When we test Upper H 0: muequals0 against Upper H Subscript a: mugreater than0, we get a P-value of 0.03. a. What would the
1 answer:
Answer:
(a) The null hypothesis will be rejected.
(b) Type I error
(c) The null hypothesis will not be rejected. The error is type II error.
Step-by-step explanation:
The hypothesis provided is:
The p-value of the test obtained is 0.03
(a)
Decision rule for hypothesis testing, based on p-value, states that if the p-value is less than the significance level (α) then the null hypothesis is rejected and vice versa.
The significance level is α = 0.10
Then,
Thus, the null hypothesis will be rejected.
Conclusion:
The null hypothesis is rejected stating that the value of μ is more than 0.
(b)
If the decision in (a) is an error, i.e. the null hypothesis is rejected when in fact it is true, this type of error is known as type I error.
(c)
The significance level is α = 0.01
Then,
Thus, the null hypothesis will not be rejected.
Conclusion:
The null hypothesis was not rejected stating that the value of μ is 0.
If this decision is an error, i.e. the null hypothesis was not rejected when in fact it is false, this type of error is known as type II error.
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Answer:
a)
For this case the value for is always smaller than the value of a, assuming
So then for this case it cannot be unbiased because an unbiased estimator satisfy this property:
and that's not our case.
b)
Since is a negative value we can conclude that underestimate the real value a.
c)
And assuming independence we have this:
e) On this case we see that the estimator is better than
and the reason why is because:
and that's satisfied for n>1.
Step-by-step explanation:
Part a
For this case we are assuming
And we are are ssuming the following estimator:
For this case the value for is always smaller than the value of a, assuming
So then for this case it cannot be unbiased because an unbiased estimator satisfy this property:
and that's not our case.
Part b
For this case we assume that the estimator is given by:
And using the definition of bias we have this:
Since is a negative value we can conclude that underestimate the real value a.
And when we take the limit when n tend to infinity we got that the bias tend to 0.
Part c
For this case we the followng random variable and we can find the cumulative distribution function like this:
And assuming independence we have this:
Since all the random variables have the same distribution.
Now we can find the density function derivating the distribution function like this:
Now we can find the expected value for the random variable Y and we got this:
And the bias is given by:
And again since the bias is not 0 we have a biased estimator.
Part e
For this case we have two estimators with the following variances:
On this case we see that the estimator is better than
and the reason why is because:
and that's satisfied for n>1.