1. obtuse angle is shown, 45° is missing.
2. obtuse angle is shown, 29° is missing.
3. acute angle is shown, 65° is missing.
4. acute angle is shown, 40° is missing.
5. acute angle is shown, 42° is missing.
6. acute angle is shown, 100° is missing.
7 an obtuse angle and a acute angle are shown, 54° (for number 1) and 126° (for number 2) are missing.
8. an obtuse and a acute angle are shown, 45° (for 1) and 135° (for 2) are missing.
Answer:
We are 95% confident that the percentage of registered voters in the nation planning on voting for Robert Smith is between 49% and 55%.
Step-by-step explanation:
Given that :
Margin of Error = ±3%
Sample Proportion = 52%
Confidence level = 95%
The 95% confidence interval is :
Sample proportion ± margin of error
52% ± 3%
Lower boundary = 52% - 3% = 49%
Upper boundary = 52% + 3% = 55%
The interpretation is that at a given confidence level ; the popukation proportion based on the sample proportion and margin of error is in the confidence interval.
Step-by-step explanation:
If f(x) =x, is the father function, then all it's child function would be equally inclined to x and y-axis respectively.
Answer:
can you show a picture
Step-by-step explanation:
it depends on the situation and the problem but normally you would use the other numbers to figure out what number goes in "x"
<u>Answer:</u>
<u>Null hypothesis: Policy B remains more effective than policy A.</u>
<u>Alternate hypothesis: Policy A is more effective than policy B.</u>
<u>Step-by-step explanation:</u>
Remember, a hypothesis is a usually tentative (temporary until tested) assumption about two variables– independent and the dependent variable.
We have two types of hypothesis errors:
1. A type I error occurs when the null hypothesis (H0) is wrongly rejected.
That is, rejecting the assumption that policy B remains more effective than policy A when it is <em>actually true.</em>
2. A type II error occurs when the null hypothesis H0, is not rejected when it is actually false. That is, accepting the assumption that policy B remains more effective than policy A when it is <em>actually false.</em>