To determine whether a particular hypothesis is supported by the data.

To estimate the value of a population parameter.

To make predictions about future events.

To describe the distribution of a variable.

A population is the entire group of individuals or objects that are being studied, while a sample is a subset of the population.

A population is the average value of a variable, while a sample is the individual values of the variable.

A population is the distribution of a variable, while a sample is the mean of the variable.

A population is the standard deviation of a variable, while a sample is the variance of the variable.

The central limit theorem states that the distribution of sample means will be approximately normal, regardless of the distribution of the population from which the samples are drawn.

The central limit theorem states that the mean of a sample will be equal to the mean of the population from which the sample is drawn.

The central limit theorem states that the variance of a sample will be equal to the variance of the population from which the sample is drawn.

The central limit theorem states that the standard deviation of a sample will be equal to the standard deviation of the population from which the sample is drawn.

Correlation is the relationship between two variables, while causation is the relationship between a cause and an effect.

Correlation is the strength of the relationship between two variables, while causation is the direction of the relationship.

Correlation is the statistical measure of the relationship between two variables, while causation is the logical relationship between two variables.

Correlation is the association between two variables, while causation is the explanation for the association.

Regression analysis is a statistical method used to determine the relationship between a dependent variable and one or more independent variables.

Regression analysis is a statistical method used to estimate the value of a population parameter.

Regression analysis is a statistical method used to make predictions about future events.

Regression analysis is a statistical method used to describe the distribution of a variable.

Simple linear regression involves only one independent variable, while multiple linear regression involves two or more independent variables.

Simple linear regression involves a linear relationship between the dependent variable and the independent variable, while multiple linear regression involves a nonlinear relationship between the dependent variable and the independent variables.

Simple linear regression involves a positive relationship between the dependent variable and the independent variable, while multiple linear regression involves a negative relationship between the dependent variable and the independent variables.

Simple linear regression involves a strong relationship between the dependent variable and the independent variable, while multiple linear regression involves a weak relationship between the dependent variable and the independent variables.

The coefficient of determination is a measure of the strength of the relationship between the dependent variable and the independent variables in a regression model.

The coefficient of determination is a measure of the accuracy of the regression model.

The coefficient of determination is a measure of the significance of the regression model.

The coefficient of determination is a measure of the goodness of fit of the regression model.

To determine whether there is a significant difference between the means of two or more groups.

To estimate the value of a population parameter.

To make predictions about future events.

To describe the distribution of a variable.

A t-test is used to compare the means of two groups, while an ANOVA is used to compare the means of three or more groups.

A t-test is used to test the hypothesis that the means of two groups are equal, while an ANOVA is used to test the hypothesis that the means of three or more groups are equal.

A t-test is used to compare the means of two groups with different sample sizes, while an ANOVA is used to compare the means of two groups with the same sample size.

A t-test is used to compare the means of two groups with a normal distribution, while an ANOVA is used to compare the means of two groups with a non-normal distribution.

The chi-square test is a statistical method used to test the hypothesis that the observed frequencies of a categorical variable are equal to the expected frequencies.

The chi-square test is a statistical method used to estimate the value of a population parameter.

The chi-square test is a statistical method used to make predictions about future events.

The chi-square test is a statistical method used to describe the distribution of a variable.

Parametric tests assume that the data is normally distributed, while non-parametric tests do not assume that the data is normally distributed.

Parametric tests are more powerful than non-parametric tests.

Parametric tests are more robust than non-parametric tests.

Parametric tests are easier to use than non-parametric tests.

To reduce the number of variables in a dataset.

To identify the underlying structure of a dataset.

To make predictions about future events.

To describe the distribution of a variable.

Exploratory factor analysis is used to explore the underlying structure of a dataset, while confirmatory factor analysis is used to test a specific hypothesis about the underlying structure of a dataset.

Exploratory factor analysis is used to reduce the number of variables in a dataset, while confirmatory factor analysis is used to identify the underlying structure of a dataset.

Exploratory factor analysis is used to make predictions about future events, while confirmatory factor analysis is used to describe the distribution of a variable.

Exploratory factor analysis is used to identify the underlying structure of a dataset, while confirmatory factor analysis is used to test a specific hypothesis about the underlying structure of a dataset.

To group similar objects together.

To identify the underlying structure of a dataset.

To make predictions about future events.

To describe the distribution of a variable.

Hierarchical cluster analysis produces a dendrogram, while k-means cluster analysis produces a set of clusters.

Hierarchical cluster analysis is used to group similar objects together, while k-means cluster analysis is used to identify the underlying structure of a dataset.

Hierarchical cluster analysis is used to make predictions about future events, while k-means cluster analysis is used to describe the distribution of a variable.

Hierarchical cluster analysis is used to identify the underlying structure of a dataset, while k-means cluster analysis is used to group similar objects together.