Comparing two Proportions von David Spade, PhD

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Über den Vortrag

Der Vortrag „Comparing two Proportions“ von David Spade, PhD ist Bestandteil des Kurses „Statistics Part 2“. Der Vortrag ist dabei in folgende Kapitel unterteilt:

  • Comparing Two Proportions
  • Assumptions and Conditions
  • Hypothesis Testing
  • Snoring Example
  • Example: Mechanics and Conclusion

Quiz zum Vortrag

  1. We usually do not know the population proportions, so finding the standard deviation is not possible and we have to estimate it using the standard error.
  2. The standard deviation of the difference in the proportions is simply the difference between the standard deviations of the two proportions.
  3. The standard deviation of the difference in the proportions is simply the sum of the standard deviations of the proportions.
  4. The standard error of the difference in the proportions is the sum of the standard errors of the individual proportions.
  5. The standard error is a measure that accounts for the entire population.
  1. The groups we are comparing are linearly related to each other.
  2. Each group’s observations are independent or are randomly selected.
  3. We need to observe at least 10 successes and 10 failures in each group.
  4. The sample sizes should not be larger than 10% of the respective population sizes.
  5. The sample sizes cannot be larger than 25% of the population size.
  1. We are 95% confident that p1 - p2 is between a and b.
  2. We know that the difference between the population proportions falls between a and b.
  3. The value ˆ p 1 - ˆ p 2 is a more plausible value for the difference in the population proportions than are values near a or near b.
  4. We are 95% certain that p 1 - p 2 is between a and b.
  5. We know that a and b are representative of 95% of the population.
  1. We do not know either population proportion under the null hypothesis, so we construct a pooled estimate based on the two sample proportions.
  2. The procedures described in this chapter can be used if the groups are not independent.
  3. The procedures described in this chapter can be used if the individuals in the study are not randomly sampled.
  4. We do not know either sample proportion under the null hypothesis, so we have to construct a pooled estimate based on the population proportions.
  5. A best guess at the difference between two population proportions is as accurate as a pooled estimate based on the sample proportions.
  1. Based on the data we observed, the null hypothesis is not a reasonable explanation of the behavior between the two populations
  2. We are certain that there is a difference between the two population proportions.
  3. We are certain that there is no difference between the two population proportions.
  4. We have evidence that there is no difference between the two population proportions.
  5. The null hypothesis is false.
  1. 0.24;0.09
  2. 0.24;0.08
  3. 0.24;0.07
  4. 0.24;0.06
  5. 0.24;0.05
  1. 0.55
  2. 0.57
  3. 0.49
  4. 0.51
  5. 0.53

Dozent des Vortrages Comparing two Proportions

 David Spade, PhD

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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