Question 1

In general, simulations are used to create an approximation of which of the following?

Accepted Answer

A)  The distribution of parameter values when the null hypothesis is false 
B)  The distribution of parameter values when the null hypothesis is true 
C)  The distribution of test statistic values when the null hypothesis is false 
D)  The distribution of test statistic values when the null hypothesis is true 
A)  The distribution of parameter values when the null hypothesis is false 
B)  The distribution of parameter values when the null hypothesis is true 
C)  The distribution of test statistic values when the null hypothesis is false 
D)  The distribution of test statistic values when the null hypothesis is true

Question 2

Bootstrapping involves sampling the original data set without replacement to create new data sets that are analyzed.

Accepted Answer

A) True 
 B)False

Question 3

Bootstrapping involves sampling the original data set without replacement to create new data sets that are analyzed under which conditions?

Accepted Answer

A)  When the mean of the estimated value is unknown 
B)  When the sampling distribution of the estimated value is unknown 
C)  When the standard deviation of the estimated value is unknown 
D)  When the variance of the estimated value is unknown 
A)  When the mean of the estimated value is unknown 
B)  When the sampling distribution of the estimated value is unknown 
C)  When the standard deviation of the estimated value is unknown 
D)  When the variance of the estimated value is unknown

Question 4

The bootstrap standard error tends to be smaller than the standard deviation.

Accepted Answer

A) True 
 B)False

Question 5

When doing a bootstrap, if we use a sample size that is too small, how does this bias our results?

Accepted Answer

A)  The confidence intervals are too narrow and the standard errors are too large. 
B)  The confidence intervals are too narrow and the standard errors are too small. 
C)  The confidence intervals are too wide and the standard errors are too large. 
D)  The confidence intervals are too wide and the standard errors are too small. 
A)  The confidence intervals are too narrow and the standard errors are too large. 
B)  The confidence intervals are too narrow and the standard errors are too small. 
C)  The confidence intervals are too wide and the standard errors are too large. 
D)  The confidence intervals are too wide and the standard errors are too small.

In general, simulations are used to create an approximation of which of the following?

Bootstrapping involves sampling the original data set without replacement to create new data sets that are analyzed.

Bootstrapping involves sampling the original data set without replacement to create new data sets that are analyzed under which conditions?

The bootstrap standard error tends to be smaller than the standard deviation.

When doing a bootstrap, if we use a sample size that is too small, how does this bias our results?

Statistics and Samples

Displaying Data

Describing Data

Estimating With Uncertainty

Probability

Hypothesis Testing

Analyzing Proportions

Fitting Probability Models to Frequency Data

Contingency Analysis: Associations Between

The Normal Distribution

Inference for a Normal Population

Comparing Two Means

Handling Violations of Assumptions

Designing Experiments

Comparing Means of More Than Two Groups

Correlation Between Numerical Variables

Regression

Multiple Explanatory Variables

Likelihood

Meta-Analysis: Combining Information From

Filters

Exam 19: Computer-Intensive Methods

In general, simulations are used to create an approximation of which of the following?

Bootstrapping involves sampling the original data set without replacement to create new data sets that are analyzed.

Bootstrapping involves sampling the original data set without replacement to create new data sets that are analyzed under which conditions?

The bootstrap standard error tends to be smaller than the standard deviation.

When doing a bootstrap, if we use a sample size that is too small, how does this bias our results?

Statistics and Samples

Displaying Data

Describing Data

Estimating With Uncertainty

Probability

Hypothesis Testing

Analyzing Proportions

Fitting Probability Models to Frequency Data

Contingency Analysis: Associations Between

The Normal Distribution

Inference for a Normal Population

Comparing Two Means

Handling Violations of Assumptions

Designing Experiments

Comparing Means of More Than Two Groups

Correlation Between Numerical Variables

Regression

Multiple Explanatory Variables

Likelihood

Meta-Analysis: Combining Information From

Filters