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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

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

College Algebra (MindTap Course List)
College Algebra (MindTap Course List)
12th Edition
ISBN:9781305652231
Author:R. David Gustafson, Jeff Hughes
Publisher:R. David Gustafson, Jeff Hughes
Chapter8: Sequences, Series, And Probability
Section8.7: Probability
Problem 58E: What is meant by the sample space of an experiment?
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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
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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