Answer:
0.0623 ± ( 2.056 )( 0.0224 ) can be used to compute a 95% confidence interval for the slope of the population regression line of y on x
Step-by-step explanation:
Given the data in the question;
sample size n = 28
slope of the least squares regression line of y on x or sample estimate = 0.0623
standard error = 0.0224
95% confidence interval
level of significance ∝ = 1 - 95% = 1 - 0.95 = 0.05
degree of freedom df = n - 2 = 28 - 2 = 26
∴ the equation will be;
⇒ sample estimate ± ( t-test) ( standard error )
⇒ sample estimate ± (
) ( standard error )
⇒ sample estimate ± (
) ( standard error )
⇒ sample estimate ± (
) ( standard error )
{ from t table; (
) = 2.055529 = 2.056
so we substitute
⇒ 0.0623 ± ( 2.056 )( 0.0224 )
Therefore, 0.0623 ± ( 2.056 )( 0.0224 ) can be used to compute a 95% confidence interval for the slope of the population regression line of y on x
Answer:
mZR = (4x + 160)°
Step-by-step explanation:
cause, mZR = mZN
;-))
If AB = BC then set up the equation 3x - 4 = 5x - 10
Subtract 3x.
-4 = 2x - 10
Add 10.
6 = 2x
Divide by 2.
3 = x
AB is 3x - 4 so
3(3) - 4
9 - 4
5.
AB = 5
If AB is not equal to BC then this is not solvable.
But if they are I am sure AB = 5.
Hope this helps
Answer:
6x^2 + 4x
Step-by-step explanation:
g(x) + f(x)
= -8x^2 - 3x + 5 + 2x^2 + 7x - 5
= -8x^2 + 2x^2 - 3x + 7x + 5 - 5
= - 6x^2 + 4x
Answer:
Step-by-step explanation:
(35/5) - 5
7 - 5 = 2