- Conditions for inference
- The t distributions
- The one-sample t confidence interval
- The one-sample t test
- Using technology
- Matched pairs t procedures
- Robustness of t procedures
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| 360218745 | Tests and confidence intervals for the mean μ of a Normal population are based on what? | The sample mean x̄ of an SRS. | |
| 360218746 | Because of the central limit theorem, the resulting procedures are approximately correct for other population distributions when what condition is met? | When the sample is large. | |
| 360218747 | Conditions for inference about a mean: We can regard our data as an ____ from the population. This condition is very important. Observations from the population have a ___ with mean μ and standardd deviation σ. Both are unknnown parammeters. | SRS Normal distribution | |
| 360218748 | In practice, inference procedurres can accomodate some deviations from the Normality conditions when the sample is what? | Large enough. | |
| 360218749 | The standardized sample mean is the one-sample z statistic. | z = x̄-µ / σ/√n | |
| 360218750 | Why don't we use the z statistic? | If we knew σ, we would use the z statistic and the standard Normal distribution. However, in practice, we do not know z. | |
| 360218751 | We use the one-sample t statistic when? | We cannot use the z statistic due to not knowing what σ is. | |
| 360218752 | The standard error ___ replaces the standard deviatioon σ/√n in the one-sample t statistic. | s/√n | |
| 360218753 | What is the equation for the one-sample t statistic? | t = z = x̄-µ / s/√n | |
| 360218754 | The t statistic has the ___ distribution with ___ degrees of freedom. | t (distribution) n-1 |
