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Fig. 3 depicts the relationship among power, slope of trend, and sample size for the given significance level of 0.05 and coefficient of variation C V =0.5. The power of the test is an increasing function of both the absolute slope and the sample size. In other words, as the sample size increases, the power of the test increases leading to an increased ability to discern the existence of trend.
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the common practice of testing for pre-existing trends can be formalized by setting to impose restrictions on the smoothness of the difference in trends between the pre-treatment and post-treatment periods. Likewise, economic knowledge about si-multaneous policy changes or confounding secular trends may imply sign or shape restrictions.
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The Mann-Kendall (MK) test, proposed by Mann (1945) and Kendall (1975), is a widely used nonparametric test method (Alashan 2020; Gumus et al. 2022;Khavse and Chaudhary 2022;Wang et al. 2020). In.
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Trend slope values vary between 0.0015 and 0.0125 (0.003 and 0.0125)°C per year according to Şen_ITA (SSE) method (Table 2). Furthermore, in the Şen_ITA method, maximum and minimum trend values are observed in October and February, while the SSE method gives the same in March and June. The Şen_ITA yields bigger (smaller) trend slopes than.
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Introduction. The Mann-Kendall trend test, named after H. A. Mann and D. R. Kendall, It's non-parametric test used to determine the trend to be significant overtime. The trend can be monotonically increasing or decreasing overtime. Since it is non-parametric test so we don't have to worry about distribution of the data.
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5. Difference-in-differences (DiD) analysis is one of the most widely applicable methods of analyzing the impact of a policy change. Moreover, the analysis seemed very straightforward. For example, in the two-period case, we simply estimate the linear regression: Y = a + b*Treated + c*Post + d*Treated*Post + e.
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3.1 Mann-Kendall Trend Test Trends, for all the period, were calculated using the Mann-Kendall test (Mann, 1945; Kendall, 1955). This test detects the presence of a monotonic tendency in a chronological series of a variable. It is a non-parametric method; that is, it makes no assumptions about the underlying distribution of the data, and
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The MK test for trends is a non-parametric method based on rank. The calculated S statistic is normally distributed for a number of observations N>10 and the significance of the trends is tested by comparing the standardized test statistic Z = S / [var (S)] 0.5 with the standard normal variate at the desired significance level. For N≤10, an exact S distribution has to be applied (see, e.g.
