Comparing two Means
Über den Vortrag
Der Vortrag „Comparing two Means“ von David Spade, PhD ist Bestandteil des Kurses „Statistics Part 2“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Comparing Two Means
- The Sampling Distribution
- Check the Conditions
- The Pooled t-Test
Quiz zum Vortrag
What should be performed before carrying out any inference about the difference between two means?
- The data in each group should be plotted using side-by-side box plots in order to look at the differences in the two distributions.
- The data should be plotted in one boxplot in order to look at the differences in the two distributions.
- Nothing needs to be done before using the two-sample t-procedures.
- The data should be grouped together and examined in one histogram in order to look at the differences between the two groups.
- The sample should undergo subgroup analysis to match age, gender, and other important characteristics.
What is not a condition necessary for a two-sample t-interval?
- Neither data set must look roughly normal.
- The data in each group are drawn independently.
- The data are collected in a suitably random fashion.
- The sample sizes are each less than 10% of the respective population sizes.
- Both data sets must look roughly normal.
When is the pooled t-test appropriate?
- The pooled t-test is appropriate when the spreads in each group are roughly the same and all other conditions for the two-sample t-procedures are satisfied.
- The pooled t-test is appropriate when both groups are independent.
- The pooled t-test is appropriate in any situation in which we are comparing two population means.
- The pooled t-test is appropriate when no distribution appears normal.
- The pooled t-test is appropriate when the spreads differ by a significant amount and all other conditions for the two-sample t-procedures are satisfied.
What is true regarding the differences between the two-sample t-procedures and the pooled t-procedures?
- Once the test statistic is calculated along with the degrees of freedom for the test statistic, the computation of the critical value is done in the same way for each procedure.
- The standard error calculations are the same for each procedure.
- The degrees of freedom are the same for the two procedures.
- The test statistic is calculated in the same way for each procedure, including the standard error calculation.
- The pooled standard error is necessary for both tests.
What is true of two-sample inference?
- A significant difference in means or proportions is not necessarily evidence of cause.
- Looking at plots to check conditions before performing inference will cause problems with the inference procedure.
- Applying the two-sample t-procedures is fine to do even if the data are not suitably randomized.
- It is not appropriate to apply the t-procedures if the groups are not independent.
- Two-sample inference provides a precise value that negates the need for a confidence interval.
For which hypothesis are we likely to use a two-tailed test?
- H1: µ1 - µ2 ≠ 15
- H1: µ1 - µ2 > 15
- H1: µ1 - µ2 < 15
- H1: µ1 - µ2 ≤ 15
- H1: µ1 - µ2 ε 15
A z-distribution is used when the population standard deviation is known and the sample size is greater than what value?
- 30
- 10
- 20
- 40
- 50
Dozent des Vortrages Comparing two Means
David Spade, PhD
Dr. David Spade is an Assistant Professor of Mathematical Sciences and Statistics at the University of Wisconsin-Milwaukee and holds a courtesy appointment as an Assistant Professor of Statistics at the University of Missouri-Kansas City, USA.
He obtained his MS in Statistics in 2010 and then completed his PhD in Statistics from Ohio State University in 2013.
An experienced mathemathics instructor, Dr. Spade has been teaching diverse statistics courses from the introductory to the graduate level since 2007.
Within Lecturio, he teaches courses on Statistics.
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