To identify patterns and trends in data

To make predictions about future events

To draw conclusions about the population from a sample

All of the above

A population is the entire group of individuals or objects of interest, 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.

Surveys

Experiments

Observational studies

All of the above

To display the distribution of a variable

To compare the values of two or more variables

To identify outliers in a dataset

All of the above

The mean is the average value of a variable, while the median is the middle value of a variable.

The mean is the sum of all values of a variable divided by the number of values, while the median is the value that divides the distribution into two equal halves.

The mean is more sensitive to outliers than the median.

All of the above

To display the relationship between two variables

To identify outliers in a dataset

To compare the values of two or more variables

All of the above

A correlation is a relationship between two variables, while a causation is a relationship where one variable causes the other.

A correlation is a positive relationship between two variables, while a causation is a negative relationship between two variables.

A correlation is a linear relationship between two variables, while a causation is a non-linear relationship between two variables.

None of the above

To test a claim about a population based on a sample

To estimate the value of a population parameter

To compare the values of two or more populations

All of the above

A Type I error is rejecting a true null hypothesis, while a Type II error is accepting a false null hypothesis.

A Type I error is accepting a true null hypothesis, while a Type II error is rejecting a false null hypothesis.

A Type I error is rejecting a true alternative hypothesis, while a Type II error is accepting a false alternative hypothesis.

A Type I error is accepting a true alternative hypothesis, while a Type II error is rejecting a false alternative hypothesis.

To estimate the value of a population parameter with a specified level of confidence

To test a claim about a population based on a sample

To compare the values of two or more populations

All of the above

A descriptive statistic summarizes the data in a sample, while an inferential statistic makes an inference about the population based on a sample.

A descriptive statistic is a number that describes the data in a sample, while an inferential statistic is a statement about the population based on a sample.

A descriptive statistic is used to describe the data in a sample, while an inferential statistic is used to make predictions about the population.

All of the above

To model the relationship between two or more variables

To predict the value of one variable based on the values of other variables

To identify outliers in a dataset

All of the above

A simple linear regression models the relationship between two variables, while a multiple linear regression models the relationship between three or more variables.

A simple linear regression assumes a linear relationship between the variables, while a multiple linear regression assumes a non-linear relationship between the variables.

A simple linear regression is used to predict the value of one variable based on the value of another variable, while a multiple linear regression is used to predict the value of one variable based on the values of two or more other variables.

All of the above

To analyze the pattern of a variable over time

To forecast the future values of a variable

To identify trends and seasonality in a variable

All of the above