Answer:
$390 you divide 520 by 4 so 520/4=130 now we subtract 130 from 520, 520-130=390
Step-by-step explanation:
Answer:
Step-by-step explanation:
Hello!
Given the linear regression of Y: "Annual salary" as a function of X: "Mean score on teaching evaluation" of a population of university professors. It is desired to study whether student evaluations are related to salaries.
The population equation line is
E(Y)= β₀ + β₁X
Using the information of a n= 100 sample, the following data was calculated:
R²= 0.23
Coefficient Standard Error
Intercept 25675.5 11393
x 5321 2119
The estimated equation is
^Y= 25675.5 + 5321X
Now if the interest is to test if the teaching evaluation affects the proffesor's annual salary, the hypotheses are:
H₀: β = 0
H₁: β ≠ 0
There are two statistic you can use to make this test, a Student's t or an ANOVA F.
Since you have information about the estimation of β you can calculate the two tailed t test using the formula:
~
= 25.1109
The p-value is two-tailed, and is the probability of getting a value as extreme as the calculated
under the distribution 
p-value < 0.00001
I hope it helps!
1/3(9-6m) = 3 - 2m
1/4(12m -8) = 3m - 2
3m - 2m = m
3 - -2 = 5
The answer is
M + 5
Answer:
$144,843.5
Step-by-step explanation:
In this problem we are going to apply the compound interest formula
A= P(1+r)^t
A = final amount
P = initial principal balance
r = interest rate
t = number of time periods elapsed
Given data
P= $27,000
R= 7.25%= 7.25/100= 0.0725
T= 24
A=27000(1+0.0725)^24
A= 27000(1.0725)^24
A= 27000*5.364
A= $144,843.5
In 24 years her account balance will be $144,843.5
Answer: choice B
Step-by-step explanation:
Events A and B are independent if the equation P(B)=P(B|A) or P(A∩B) = P(A) · P(B) holds true.
in this example
p(A)=1/6 {5}
p(B)=1/2 {1,3,5}
P(B|A)=1
so
P(B)≠P(B|A)
therefore A and B are dependent.