12 13 14 21 YMS ChII: Inference for Means AP Statistics at LSHS Mr. Molesky 9 10 18 17 23 11 15 19 20 22 16 5 Across 4. The hypothesis we are gathering evidence for: 6. If Hauk, we perform a _-_ test. 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 Interval. 12. 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 hypothesis. 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 df. 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 _ than normal distributions. 5. In a Cl, the margin of error is controlled by sample size and value. 6. TI command to build an interval for a single mean. 7. Another name for a t-score: _ -statistic 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. 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 17. In a single sample setting, (n-1) = _ of freedom. Across 22. df= degrees of 23. If our p-value is very small, our evidence is. significant. Down 19. Real name of "Student" 20. As n gets larger, the t-distributions become approximately

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Chapter10: Statistics
Section10.3: Measures Of Spread
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21
YMS ChII: Inference for Means
AP Statistics at LSHS
Mr. Molesky
9
10
18
17
23
11
15
19
20
22
16
5
Across
4. The hypothesis we are gathering evidence for:
6. If Hauk, we perform a _-_ test.
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 Interval.
12. 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 hypothesis.
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
df.
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 _ than normal distributions.
5. In a Cl, the margin of error is controlled by
sample size and value.
6. TI command to build an interval for a single
mean.
7. Another name for a t-score: _ -statistic
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.
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
17. In a single sample setting, (n-1) = _ of
freedom.
Across
22. df= degrees of
23. If our p-value is very small, our evidence is.
significant.
Down
19. Real name of "Student"
20. As n gets larger, the t-distributions become
approximately
Transcribed Image Text:12 13 14 21 YMS ChII: Inference for Means AP Statistics at LSHS Mr. Molesky 9 10 18 17 23 11 15 19 20 22 16 5 Across 4. The hypothesis we are gathering evidence for: 6. If Hauk, we perform a _-_ test. 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 Interval. 12. 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 hypothesis. 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 df. 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 _ than normal distributions. 5. In a Cl, the margin of error is controlled by sample size and value. 6. TI command to build an interval for a single mean. 7. Another name for a t-score: _ -statistic 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. 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 17. In a single sample setting, (n-1) = _ of freedom. Across 22. df= degrees of 23. If our p-value is very small, our evidence is. significant. Down 19. Real name of "Student" 20. As n gets larger, the t-distributions become approximately
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