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:
5796 people
Step-by-step explanation:
.28 percent of 5780 is 16.184 so added 5,780+16.184=5,796.184 but rounded to a whole person is 5,796!
Answer:
.
Step-by-step explanation:
Notice that the first two factors are in the form
, which is equal to
. Start by combining and expanding these two factors:
Let
.
.
.
This expression can now be expressed as
.
stands the unit imaginary number, where
. Unless
is raised to a certain power other than
, it can be treated just like a constant.
Expand this expression using FOIL:
.
The answer is D because when you insert it into a calculator it gives you D. Yet, if you do it manually you still get the same number .