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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Your median is 5 because that is the number that is mentioned the most.
A median is a number that i said the most.
And in the pic your median is 7.
Hope this helps you :0
hope I'm right but either 2 or 4 i can kinda go either way
Answer:
It would be the first one.
Step-by-step explanation:
When it says "at least" that means Kristen has to walk 20 miles at minimum, but she can walk more if she chooses to. Therefore in context of the problem, the greater than or equal to sign would work best.
Standard deviation is simply the square root of the variance.
We need to get the square root of each variance to see whether its standard deviation is greater than its variance.
A. 0.25 ⇒ √0.25 = 0.50B. 1.75 ⇒ √1.75 = 1.32C. 2.5 ⇒ √2.5 = 1.58D. 1.5 ⇒ √1.5 = 1.22
Among the choices, only A. 0.25 has a standard deviation that is greater than the variance. The standard deviation is 0.50
