Using a calculator, the equation for the line of best fit where x represents the month and y represents the time is given by:
a. y = −1.74x + 46.6
<h3>How to find the equation of linear regression using a calculator?</h3>
To find the equation, we need to insert the points (x,y) in the calculator.
For this problem, the points (x,y) are given as follows, from the given table:
(1, 46), (2, 42), (3,40), (4, 41), (5, 38), (6,36).
Hence, inserting these points in the calculator, the equation for the line of best fit where x represents the month and y represents the time is given by:
a. y = −1.74x + 46.6
More can be learned about a line of best fit at brainly.com/question/22992800
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Answer:
Yes, B
Step-by-step explanation:
Yes the data is linear because there is a constant rate of change.
For x or the input the rate of change is plus 2
For y or the output the rate of change is plus 4
y/x is your slope so 4/2=2
and the only equation with a slope of 2 is B
Answer:
74.30
Step-by-step explanation:
Let s = entry price for a student
Let t = entry price for a teacher
4s +5t = 95
6s+10t = 173
I will use elimination to solve this problem.
Multiply the first equation by -2
-2(4s +5t) = -2*95
Distribute
-8s - 10t = -190
Add this equation to the second equation to eliminate t
-8s - 10t = -190
6s+10t = 173
----------------------
-2s = -17
Divide by -2
-2s/-2 = -17/-2
s = 8.50
Now we need to find t
4s +5t = 95
Substitute s=8.50
4(8.50) +5(t) = 95
34 +5t = 95
Subtract 34 from each side
34-34 +5t = 95-34
5t = 61
Divide by 5
5t/5 = 61/5
t = 12.20
We want to find the cost for 3 students and 4 teachers
3s+4t
3(8.50) + 4(12.20)
25.50 + 48.80
74.30
Answer:
See the proof below.
Step-by-step explanation:
For this case we just need to apply properties of expected value. We know that the estimator is given by:
And we want to proof that 
So we can begin with this:
And we can distribute the expected value into the temrs like this:
And we know that the expected value for the estimator of the variance s is 
, or in other way 
so if we apply this property here we have:
And we know that 
so using this we can take common factor like this:
And then we see that the pooled variance is an unbiased estimator for the population variance when we have two population with the same variance.
