes the value of adjusted \( R^2 \), then
\[
100 R^2_{\text{adj}} = \underline{\hspace{2cm}}
\]
(round off to 2 decimal places).