{"id":3572,"date":"2021-07-29T11:04:59","date_gmt":"2021-07-29T11:04:59","guid":{"rendered":"https:\/\/mathemerize.com\/?p=3572"},"modified":"2021-11-29T18:43:29","modified_gmt":"2021-11-29T13:13:29","slug":"formula-for-variance-and-standard-deviation","status":"publish","type":"post","link":"https:\/\/mathemerize.com\/formula-for-variance-and-standard-deviation\/","title":{"rendered":"Formula for Variance and Standard Deviation"},"content":{"rendered":"
Here, you will learn learn formula for variance and standard deviation and relationship between variance and standard deviation.<\/p>\n
Let’s begin –<\/p>\n
The variance of a distribution is, the mean of squares of deviation of variate from their mean. It is denoted by \\(\\sigma^2\\) or var(x).<\/p>\n
The positive square root of the variance are called the standard deviation. It is denoted by \\(\\sigma\\) or S.D.<\/p>\n
\nHence standard deviation = + \\(\\sqrt{variance}\\)<\/p>\n<\/blockquote>\n
Formula for Variance :<\/h2>\n
(i) <\/strong>for ungrouped distribution :<\/strong><\/p>\n
\n\\({\\sigma^2}_x\\) = \\(\\sum(x_i – \\bar{x})^2\\over n\\)<\/p>\n
\\({\\sigma^2}_x\\) = \\(\\sum{x_i}^2\\over n\\) – \\(\\bar{x}^2\\)<\/p>\n
= \\(\\sum{x_i}^2\\over n\\) – \\(({\\sum{x_i}\\over n})^2\\)<\/p>\n
\\({\\sigma^2}_d\\) = \\(\\sum{d_i}^2\\over n\\) – \\(({\\sum{d_i}\\over n})^2\\), where \\(d_i\\) = \\(x_i\\) – a<\/p>\n<\/blockquote>\n
(ii) for frequency distribution :<\/strong><\/p>\n
\n\\({\\sigma^2}_x\\) = \\(\\sum f_i(x_i – \\bar{x})^2\\over N\\)<\/p>\n
\\({\\sigma^2}_x\\) = \\(\\sum f_i{x_i}^2\\over N\\) – \\(\\bar{x}^2\\)<\/p>\n
= \\(\\sum f_i{x_i}^2\\over N\\) – \\(({\\sum f_i{x_i}\\over N})^2\\)<\/p>\n
\\({\\sigma^2}_d\\) = \\(\\sum f_i{d_i}^2\\over n\\) – \\(({\\sum f_i{d_i}\\over n})^2\\), where \\(d_i\\) = \\(x_i\\) – a<\/p>\n
\\({\\sigma^2}_d\\) = \\(h^2\\)[\\(\\sum f_i{u_i}^2\\over n\\) – \\(({\\sum f_i{u_i}\\over n})^2\\)], where \\(u_i\\) = \\(x_i\\over h\\)<\/p>\n<\/blockquote>\n
(iii) Coefficient of Standard Deviation = \\(\\sigma\\over \\bar{x}\\)<\/strong><\/p>\n
\nCoefficient of variation = \\(\\sigma\\over \\bar{x}\\) \\(\\times\\) 100 (in percentage)<\/p>\n<\/blockquote>\n\n\n
Example : <\/span>Find the variance and standard deviation of first n natural numbers.<\/p>\n
Solution : <\/span> We know that,
\n\\({\\sigma^2}_x\\) = \\(\\sum{x_i}^2\\over n\\) – \\(({\\sum{x_i}\\over n})^2\\)
\n = \\(\\sum{n}^2\\over n\\) – \\(({\\sum{n}\\over n})^2\\) = \\(n(n + 1)(2n + 1)\\over {6n}\\) – \\([{n(n + 1)\\over {2n}}]^2\\) = \\(n^2 – 1\\over 12\\)
\nStandard Deviation = \\(\\sqrt{variance}\\) = \\(\\sqrt{n^2 – 1\\over 12}\\)
<\/br
<\/p>\n\n\n\nExample : <\/span>Find the Coefficient of variation in percentage of first n natural numbers.<\/p>\n
Solution : <\/span> We know that
\nMean \\(\\bar{x}\\) = \\(n + 1\\over 2\\),\nVariance = \\({\\sigma^2}_x\\) = \\(n^2 – 1\\over 12\\)
\nStandard Deviation \\(\\sigma\\) = \\(\\sqrt{variance}\\) = \\(\\sqrt{n^2 – 1\\over 12}\\)
\nCoefficient of variation = \\(\\sigma\\over \\bar{x}\\) \\(\\times\\) 100
\n= \\(\\sqrt{n^2 – 1\\over 12}\\) \\(\\times\\) (\\(2\\over n + 1\\)) \\(\\times\\) 100
\n= \\(\\sqrt{(n – 1)\\over 3(n + 1)}\\) \\(\\times\\) 100
\n<\/br
<\/p>\n\n\nHope you learnt what is the formula for variance and standard deviation, learn more concepts of statistics and practice more questions to get ahead in the competition. Good luck!<\/p>\n\n\n