Question 1

The population is the entire set of subjects of interest in any study, and a sample is a portion or subset of the population.

Accepted Answer

A) True 
 B)False  True

Question 2

When the sample size is smaller than 30, the normal distribution is replaced by the t distribution for calculating confidence interval.

Accepted Answer

A) True 
 B)False  True

Question 3

The best way to draw a sample is randomly-each subject should have an equal chance of being selected as part of your sample.

Accepted Answer

A) True 
 B)False  True

Question 4

A 95% confidence interval is sometimes expressed as stating that when drawing many samples, 5% of the times the mean will lie within a confidence interval.

Accepted Answer

A) True 
 B)False

Question 5

A confidence interval is defined the range within which the unknown but true population parameter is estimated to lie.

Accepted Answer

A) True 
 B)False

Question 6

It is practical to survey the entire population of a city.

Accepted Answer

A) True 
 B)False

Question 7

When both variables are categorical, the t-test should be used for hypothesis testing.

Accepted Answer

A) True 
 B)False

Question 8

Multivariate statistics involve statistics for three or more variables.

Accepted Answer

A) True 
 B)False

Question 9

A normal distribution (Appendix
A) A) is an example of a probability distribution.

Accepted Answer

A) True 
 B)False

Question 10

The standard deviation of a probability distribution is also called a standard error.

Accepted Answer

A) True 
 B)False

Question 11

The Central Limit Theorem is fundamental statistical insight that allows us to make inferences from a single sample to the population.

Accepted Answer

A) True 
 B)False

Question 12

When both variables are continuous, simple regression can be used.

Accepted Answer

A) True 
 B)False

Question 13

The formula for calculating a confidence interval for samples larger than 30 is .

Accepted Answer

A) True 
 B)False

Question 14

The estimation error is the difference between a sample statistic and a population parameter.

Accepted Answer

A) True 
 B)False

Question 15

When both variables are continuous, the χ2 test should be used for hypothesis testing.

Accepted Answer

A) True 
 B)False

Question 16

We use population parameters to estimate unknown sample statistics.

Accepted Answer

A) True 
 B)False

Question 17

Confidence intervals are also calculated for proportions (e.g., polling data)

Accepted Answer

A) True 
 B)False

Question 18

A probability distribution is a statistical function which describes all possible values and likelihoods that a variable can take.

Accepted Answer

A) True 
 B)False

Question 19

The purpose of inferential statistics is to make inferences about characteristics of the population from which the data were drawn.

Accepted Answer

A) True 
 B)False

Question 20

Statistical inference is about drawing conclusions about a population from sample data.

Accepted Answer

A) True 
 B)False

The population is the entire set of subjects of interest in any study, and a sample is a portion or subset of the population.

When the sample size is smaller than 30, the normal distribution is replaced by the t distribution for calculating confidence interval.

The best way to draw a sample is randomly-each subject should have an equal chance of being selected as part of your sample.

A 95% confidence interval is sometimes expressed as stating that when drawing many samples, 5% of the times the mean will lie within a confidence interval.

A confidence interval is defined the range within which the unknown but true population parameter is estimated to lie.

It is practical to survey the entire population of a city.

When both variables are categorical, the t-test should be used for hypothesis testing.

Multivariate statistics involve statistics for three or more variables.

A normal distribution (Appendix A) A) is an example of a probability distribution.

The standard deviation of a probability distribution is also called a standard error.

The Central Limit Theorem is fundamental statistical insight that allows us to make inferences from a single sample to the population.

When both variables are continuous, simple regression can be used.

The formula for calculating a confidence interval for samples larger than 30 is .

The estimation error is the difference between a sample statistic and a population parameter.

When both variables are continuous, the χ² test should be used for hypothesis testing.

We use population parameters to estimate unknown sample statistics.

Confidence intervals are also calculated for proportions (e.g., polling data)

A probability distribution is a statistical function which describes all possible values and likelihoods that a variable can take.

The purpose of inferential statistics is to make inferences about characteristics of the population from which the data were drawn.

Statistical inference is about drawing conclusions about a population from sample data.

Why Statistics for Public Managers and Policy Analysts

Research Design

Conceptualization and Measurement

Measuring and Managing Performance: Present and Future

Data Collection

Central Tendency

Measures of Dispersion

Contingency Tables

Getting Results

Hypothesis Testing With Chi-Square

The T-Test

Analysis of Variance Anova

Simple Regression

Multiple Regression

Logistic and Time Series Regression

Survey of Other Techniques

Filters

Exam 10: Introducing Inference: Estimation From Samples

The population is the entire set of subjects of interest in any study, and a sample is a portion or subset of the population.

When the sample size is smaller than 30, the normal distribution is replaced by the t distribution for calculating confidence interval.

The best way to draw a sample is randomly-each subject should have an equal chance of being selected as part of your sample.

A 95% confidence interval is sometimes expressed as stating that when drawing many samples, 5% of the times the mean will lie within a confidence interval.

A confidence interval is defined the range within which the unknown but true population parameter is estimated to lie.

It is practical to survey the entire population of a city.

When both variables are categorical, the t-test should be used for hypothesis testing.

Multivariate statistics involve statistics for three or more variables.

A normal distribution (Appendix A) A) is an example of a probability distribution.

The standard deviation of a probability distribution is also called a standard error.

The Central Limit Theorem is fundamental statistical insight that allows us to make inferences from a single sample to the population.

When both variables are continuous, simple regression can be used.

The formula for calculating a confidence interval for samples larger than 30 is .

The estimation error is the difference between a sample statistic and a population parameter.

When both variables are continuous, the χ2 test should be used for hypothesis testing.

We use population parameters to estimate unknown sample statistics.

Confidence intervals are also calculated for proportions (e.g., polling data)

A probability distribution is a statistical function which describes all possible values and likelihoods that a variable can take.

The purpose of inferential statistics is to make inferences about characteristics of the population from which the data were drawn.

Statistical inference is about drawing conclusions about a population from sample data.

Why Statistics for Public Managers and Policy Analysts

Research Design

Conceptualization and Measurement

Measuring and Managing Performance: Present and Future

Data Collection

Central Tendency

Measures of Dispersion

Contingency Tables

Getting Results

Hypothesis Testing With Chi-Square

The T-Test

Analysis of Variance Anova

Simple Regression

Multiple Regression

Logistic and Time Series Regression

Survey of Other Techniques

Filters

When both variables are continuous, the χ² test should be used for hypothesis testing.