Answer:
The answer would be, 36 sq m
A type I error takes place if a given null hypothesis is rejected but is said to be true in the population such as the forecast shows it is snowing outside but actually it is isn't.
<h3>What is type 11 error?</h3>
A type II error is known to be a statistical term that tells the error that takes place when one fails not to accept a null hypothesis that is said to be really false. A type II error create a false negative.
- It known is known also as an error of omission. Example is the forecast shows that It is not snowing but it is actually snowing.
Note that:
- Type I error is false positive.
- Type II error is false negative.
The power of the test shows that:
- The Power is the likelihood of rejecting the null hypothesis even if it is false.
- Power is the likelihood of making the right decision that is to reject the null hypothesis even if the null hypothesis is false.
- Power is the likelihood of not making a Type II error and others.
See full question below
Suppose you have the following null and alternative hypothesis:
H o: it is snowing.
H a: It is not snowing
(A) in the context of this problem, describe a Type-1 error
(B) Describe Type II error
(C)Describe power of the test
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Answer:
760,000
Step-by-step explanation:
759,993
we round 993 up to 1,000
Answer:
0.57142
Step-by-step explanation:
A normal random variable with mean and standard deviation both equal to 10 degrees Celsius. What is the probability that the temperature at a randomly chosen time will be less than or equal to 59 degrees Fahrenheit?
We are told that the Mean and Standard deviation = 10°C
We convert to Fahrenheit
(10°C × 9/5) + 32 = 50°F
Hence, we solve using z score formula
z = (x-μ)/σ, where
x is the raw score = 59 °F
μ is the population mean = 50 °F
σ is the population standard deviation = 50 °F
z = 59 - 50/50
z = 0.18
Probability value from Z-Table:
P(x ≤59) = 0.57142
The probability that the temperature at a randomly chosen time will be less than or equal to 59 degrees Fahrenheit
is 0.57142