Which of the following statements is true regarding the Dickey-Fuller test in time series analysis?

Explanation: The Dickey-Fuller test is a statistical test used to check whether a time series is stationary. Stationarity implies that the statistical properties of the series, such as mean and variance, do not change over time. In this test, the null hypothesis assumes that the series has a unit root (non-stationary), while the alternative hypothesis assumes stationarity. By examining the p-value, we determine whether to reject the null hypothesis. A p-value below a chosen significance level (e.g., 0.05) indicates stationarity. This test is essential for models like ARIMA, which require stationary data for accurate forecasting. Option A: This describes autocorrelation, not the Dickey-Fuller test. Option C: Decomposition separates components but does not test stationarity. Option D: Measuring randomness pertains to residual analysis, not Dickey-Fuller. Option E: This describes autoregressive modeling, not stationarity testing.