Answer:
1/256
Step-by-step explanation:


=> 1/256
Answer:
Where:
And we can find the intercept using this:
On this case the correct answer would be:
E. none of the above
Since the intercept has no association between the increase/decrease of the dependent variable respect to the independent variable
Step-by-step explanation:
Assuming the following options:
A. there is a positive correlation between X and Y
B. there is a negative correlation between X and Y
C. if X is increased, Y must also increase
D. if Y is increased, X must also increase
E. none of the above
If we want a model
where m represent the lope and b the intercept
Where:
And we can find the intercept using this:
On this case the correct answer would be:
E. none of the above
Since the intercept has no association between the increase/decrease of the dependent variable respect to the independent variable
Answer:
x = 2.75
Step-by-step explanation:
The goal here is to isolate the variable, or to get x by itself. We can do this by using simple math. The first step is to move every thing to the opposite side of x. We do this by subtracting 38 from 60, which leaves us with 22. Now we want divide 22 by 8. After doing this we are left with the equation x = 2.75. I know that at first fractions and decimals seem annoying and they feel like a lot of work. But if you take a second to really look at them, you will realize they are not that bad. As in most cases with math, looks can be deceiving
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