Domain includes all the x-values in the function. Range includes all the y-values in the function.
The first table has x-values of -1, 3, and 6, and y-values of 4, 5, and 6. This means that the domain and range would be given by the following sets:
Domain: {-1, 3, 6}
Range: {4, 5, 6}
The second set of points has x-values of -4, -4, -3, and 1, and y-values of 1, 1, 3, and 4. Duplicate points are not listed within the domain and range, so the domain and range would be given by the following sets:
Domain: {-4, -3, 1}
Range: {1, 3, 4}
Ans123
Step-by-step explanation:
sure what the zoom
Answer:
Option C) Critical value is based on the significance level and determines the boundary for the rejection region
Step-by-step explanation:
Critical Value:
- In hypothesis testing, a critical value is a point that is compared to the test statistic
- It is used to determine whether to reject the null hypothesis or accept the null hypothesis.
- If the absolute value of your test statistic is greater than the critical value,we fail to accept the null hypothesis and reject it.
- Critical value is affected by the significance level of the testing.
- It is the value that a test statistic must exceed in order for the the null hypothesis to be rejected.
Thus, option C) is the correct interpretation of critical values.
Option C) Critical value is based on the significance level and determines the boundary for the rejection region
In Bayes Theorem, apart from the already starting information, some new information so that you can update or transform the original probabilities so you now have better information.
What is Bayes Theorem?
The Bayes' theorem was named after Thomas Bayes and it is used in probability theory and statistics as one of the fundamental theorems. It estimates the likelihood of an event based on knowledge of circumstances that might be connected to it in the past.
The Formula for Bayes' Theorem
The Bayes' theorem is given as,
P(A|B) = P(A).P(B|A) / P(B)
Here, for two events A and B,
P(A|B) is the probability for likelihood of A when B is true
P(B|A) is probability for likelihood of B when A is true
P(A) is the independent probability of event A
P(B) is the independent probability of event B
Learn more about Bayes' theorem here:
brainly.com/question/17010130
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Answer:
I think it is 62.
Step-by-step explanation:
