Inference for Means
Über den Vortrag
Der Vortrag „Inference for Means“ von David Spade, PhD ist Bestandteil des Kurses „Statistical Inference and Hypothesis Testing (LMU OMS 2 Spring Foundations of Modern Healthcare II Week 10)“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Inference for Means
- The t-Distribution
- Three Conditions
- Using Margin of Error
- Pitfalls to Avoid
Quiz zum Vortrag
What describes the central limit theorem?
- The distribution of the sample mean is more normal with larger sample sizes.
- The distribution of the sample mean is more normal with smaller sample sizes.
- In order for the central limit theorem to apply, the population must be normal.
- The standard deviation of the sample mean increases as the sample size increases.
- The distribution of the standard deviation increases with larger sample sizes.
What is not true of the use of the t-distribution as a model for the sample mean?
- The population standard deviation must be known.
- The data must come from a normal population.
- The population standard deviation is estimated with the sample standard deviation.
- The test statistic is computed in the same way as the z-statistic from previous procedures, but the population standard deviation is estimated.
- The data must come from a random sample.
What is not a property of the t-distribution?
- It has thinner tails than the normal distribution.
- It is more peaked than the normal distribution.
- The population's standard deviation is unknown.
- As the degrees of freedom increase, the t-distribution looks more and more like the normal distribution.
- The t-distribution may construct a confidence interval for the true mean.
What is the t-statistic if the hypothesized mean difference is 3, the sample mean difference is 1, the sample standard deviation of the difference is 2, and the sample size is 25?
- 5
- 1
- 3
- 4
- 2
What is not a condition required for a one-sample t-interval for means?
- The larger the sample size, the more unimodal and symmetric the histogram must look in order to use the t-interval.
- The data must come from a random sample.
- The data comes from a distribution that appears to be unimodal and symmetric.
- The sample size must be smaller than 10% of the population size.
- There may not be a strong skew to the data.
What is poor practice when using the one-sample t-procedure?
- It is poor practice to use the one-sample t-procedure with non-randomized data.
- It is poor practice to watch out for outliers.
- One should look for multimodal sets of data.
- It is poor practice to watch out for biased data.
- One should look for skewed data.
What is the t-statistic if the hypothesized mean is 5, sample mean is 3, sample standard deviation is 1, and sample size is 16?
- 8
- 5
- 6
- 7
- 9
For which hypothesis are we likely to use a two-tailed test?
- H1: µ ≠ 5
- H1: µ < 5
- H1: µ > 5
- H1: µ ≤ 5
- H1: µ Δ 5
A t-distribution is used when the population standard deviation is unknown and the sample size is less than what value?
- 30
- 10
- 20
- 40
- 50
Dozent des Vortrages Inference for 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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