The Dickey-Fuller test is a statistical test used to check for stationarity in time series data. It tests the null hypothesis that a unit root is present (i.e., the data is non-stationary). A significant p-value indicates that the null hypothesis can be rejected, confirming stationarity. For example, this test is often applied before fitting ARIMA models to ensure the data meets the stationarity requirement. Why Other Options Are Wrong : A) Granger Causality Test : Tests causality between two time series, not stationarity. C) Box-Cox Transformation : Stabilizes variance but does not directly test for stationarity. D) Ljung-Box Test : Checks the independence of residuals, not stationarity. E) Kolmogorov-Smirnov Test : Compares distributions but does not assess stationarity.
Statements: A ≥ B ≥ Y = Z = M ≥ N ≤ E ≤ F = J
Conclusions:
I. F > Z
II. J ≤ Y
Statements: D > E = K ≥ G = H ≥ I, F < J ≤ I
Conclusions:
I. G ≥ F
II. J ≤ K
III. E > F
Statements: Q ≤ B = S < U > M ≥ Z
Conclusion: I. U > Q II. S ≤ Z
...Statements: S < T < U ≤ W; C > U < V < W ≤ X
Conclusion:
I. S < V
II. T < X
Statement:
N > I ≥ H > O; O ≤ J ≤ K < F; H > P < C; C = R < S;
Conclusion:
I. I > C
II. P < F
III. H < S
Statements: L < E; M = O; E >K ≥ M
Conclusions:
I. L < M
II. K = O
Statements: C ≥ E > M ≤ Z < B; G ≥ Z > K
Conclusions: I. C > K II. G ≥ B ...
In each of the questions below are given some statements followed by two conclusions. You have to take the given statements to be true even if they see...
Statements: M = R ≥ S , N = O > Q, Q > W = A < S
Conclusions :I. N ≥ S II. W > R III. O ≤ S
Statements: O < P > Q; R < V ≤ P; O > N
Conclusions:
I. P > N
II. R < O
III. Q < N
