Answer: $ 290 thousand
Step-by-step explanation:
Given : According to a certain central bank from 2000 to 2016 the average price of a new home in a certain region increased by 62 % to $470 thousand.
Let X be the the average price of a new home in 2000 .
Then , the 62 % increase in price is given by :-
Since , the the average price of the home in 2016 = $470 thosand
Hence, the average price of a new home in 2000 = $ 290 thousand .
The answer is 133.5 I got this by dividing 267 by 2
Answer:
x = 7.5
Step-by-step explanation:
10/4 = 2.5
x = 3 × 2.5 = 7.5
Another way
x = 3/4 × 10 = 7.5
To figure out the slope you would want to use this formula for 2 of the points: Y2-Y1/X2-X1.
if you followed this for (-3,-2) and (3,2), you would get 2-(-2)/3-(-3)
this would equal 4/6, which simplifies to 2/3
the slope of line p = 2/3x
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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