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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
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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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.
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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