Let X1, X2, ..., Xm be a random sample of size m from a Binomial distribution with p = 0.25 and n unknown. That is, f(æ|n) C)(0.25) (1 – 0.25)" ", a= 1,2, 3,...,n Careful! In this case, we are using m as the sample size. For this problem, n is just a parameter of the binomial distribution. Thus, for this problem, we would define the sample mean as m m i=1 Obtain a method of moments estimator for n, ñ. Then estimate n with X1 = 4, x2 = 6, 3 = 4, X4 = 6, 25 = 2. number (rtol=0.01, atol=1e-08) Notes: Remember that we're estimating n (a whole number), hence, make sure you round your answer (no decimal digits)!

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Let X1, X2,..., Xm be a random sample of size m from a Binomial distribution with p = 0.25 and n
unknown. That is,
f(2x\n) = (".
(0.25)" (1– 0.25)" -", x = 1,2, 3, ..., n
n-x
Careful! In this case, we are using m as the sample size. For this problem, n is just a parameter of the binomial
distribution. Thus, for this problem, we would define the sample mean as
m
i=1
Obtain a method of moments estimator for n, ñ.
.
Then estimate n with
¤1 = 4, 2 = 6, x3 = 4, x4 = 6,
X5 = 2.
ñ =
number (rtol=0.01, atol=1e-08)
Notes: Remember that we're estimating n (a whole number), hence, make sure you round your answer
(no decimal digits)!
Transcribed Image Text:Let X1, X2,..., Xm be a random sample of size m from a Binomial distribution with p = 0.25 and n unknown. That is, f(2x\n) = (". (0.25)" (1– 0.25)" -", x = 1,2, 3, ..., n n-x Careful! In this case, we are using m as the sample size. For this problem, n is just a parameter of the binomial distribution. Thus, for this problem, we would define the sample mean as m i=1 Obtain a method of moments estimator for n, ñ. . Then estimate n with ¤1 = 4, 2 = 6, x3 = 4, x4 = 6, X5 = 2. ñ = number (rtol=0.01, atol=1e-08) Notes: Remember that we're estimating n (a whole number), hence, make sure you round your answer (no decimal digits)!
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