Answer:
Since the name indicates Minimum Variance Unbiased Estimator-first of all it is a parameter estimator. Secondly, it is an unbiased estimator so that the sample is carried out randomly. I.e. whenever a sample is chosen, there is no personal bias.
Then we can consider more than one sample-based unbiased estimator but sometimes they can vary in variation. But we have always intended to select an estimator that has minimal variance.
Therefore if the unbiased estimator has minimal variation between all unbiased class estimators then it is known as a good estimator.
The advantage of MVUE is that it is impartial and has a minimal variance of all unbiased estimators amongst the groups.
At times we get an estimator such as MLE which is not unbiased because the sample can be personally biased. Now let us assume an instructor needs to find the lowest marks in a physics class. Presume an instructor picks a sample and interprets the lowest possible marks.
Again the mistake could be that the instructor may choose his favorite sample learners because the sample might not be selected randomly. Therefore it is important to select an unbiased estimate with a minimum variance.
x-4=x(2)
x-4=2x
+4 +4
8 = 6
lilly has 8 stamps and mackenzie has 6
Calle: has twice as many as Juniper so = 2*25 = 50
she also has 5 times as many as sara so if she has 50, you divide 50/5 = 10
Calle has 50 stickers, Sara has 10 and Juniper has 25.
So all together they have 85 stickers.
Answer:
A. Less than zero
Step-by-step explanation:
So If you graph the function, you'll see that it never actually crosses the y axis into the third or fourth quadrant, the quadrants where all y values become negative.
The answer A is also correct on Edge
Have a great day dude
You must do 35 times 4 which is 140. X is 35 bags. You must sell 35 bags of popcorn
