To estimate and test economic relationships

To collect and analyze economic data

To develop economic theories

To forecast economic trends

Regression analysis

Time series analysis

Hypothesis testing

Factor analysis

To determine the significance of a relationship between variables

To estimate the parameters of a model

To forecast economic trends

To collect economic data

Cross-sectional data is collected at a single point in time, while time series data is collected over time.

Cross-sectional data is collected from a single group of individuals, while time series data is collected from multiple groups of individuals.

Cross-sectional data is used to study the relationship between variables at a single point in time, while time series data is used to study the relationship between variables over time.

Cross-sectional data is more reliable than time series data.

To measure the correlation between two time series

To measure the correlation between a time series and its own lagged values

To measure the correlation between a time series and its future values

To measure the correlation between a time series and its random errors

A parameter is a population value, while a statistic is a sample value.

A parameter is a fixed value, while a statistic is a random variable.

A parameter is estimated using data, while a statistic is calculated from data.

All of the above

A theorem that states that the sample mean of a large number of independent and identically distributed random variables will be approximately normally distributed.

A theorem that states that the sample variance of a large number of independent and identically distributed random variables will be approximately normally distributed.

A theorem that states that the sample median of a large number of independent and identically distributed random variables will be approximately normally distributed.

A theorem that states that the sample mode of a large number of independent and identically distributed random variables will be approximately normally distributed.

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

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

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

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

To estimate the population mean

To estimate the population variance

To estimate the population proportion

To estimate the population median

A discrete random variable can take on only a finite or countable number of values, while a continuous random variable can take on any value within a specified range.

A discrete random variable can take on only a finite number of values, while a continuous random variable can take on any value within a specified range.

A discrete random variable can take on only a countable number of values, while a continuous random variable can take on any value within a specified range.

A discrete random variable can take on any value within a specified range, while a continuous random variable can take on only a finite or countable number of values.

A function that gives the probability of a random variable taking on a specific value.

A function that gives the probability of a random variable taking on a range of values.

A function that gives the probability of a random variable taking on a set of values.

A function that gives the probability of a random variable taking on an interval of values.

A function that gives the probability of a random variable taking on a specific value or less.

A function that gives the probability of a random variable taking on a range of values or less.

A function that gives the probability of a random variable taking on a set of values or less.

A function that gives the probability of a random variable taking on an interval of values or less.

The average value of a random variable

The median value of a random variable

The mode value of a random variable

The range of a random variable

A measure of the spread of a random variable

A measure of the central tendency of a random variable

A measure of the skewness of a random variable

A measure of the kurtosis of a random variable