Solve for d:
(3 (a + x))/b = 2 d - 3 c
(3 (a + x))/b = 2 d - 3 c is equivalent to 2 d - 3 c = (3 (a + x))/b:
2 d - 3 c = (3 (a + x))/b
Add 3 c to both sides:
2 d = 3 c + (3 (a + x))/b
Divide both sides by 2:
Answer: d = (3 c)/2 + (3 (a + x))/(2 b)
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Solve for x:
(3 (a + x))/b = 2 d - 3 c
Multiply both sides by b/3:
a + x = (2 b d)/3 - b c
Subtract a from both sides:
Answer: x = (2 b d)/3 + (-a - b c)
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Solve for b:
(3 (a + x))/b = 2 d - 3 c
Take the reciprocal of both sides:
b/(3 (a + x)) = 1/(2 d - 3 c)
Multiply both sides by 3 (a + x):
Answer: b = (3 (a + x))/(2 d - 3 c)
Answer:
115% increase.
Step-by-step explanation:
129 - 60 = 69 / 60 = 1.15
1.15 x 100 = 115
Divide -3
-3/-3a = 15/-3
a = -5
Answer:
Each person pays $6.12
Step-by-step explanation:
Hope this helps
The probability of type II error will decrease if the level of significance of a hypothesis test is raised from 0.005 to 0.2.
<h3 /><h3>What is a type II error?</h3>
A type II error occurs when a false null hypothesis is not rejected or a true alternative hypothesis is mistakenly rejected.
It is denoted by 'β'. The power of the hypothesis is given by '1 - β'.
<h3>How the type II error is related to the significance level?</h3>
The relation between type II error and the significance level(α):
- The higher values of significance level make it easier to reject the null hypothesis. So, the probability of type II error decreases.
- The lower values of significance level make it fail to reject a false null hypothesis. So, the probability of type II error increases.
- Thus, if the significance level increases, the type II error decreases and vice-versa.
From this, it is known that when the significance level of the given hypothesis test is raised from 0.005 to 0.2, the probability of type II error will decrease.
Learn more about type II error of a hypothesis test here:
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