<h3>
Answer</h3>
They are being multiplied by 2 each time.
Step-by-step explanation:
12. Cos 60° = 8/c
0,5 = 8/c
0,5 c = 8
c = 16
D² = V16²-8²
= V256-64
=V192 = V16×12 = 4V12
= 4V4×3 = 8V3
13. Cos 30° = 6/b
V3/2 = 6/b
V3 b = 12
b = 12/V3
b/Sin B = a /sin A
b/Sin90° = 6/ sin 60°
<u>b</u> = <u> </u><u> </u><u> </u><u>6</u><u> </u><u> </u><u> </u>
1 V3/2
b× <u>V3</u> = 6
2
b = 6× 2/V3
= 12/V3
Answer: the population after 10 years is 12036
Step-by-step explanation:
We would apply the formula for exponential growth which is expressed as
A = P(1 - r)^ t
Where
A represents the population after t years.
t represents the number of years.
P represents the initial population.
r represents rate of growth.
From the information given,
P = 14000
r = 1.5% = 1.5/100 = 0.015
t = 10 years
Therefore, the exponential decay equation to find the population of the town after 10 years is
A = 14000(1 - 0.015)^10
A = 14000(0.985)^10
A = 12036
Answer:
The larger acute angle is equal to 50.8 degrees.
Step-by-step explanation:
Let's solve for both of the acute angles for the purpose of checking our work at the end with angle A being the top angle and angle B being the one on the base of the triangle (that's not the 90 degrees one). Determining whether to use sin/cos/tan comes from SOH-CAH-TOA.
A = cos^-1 (2√6/2√15)
However, you need to move the radical out of the denominator by multiplying √15 to the numerator and denominator. You should come up with (2√90)/30. So,
A = cos^-1 (2√90/30) = 50.768 degrees.
B = sin^-1 (2√90/30) = 39.231 degrees.
Now, we can check the work by adding the 2 angles to 90 and, if it comes to 180, it's right.
cos^-1 (2√90/30) + sin^-1 (2√90/30) + 90 = 180.
If you have any questions on where I got a formula or any step, feel free to ask in the comments!
Answer:
A. The stepwise selection procedure uses Adjusted R-square as the "best" model criterion.
Step-by-step explanation:
Stepwise regression is a model which uses variables in step by step manner. The procedure involves removal or inclusion of independent variables one by one. It adds the most significant independent variable and removes the less significant independent variable. Usually stepwise selection uses R-square or Mallows Cp for picking the best fit.
