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
<span>(3x^2+3)(4+x^3)
=</span><span>12x^2 +3x^5 + 12 + 3x^3
= </span>3x^5 + 3x^3 + 12x^2 + 12
Answer:
five miles
Step-by-step explanation:
taxi A: for one mile: 10.75
taxi B: for one mile: 11.75
two miles: A: 12.25 B: 13
three miles: A: 13.75 B: 14.25
four miles: A: 15.25 B: 15.5
five miles: A: 16.75 B: 16.75
Answer:
"absolute value function," y = |x|
Step-by-step explanation:
This is the graph of the "absolute value function," y = |x|.