To estimate economic relationships using statistical methods.

To develop economic theories.

To collect economic data.

To forecast economic outcomes.

A cross-sectional dataset contains data on multiple individuals or entities at a single point in time, while a time series dataset contains data on a single individual or entity over time.

A cross-sectional dataset contains data on multiple individuals or entities at multiple points in time, while a time series dataset contains data on a single individual or entity at a single point in time.

A cross-sectional dataset contains data on a single individual or entity at multiple points in time, while a time series dataset contains data on multiple individuals or entities at a single point in time.

A cross-sectional dataset contains data on a single individual or entity at a single point in time, while a time series dataset contains data on multiple individuals or entities at multiple points in time.

Linear regression

Logistic regression

Poisson regression

Negative binomial regression

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

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

A parameter is a population characteristic, while a statistic is a population estimate.

A parameter is a sample characteristic, while a statistic is a sample estimate.

The hypothesis that there is no relationship between the variables.

The hypothesis that there is a relationship between the variables.

The hypothesis that the population mean is equal to a specified value.

The hypothesis that the population mean is not equal to a specified value.

The hypothesis that there is no relationship between the variables.

The hypothesis that there is a relationship between the variables.

The hypothesis that the population mean is equal to a specified value.

The hypothesis that the population mean is not equal to a specified value.

The probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the null hypothesis is true.

The probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the alternative hypothesis is true.

The probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the null hypothesis is false.

The probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the alternative hypothesis is false.

The value of the test statistic that separates the rejection region from the non-rejection region.

The value of the test statistic that is equal to the p-value.

The value of the test statistic that is equal to the null hypothesis.

The value of the test statistic that is equal to the alternative hypothesis.

A Type I error is the error of rejecting the null hypothesis when it is true, while a Type II error is the error of failing to reject the null hypothesis when it is false.

A Type I error is the error of failing to reject the null hypothesis when it is true, while a Type II error is the error of rejecting the null hypothesis when it is false.

A Type I error is the error of rejecting the alternative hypothesis when it is true, while a Type II error is the error of failing to reject the alternative hypothesis when it is false.

A Type I error is the error of failing to reject the alternative hypothesis when it is true, while a Type II error is the error of rejecting the alternative hypothesis when it is false.

The probability of rejecting the null hypothesis when it is false.

The probability of failing to reject the null hypothesis when it is false.

The probability of rejecting the null hypothesis when it is true.

The probability of failing to reject the null hypothesis when it is true.

A confidence interval is an interval that is likely to contain the true population mean, while a prediction interval is an interval that is likely to contain a future observation.

A confidence interval is an interval that is likely to contain a future observation, while a prediction interval is an interval that is likely to contain the true population mean.

A confidence interval is an interval that is likely to contain the true population mean, while a prediction interval is an interval that is likely to contain the true population median.

A confidence interval is an interval that is likely to contain the true population median, while a prediction interval is an interval that is likely to contain the true population mean.

A moving average model uses a weighted average of past observations to forecast future values, while an exponential smoothing model uses a weighted average of past observations and the most recent forecast to forecast future values.

A moving average model uses a weighted average of past observations and the most recent forecast to forecast future values, while an exponential smoothing model uses a weighted average of past observations to forecast future values.

A moving average model uses a weighted average of past observations to forecast future values, while an exponential smoothing model uses a weighted average of past observations and the most recent actual value to forecast future values.

A moving average model uses a weighted average of past observations and the most recent actual value to forecast future values, while an exponential smoothing model uses a weighted average of past observations to forecast future values.

A Box-Jenkins model is a time series model that uses a combination of autoregressive and moving average terms to forecast future values, while an ARIMA model is a time series model that uses a combination of autoregressive, moving average, and differencing terms to forecast future values.

A Box-Jenkins model is a time series model that uses a combination of autoregressive and moving average terms to forecast future values, while an ARIMA model is a time series model that uses a combination of autoregressive and differencing terms to forecast future values.

A Box-Jenkins model is a time series model that uses a combination of moving average and differencing terms to forecast future values, while an ARIMA model is a time series model that uses a combination of autoregressive, moving average, and differencing terms to forecast future values.

A Box-Jenkins model is a time series model that uses a combination of autoregressive, moving average, and differencing terms to forecast future values, while an ARIMA model is a time series model that uses a combination of autoregressive and moving average terms to forecast future values.

A GARCH model is a time series model that uses a combination of autoregressive and moving average terms to model the conditional variance of a time series, while a stochastic volatility model is a time series model that uses a combination of autoregressive and moving average terms to model the unconditional variance of a time series.

A GARCH model is a time series model that uses a combination of autoregressive and moving average terms to model the unconditional variance of a time series, while a stochastic volatility model is a time series model that uses a combination of autoregressive and moving average terms to model the conditional variance of a time series.

A GARCH model is a time series model that uses a combination of autoregressive and moving average terms to model the conditional variance of a time series, while a stochastic volatility model is a time series model that uses a combination of autoregressive and moving average terms to model the conditional mean of a time series.

A GARCH model is a time series model that uses a combination of autoregressive and moving average terms to model the conditional mean of a time series, while a stochastic volatility model is a time series model that uses a combination of autoregressive and moving average terms to model the conditional variance of a time series.