Where, N is the number of observations, Pi is the predicted probability for observation i, and Oi is the actual outcome for observation i. The DeLong test is based on the covariance between the models. The test statistic follows a standard normal distribution under the null hypothesis of no ...
Figure 4. Normal probability plot of the log-returns for (a) SPX and (b) BTC data. Table 3. Mean, standard dev., skewness and kurtosis of the SPX and BTC standardized residuals. Figure 5 shows the sample autocorrelation functions of the squared log-returns and squared residuals, indicat...
The amount of data generated is based on cycle time and the process capability to store data. 3.5. Process Capability Indices If the generated data follows a normal distribution, then the process capability indices can be well estimated using the traditional approach. However, as the proposed ...
The lognormal probability plot, its associated correlation coefficient and a test for significance are used to fit and assess how well a 2-parameter lognormal distribution describes the data. Daily data are analysed at all sites on an annual basis and further seasonal and meteorological ...
Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unava
Statistical methods were employed to assess differences between the training and validation cohorts, including: (1) Normality tests such as skewness and kurtosis tests for continuous variables. (2) In cases where continuous variables did not exhibit normal distribution, the Mann–Whitney U-test was ...
(b) Normal probability plot of residuals. (c) Residuals vs. Predicted values. (d) Predicted values vs. Measured values. The model determined by using all factors and interactions is checked, and it is found that all assumptions are fulfilled. The normality of residual assumptions was checked ...
When the original data do not meet distributional assumptions required for a particular statistical technique (e.g. normal distribution to apply a t-test) a common approach is transforming the data to meet the data assumptions, for example to use the logarithm of the values [4]. However, ...
A Q-Q plot produces a graph of quantiles of the variable against quantiles of the normal distribution, allowing the visual identification of marked departures of a distribution from normality. This approach is recommended over simple reliance on significance tests for normality, such as the ...
Table 4. Basic characteristics and normality test of 15 stocks from 2019 to 2021. Figure 6 shows the histogram of the normality test for 15 stocks. If the normality plot is roughly bell-shaped (high in the middle and low at the ends), the data are largely accepted as normally distribute...