Answer:

But we need to calculate the mean with the following formula:

And replacing we got:

And for the sample variance we have:

And thi is the best estimator for the population variance since is an unbiased estimator od the population variance 

Step-by-step explanation:
For this case we have the following data:
1.04,1.00,1.13,1.08,1.11
And in order to estimate the population variance we can use the sample variance formula:

But we need to calculate the mean with the following formula:

And replacing we got:

And for the sample variance we have:

And thi is the best estimator for the population variance since is an unbiased estimator od the population variance 

Answer:
52
Step-by-step explanation:
6^2= 36
4^2= 16
36+16=52
You would need to go in the order of pemdas
Answer:
It is neither likely nor unlikely that it will rain today.
Step-by-step explanation:
One reason that negative numbers would not make sense for values of the variables is that the solution having a negative value is not valid. It might be that the expression represents a real word problem and a negative value will not make any sense. Hope this helps.