A function of random variables utilized to calculate a parameter of distribution exists as an unbiased estimator.
<h3>What are the parameters of a random variable?</h3>
A function of random variables utilized to calculate a parameter of distribution exists as an unbiased estimator.
An unbiased estimator exists in which the difference between the estimator and the population parameter grows smaller as the sample size grows larger. This simply indicates that an unbiased estimator catches the true population value of the parameter on average, this exists because the mean of its sampling distribution exists the truth.
Also, we comprehend that the bias of an estimator (b) that estimates a parameter (p) exists given by; E(b) - p
Therefore, an unbiased estimator exists as an estimator that contains an expected value that exists equivalent to the parameter i.e the value of its bias exists equivalent to zero.
Generally, in statistical analysis, the sample mean exists as an unbiased estimator of the population mean while the sample variance exists as an unbiased estimator of the population variance.
Therefore, the correct answer is an unbiased estimator.
To learn more about unbiased estimators refer to:
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Answer:
0.125
Step-by-step explanation:
1=40000
x-5000
x=5000÷40000=1/8=0.125
Step-by-step explanation:
1 - ((9*t) - (9*-2) = ( 4*t) - (4*-15)
1 - 9t + 18 = 4t + 60
- 9t - 4t = 60 - 1 - 18
-13t = 40
t = 40/-13
2x+6 <u>></u> -4
trate it as a temporary equals sign
2x+6=-4
subtract 6 rom both sdies
2x=-10
divide both sides by 2
x=-5
put it back
x <u>></u> -5
so the answer is C
