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
200 milimeters is the answer
Hello from MrBillDoesMath!
Answer:
8(v+3) ( -1/2 (sqrt(14) - 4 v) (4 v + sqrt(14)) )
Discussion:
Given
64v^3 + 192v^2 - 56 v - 168
Factor 64v^2 from the first two terms. Factor 56 from the last two terms:
64v^2(v+3) - 56(v + 3) => factor (v+3) from both terms
(v+3) (64v^2 - 56) => factor 8 from both terms in the right ()
8(v+3)(8v^2-7) => factor 8y^2-7
8(v+3) ( -1/2 (sqrt(14) - 4 v) (4 v + sqrt(14)) )
Thank you,
MrB
<span>
4x^2-8x-12 =
(x -3) * (x +1)
4x^2-24x+32
</span><span>(x -4) * (x -2)
</span>Factored Form
(x -3) * (x +1) * <span>(x -4) * (x -2) = 0
x = 3
x = -1
x = 4
x = 2
All four roots are real numbers and so the graph crosses the x-axis four times. The graph of the equation would resemble a "W".
</span>
