First let's sort the data, write appearing frequencies of each value and corresponding probabilities of appearing (the number of different values will be 17):
6.62 - 2, p = 0.117647
6.64 - 1, p = 0.0588235
6.66 - 4, p = 0.235294
6.67 - 1, p = 0.0588235
6.68 - 1, p = 0.0588235
6.7 - 4, p = 0.235294
6.72 - 5, p = 0.294118
6.73 - 1, p = 0.0588235
6.74 - 1, p = 0.0588235
6.75 - 1, p = 0.0588235
6.76 - 7, p = 0.411765
6.77 - 1, p = 0.0588235
6.78 - 3, p = 0.176471
6.79 - 1, p = 0.0588235
6.8 - 1, p = 0.0588235
6.81 - 1, p = 0.0588235
6.82 - 1, p = 0.0588235
Let n = 36
Mean:
μ = 1 n ∑ k = 1 n x k = = 1 36 ( 6.62 ⋅ 2 + 6.64 + 6.66 ⋅ 4 + 6.67 + + 6.68 + 6.7 ⋅ 4 + 6.72 ⋅ 5 + 6.73 + 6.74 + + 6.75 + 6.76 ⋅ 7 + 6.77 + 6.78 ⋅ 3 + 6.79 + 6.8 + 6.81 + 6.82 ) ≈ 6.73 \mu=\frac{1}{n}\sum_{k=1}^nx_k=\\=\frac{1}{36}(6.62\cdot2+6.64+6.66\cdot4+6.67+\\
+6.68+6.7\cdot4+6.72\cdot5+6.73+6.74+\\
+6.75+6.76\cdot7+6.77+6.78\cdot3+6.79+\\
6.8+6.81+6.82)\approx6.73 μ = n 1 ∑ k = 1 n x k = = 36 1 ( 6.62 ⋅ 2 + 6.64 + 6.66 ⋅ 4 + 6.67 + + 6.68 + 6.7 ⋅ 4 + 6.72 ⋅ 5 + 6.73 + 6.74 + + 6.75 + 6.76 ⋅ 7 + 6.77 + 6.78 ⋅ 3 + 6.79 + 6.8 + 6.81 + 6.82 ) ≈ 6.73
Variance:
σ 2 = 1 n − 1 ∑ k = 1 n ( x k − μ ) 2 = = 1 35 ( 0.1 1 2 ⋅ 2 + 0.0 9 2 + 0.0 7 2 ⋅ 4 + 0.0 6 2 + + 0.0 5 2 + 0.0 3 2 ⋅ 4 + 0.0 1 2 ⋅ 5 + 0.0 1 2 + 0.0 2 2 + + 0.0 3 2 ⋅ 7 + 0.0 4 2 + 0.0 5 2 ⋅ 3 + 0.0 6 2 + + 0.0 7 2 + 0.0 8 2 + 0.0 9 2 ) ≈ 0.002885714 ≈ 0.00289 \sigma^2=\frac{1}{n-1}\sum_{k=1}^n(x_k-\mu)^2=\\
=\frac{1}{35}(0.11^2\cdot2+0.09^2+0.07^2\cdot4+0.06^2+\\
+0.05^2+0.03^2\cdot4+0.01^2\cdot5+0.01^2+0.02^2+\\
+0.03^2\cdot7+0.04^2+0.05^2\cdot3+0.06^2+\\
+0.07^2+0.08^2+0.09^2)\approx0.002885714\approx0.00289 σ 2 = n − 1 1 ∑ k = 1 n ( x k − μ ) 2 = = 35 1 ( 0.1 1 2 ⋅ 2 + 0.0 9 2 + 0.0 7 2 ⋅ 4 + 0.0 6 2 + + 0.0 5 2 + 0.0 3 2 ⋅ 4 + 0.0 1 2 ⋅ 5 + 0.0 1 2 + 0.0 2 2 + + 0.0 3 2 ⋅ 7 + 0.0 4 2 + 0.0 5 2 ⋅ 3 + 0.0 6 2 + + 0.0 7 2 + 0.0 8 2 + 0.0 9 2 ) ≈ 0.002885714 ≈ 0.00289
Standard deviation:
σ = σ 2 ≈ 0.0537 \sigma=\sqrt{\sigma^2}\approx0.0537 σ = σ 2 ≈ 0.0537
Coefficient of variation:
C V = σ μ ≈ 0.008 CV=\frac{\sigma}{\mu}\approx0.008 C V = μ σ ≈ 0.008
Coefficient of skewness:
b = 1 n ∑ k = 1 n ( x k − μ ) 3 / σ 3 = 1 36 ⋅ 0.053 7 3 ( ( − 0.11 ) 3 ⋅ 2 + ( − 0.09 ) 3 + + ( − 0.07 ) 3 ⋅ 4 + ( − 0.06 ) 3 + + ( − 0.05 ) 3 + ( − 0.03 ) 3 ⋅ 4 + ( − 0.01 ) 3 ⋅ 5 + + 0.0 1 3 + 0.0 2 3 + + 0.0 3 3 ⋅ 7 + 0.0 4 3 + 0.0 5 3 ⋅ 3 + 0.0 6 3 + + 0.0 7 3 + 0.0 8 3 + 0.0 9 3 ) ≈ − 18 b=\frac{1}{n}\sum_{k=1}^n(x_k-\mu)^3/\sigma^3=\\
\frac{1}{36\cdot0.0537^3}((-0.11)^3\cdot2+(-0.09)^3+\\
+(-0.07)^3\cdot4+(-0.06)^3+\\
+(-0.05)^3+(-0.03)^3\cdot4+(-0.01)^3\cdot5+\\
+0.01^3+0.02^3+\\
+0.03^3\cdot7+0.04^3+0.05^3\cdot3+0.06^3+\\
+0.07^3+0.08^3+0.09^3)\approx−18 b = n 1 ∑ k = 1 n ( x k − μ ) 3 / σ 3 = 36 ⋅ 0.053 7 3 1 (( − 0.11 ) 3 ⋅ 2 + ( − 0.09 ) 3 + + ( − 0.07 ) 3 ⋅ 4 + ( − 0.06 ) 3 + + ( − 0.05 ) 3 + ( − 0.03 ) 3 ⋅ 4 + ( − 0.01 ) 3 ⋅ 5 + + 0.0 1 3 + 0.0 2 3 + + 0.0 3 3 ⋅ 7 + 0.0 4 3 + 0.0 5 3 ⋅ 3 + 0.0 6 3 + + 0.0 7 3 + 0.0 8 3 + 0.0 9 3 ) ≈ − 18
Coefficient of Kurtosis:
K = n ( n + 1 ) ( n − 1 ) ( n − 2 ) ( n − 3 ) 1 σ 4 ∑ k = 1 n ( x k − μ ) 4 = = 36 ⋅ 37 35 ⋅ 34 ⋅ 33 1 0.053 7 4 ( 0.1 1 4 ⋅ 2 + 0.0 9 4 + + 0.0 7 4 ⋅ 4 + 0.0 6 4 + + 0.0 5 4 + 0.0 3 4 ⋅ 4 + 0.0 1 4 ⋅ 5 + 0.0 1 4 + 0.0 2 4 + + 0.0 3 4 ⋅ 7 + 0.0 4 4 + 0.0 5 4 ⋅ 3 + 0.0 6 4 + + 0.0 7 2 + 0.0 8 2 + 0.0 9 2 ) ≈ 2.64 K=\frac{n(n+1)}{(n-1)(n-2)(n-3)}\frac{1}{\sigma^4}\sum_{k=1}^n(x_k-\mu)^4=\\
=\frac{36\cdot37}{35\cdot34\cdot33}\frac{1}{0.0537^4}(0.11^4\cdot2+0.09^4+\\
+0.07^4\cdot4+0.06^4+\\
+0.05^4+0.03^4\cdot4+0.01^4\cdot5+0.01^4+0.02^4+\\
+0.03^4\cdot7+0.04^4+0.05^4\cdot3+0.06^4+\\
+0.07^2+0.08^2+0.09^2)\approx2.64 K = ( n − 1 ) ( n − 2 ) ( n − 3 ) n ( n + 1 ) σ 4 1 ∑ k = 1 n ( x k − μ ) 4 = = 35 ⋅ 34 ⋅ 33 36 ⋅ 37 0.053 7 4 1 ( 0.1 1 4 ⋅ 2 + 0.0 9 4 + + 0.0 7 4 ⋅ 4 + 0.0 6 4 + + 0.0 5 4 + 0.0 3 4 ⋅ 4 + 0.0 1 4 ⋅ 5 + 0.0 1 4 + 0.0 2 4 + + 0.0 3 4 ⋅ 7 + 0.0 4 4 + 0.0 5 4 ⋅ 3 + 0.0 6 4 + + 0.0 7 2 + 0.0 8 2 + 0.0 9 2 ) ≈ 2.64
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