The mean is given by \( \bar{x} = \frac{\sum x_i}{n} = \frac{1 + 3 + a + 7 + b}{5} = 5 \). This simplifies to \( 11 + a + b = 25 \), and subsequently \( a + b = 14 \). The variance is given by \( \sigma^2 = \frac{\sum x_i^2}{n} - \left( \bar{x} \right)^2 = 10 \). Substituting values, we get \( \frac{1^2 + 3^2 + a^2 + 7^2 + b^2}{5} - (5)^2 = 10 \), which further simplifies to \( \frac{1 + 9 + a^2 + 49 + b^2}{5} - 25 = 10 \). This leads to \( 59 + a^2 + b^2 = 175 \), and \( a^2 + b^2 = 116 \). We have the system of equations: \( a + b = 14 \) and \( a^2 + b^2 = 116 \). Using the identity \( (a + b)^2 = a^2 + b^2 + 2ab \), we substitute the known values: \( 14^2 = 116 + 2ab \). This gives \( 196 = 116 + 2ab \), so \( 2ab = 80 \), and \( ab = 40 \). Solving the system \( a + b = 14 \) and \( ab = 40 \) by forming the quadratic \( t^2 - (a + b)t + ab = 0 \), we get \( t^2 - 14t + 40 = 0 \). Factoring yields \( (t - 10)(t - 4) = 0 \). Thus, \( t = 10 \) or \( t = 4 \). Given \( a>b \), we determine that \( a = 10 \) and \( b = 4 \). The original observations \( x_i \) are \( 1, 3, 10, 7, 4 \). The new observations \( n + x_n \) for \( n = 1 \) to \( 5 \) are: \( 1 + x_1 = 1 + 1 = 2 \), \( 2 + x_2 = 2 + 3 = 5 \), \( 3 + x_3 = 3 + 10 = 13 \), \( 4 + x_4 = 4 + 7 = 11 \), \( 5 + x_5 = 5 + 4 = 9 \). The new set of observations is \( 2, 5, 13, 11, 9 \). The mean of this new set is \( \frac{2 + 5 + 13 + 11 + 9}{5} = \frac{40}{5} = 8 \). The variance of the new set is \( \frac{2^2 + 5^2 + 13^2 + 11^2 + 9^2}{5} - (8)^2 \). This calculates to \( \frac{4 + 25 + 169 + 121 + 81}{5} - 64 = \frac{400}{5} - 64 = 80 - 64 = 16 \).