Let "X_1, X_2,\u2026.., X_n" be independently and identically distributed b(1, p) random variables. Obtain confidence interval for p using Chebychev’s inequality.
The "chebyshev's" inequality is given as,
"p(|x-\\mu|\\lt k\\sigma )\\geqslant 1-(1\/k^2)"
where ,
"x" is the point estimate of the parameter whose confidence interval needs to be determined.
"\\sigma" is the standard deviation of that parameter.
This question requires us to determine the confidence interval for the parameter "p" of the binomial distribution.
Given the random variables "X_1,X_2,X_3,...,X_n" , we need to determine the point estimate for "p".
To do so, let us define the random variable "Y=\\sum ^n_{i=1}X_i=X_1+X_2+....+X_n" ,
Let "\\hat{p}" be the point estimate for "p" then "\\hat{p}=\\sum^n_{i=1}X_i\/n=Y\/n" .
The standard deviation"(sd(p))" for "p" is given as,
"sd(p)=\\sqrt{\\hat{p}(1-\\hat{p})\/n}" .
Now in the "chebyshev's" inequality stated above we need to find the value for "k" using the 95% confidence.
The right hand side of the inequality is equated with 95% in order to find "k" as below,
"1-(1\/k^2)=0.95\\implies0.05=1\/k^2\\implies k^2=20\\implies k=4.5(1dp)"
A 95% confidence interval for "p" is given as,
"|\\hat{p}-p|\\lt k*sd(p)"
This can be re-written as,
"\\hat{p}-k*sd(p)\\lt p\\lt\\hat{p}+k*sd(p)............(i)"
Replacing for "k" and "sd(p)" in "equation (i)" we have,
"\\hat{p}-4.5*\\sqrt{\\hat{p}(1-\\hat{p})\/n}\\lt p\\lt \\hat{p}+4.5*\\sqrt{\\hat{p}(1-\\hat{p})\/n}" .
Therefore the 95% confidence interval for the parameter "p" is given as,
"C.I=(\\hat{p}-4.5*\\sqrt{\\hat{p}(1-\\hat{p})\/n },\\space \\hat{p}+4.5*\\sqrt{\\hat{p}(1-\\hat{p})\/n })"
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