The last one. Mark a point at 10 on the y-axis, go up 5 and right one and mark this point.
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:
gradient = 
Step-by-step explanation:
calculate the gradient m using the gradient formula
m = 
with (x₁, y₁ ) = (- 6, 0) and (x₂, y₂ ) = (0, 2) ← 2 points on the line
m =
=
=
= 
Answer:
q =1
Step-by-step explanation:
q=17-18
.•. q=1
pls mark me as a hot boy
Answer:
no identity will escape! I know where you live