Question #330241

The following data are the measures of the diameters of 36 rivet heads in 1/100 of an inch.

6.72 6.77 6.82 6.70 6.78 6.70 6.62 6.75

6.66 6.66 6.64 6.76 6.73 6.80 6.72 6.76

6.76 6.68 6.66 6.62 6.72 6.76 6.70 6.78

6.76 6.67 6.70 6.72 6.74 6.81 6.79 6.78

6.66 6.76 6.76 6.72

Compute the Arithmetic Mean, variance, standard deviation, Coecient of Variation, Coe-

cient of Skewness and Coecient of Kurtosis..



Expert's answer

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=1nk=1nxk==136(6.622+6.64+6.664+6.67++6.68+6.74+6.725+6.73+6.74++6.75+6.767+6.77+6.783+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


Variance:

σ2=1n1k=1n(xkμ)2==135(0.1122+0.092+0.0724+0.062++0.052+0.0324+0.0125+0.012+0.022++0.0327+0.042+0.0523+0.062++0.072+0.082+0.092)0.0028857140.00289\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


Standard deviation:

σ=σ20.0537\sigma=\sqrt{\sigma^2}\approx0.0537


Coefficient of variation:

CV=σμ0.008CV=\frac{\sigma}{\mu}\approx0.008


Coefficient of skewness:

b=1nk=1n(xkμ)3/σ3=1360.05373((0.11)32+(0.09)3++(0.07)34+(0.06)3++(0.05)3+(0.03)34+(0.01)35++0.013+0.023++0.0337+0.043+0.0533+0.063++0.073+0.083+0.093)18b=\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


Coefficient of Kurtosis:

K=n(n+1)(n1)(n2)(n3)1σ4k=1n(xkμ)4==363735343310.05374(0.1142+0.094++0.0744+0.064++0.054+0.0344+0.0145+0.014+0.024++0.0347+0.044+0.0543+0.064++0.072+0.082+0.092)2.64K=\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


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