Inference for Means 13 21 Across Down 1. TI command to perform a hypothesis test for a single mean. 2. To test a claim about a parameter, we perform a test of 3. t-distributions are 5. In a CI, the margin of error is controlled by sample size andvalue. 6. TỈ command to build an interval for a single mean. 7. Another name for a t-score: 10. Since we don't know sigma for the population, we have to rely on the sample standard 11. Name of "Student's" famous statistician friend.SKip 13. t-procedures are accurate as long as the sample data is not strongly skewed and doesn't contain outliers. 14. We reject the null if the p-value is less than 4. The hypothesis we are gathering evidence for: 6. If Ha:psk, we perform a -_test. data are the result of a paired experiment/ setting, we can perform apairs test on the differences. To estimate a mean or difference of means, we construct a 12. than normal distributions. Interval. t-distributions are more than normal distributions. 15. The sampling variability of means: Standard of x-bar. 16. The "claim" we are testing in a hypothesis test is called the 18. For df=25, the t for 95% confidence is than 1.96. 19. Name of the brewery that played a role in the development of t-distributions. 21. If our df is not on the table, we should use a conservative approach and use the 22. df= degrees of 23. If our p-value is very small, our evidence is significant. -statistic hypothesis. That is, they are 17. In a single sample setting, (n-1) =of freedom. df. 19. Real name of "Student" skle 20. As n gets larger, the t-distributions become approximately
Inference for Means 13 21 Across Down 1. TI command to perform a hypothesis test for a single mean. 2. To test a claim about a parameter, we perform a test of 3. t-distributions are 5. In a CI, the margin of error is controlled by sample size andvalue. 6. TỈ command to build an interval for a single mean. 7. Another name for a t-score: 10. Since we don't know sigma for the population, we have to rely on the sample standard 11. Name of "Student's" famous statistician friend.SKip 13. t-procedures are accurate as long as the sample data is not strongly skewed and doesn't contain outliers. 14. We reject the null if the p-value is less than 4. The hypothesis we are gathering evidence for: 6. If Ha:psk, we perform a -_test. data are the result of a paired experiment/ setting, we can perform apairs test on the differences. To estimate a mean or difference of means, we construct a 12. than normal distributions. Interval. t-distributions are more than normal distributions. 15. The sampling variability of means: Standard of x-bar. 16. The "claim" we are testing in a hypothesis test is called the 18. For df=25, the t for 95% confidence is than 1.96. 19. Name of the brewery that played a role in the development of t-distributions. 21. If our df is not on the table, we should use a conservative approach and use the 22. df= degrees of 23. If our p-value is very small, our evidence is significant. -statistic hypothesis. That is, they are 17. In a single sample setting, (n-1) =of freedom. df. 19. Real name of "Student" skle 20. As n gets larger, the t-distributions become approximately
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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Transcribed Image Text:Inference for Means
12
21
Across
4. The hypothesis we are gathering evidence for:
Down
1. TI command to perform a hypothesis test for a
single mean.
2. To test a claim about a parameter, we perform
a test of
3. t-distributions are
5. In a CI, the margin of error is controlled by
sample size and
6. TỈ command to build an interval for a single
6. If Ha:pak, we perform a
8. If data are the result of a paired experiment/
setting, we can perform a pairs test on the
differences.
9. To estimate a mean or difference of means, we
construct a
12. t-distributions are more_ than normal
distributions.
15. The sampling variability of means: Standard
of x-bar.
16. The
is called the
18. For df=25, the t for 95% confidence is
than 1.96.
19. Name of the brewery that played a role in the
development of t-distributions.
21. If our df is not on the table, we should use a
conservative approach and use the
22. df= degrees of
23. If our p-value is very small, our evidence is
significant.
test.
than normal distributions.
value.
Interval.
mean.
7. Another name for a t-score:
10. Since we don't know sigma for the population,
we have to rely on the sample standard
11. Name of "Student's" famous statistician friend.skip
13. t-procedures are That is, they are
accurate as long as the sample data is not
strongly skewed and doesn't contain outliers.
14. We reject the null if the p-value is less than
-statistic
"claim" we are testing in a hypothesis test
hypothesis.
17. In a single sample setting, (n-1) =_of
freedom.
df.
19. Real name of "Student" skle
20. As n gets larger, the t-distributions become
approximately.
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