Let data points as "x_i" and number of data points as n.
After increase every data"(x_i)" point by 10%,new data points"(y_i)" are,
"y_i=x_i+x_i*0.1=1.1*x_i"
mean of data set before increment"(\\bar{x})" ,
"\\bar{x}=\\frac{\\sum\\limits_{i=1}^{n}{x_i}}{n}\\\\"
mean of the data set after increment"(\\bar{y})" ,
"\\bar{y}=\\frac{\\sum\\limits_{i=1}^{n}{y_i}}{n}\\\\\n\\bar{y}=\\frac{\\sum\\limits_{i=1}^{n}{1.1*x_i}}{n}\\\\\n\\bar{y}=1.1\\frac{\\sum\\limits_{i=1}^{n}{x_i}}{n}\\\\\n\\bar{y}=1.1\\bar{x}"
Therefore mean is also increment by 10% when data set increment by 10%.
mean deviation of the data set before increment"(M_x),"
"M_x=\\frac{\\sum \\limits_{i=1}^{n}{\\text{\\textbar} x_i-\\bar{x}\\text{\\textbar}}}{n}"
mean deviation of the data set before increment"(M_y),"
"M_y=\\frac{\\sum \\limits_{i=1}^{n}{\\text{\\textbar} y_i-\\bar{y}\\text{\\textbar}}}{n}\\\\\nM_y=\\frac{\\sum \\limits_{i=1}^{n}{\\text{\\textbar}1.1* x_i-1.1*\\bar{x}\\text{\\textbar}}}{n}\\\\\nM_y=\\frac{1.1*\\sum \\limits_{i=1}^{n}{\\text{\\textbar} x_i-\\bar{x}\\text{\\textbar}}}{n}\\\\\nM_y=1.1*M_x"
Therefore mean deviation is also increment by 10% when data set increment by 10%.
variance of the data set before increment"(\\sigma^2_x)"
"\\sigma^2_x=\\frac{\\sum\\limits_{i=1}^{n}{(x_i-\\bar{x})^2}}{n}=\\frac{\\sum\\limits_{i=1}^{n}{x_i^2}}{n}-(\\bar{x})^2\\\\"
variance of the data set after increment"(\\sigma^2_y)"
"\\sigma^2_y=\\frac{\\sum\\limits_{i=1}^{n}{y_i^2}}{n}-(\\bar{y})^2\\\\\n\\sigma^2_y=\\frac{\\sum\\limits_{i=1}^{n}{(1.1*x_i)^2}}{n}-(1.1*\\bar{x})^2\\\\\n\\sigma^2_y=(1.1)^2*\\frac{\\sum\\limits_{i=1}^{n}{(x_i)^2}}{n}-(1.1)^2*(\\bar{x})^2\\\\\n\\sigma^2_y=(1.1)^2*\\sigma^2_x\\\\\n\\sigma^2_y=1.21*\\sigma^2_x"
standard deviation,
"\\sigma_y=1.1*\\sigma_x"
Therefore standard deviation is also increment by 10% when data set increment by 10%.
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