China
Africa
Explore chapters and articles related to this topic
Guidelines for Statistical Procedure
Published in Sam A. Hout, Manufacturing of Quality Oral Drug Products, 2022
The normality assumption must be assessed when using variable sampling plans with standard deviation unknown for non-standard processes. The method to test for normality can be selected from the list below and should be conducted using sample sizes of minimum 30 units and a confidence level of 90%. These normality tests are available in Minitab. Probability plotting of individual values and achieving an adequate linear fit of the data.Performing a statistical test of normality, such as: A Shapiro-Wilk test.A Chi-Square test.The Anderson-Darling test for normality.Any test having an equivalent statistical power to the previous tests.
Quantitative Methods for Analyzing Experimental Studies in Patient Ergonomics Research
Published in Richard J. Holden, Rupa S. Valdez, The Patient Factor, 2021
Kapil Chalil Madathil, Joel S. Greenstein
Frequency distributions can be symmetrical or asymmetrical. One often-found symmetrical frequency distribution is the normal distribution. It has a bell-shaped curve with a vast majority of the scores located near the center of the distribution. The frequency distributions of workload scores shown in Figure 11.2 show a symmetric distribution. In contrast, in asymmetrical frequency distributions, the majority of the scores are clustered toward one end of the distribution. Asymmetrical frequency distributions can be positively or negatively skewed, with the former having frequent scores clustered at the lower end of the distribution and the latter having scores clustered at the higher end of the distribution. Commonly used statistical tests to assess the normality of a sample distribution include the Kolmogorov–Smirnov and the Shapiro–Wilk tests. A statistically significant result from these tests suggests the distribution is non-normal. Additional sources provide details on assessing normality and analyzing data with non-normal distributions (Cohen et al., 2014; Cumming & Calin-Jageman, 2016; Field, 2013).
Empirical assessment of the forms of corruption in infrastructure project procurement
Published in Emmanuel Kingsford Owusu, Albert P. C. Chan, Corruption in Infrastructure Procurement, 2020
Emmanuel Kingsford Owusu, Albert P. C. Chan
Regarding the normality test, Kim (2015) opined that almost every statistical test requires data to be normally distributed. However, this is not always the case, as the researchers do not have control over how the data should turn out. This is the rationale behind the determination of the data normality as the distribution of the data influences some of the tools to be employed to analyze the data further. As commonly conducted in other studies (e.g., Gel et al. 2007; Shan et al. 2017), the Shapiro–Wilk test tool is employed to determine the normality of the data (Olawumi and Chan 2018). In determining the distribution, the null hypothesis states that “the data are normally distributed at a significance level of 0.05.” Hence the hypothesis is rejected if the actual values of the individual variables are lower than the estimated significance level (i.e., 0.05). In this scenario, the conclusion indicating the non-normal distribution of the data can be drawn. Similar values of 0.000 were generated for all the variables, which concludes that the data are non-normally distributed.
Data-driven assessment on the corporate credit scoring mechanism for Chinese construction supervision companies
Published in Construction Management and Economics, 2023
Jun Wang, Xiaodong Li, Ashkan Memari, Martin Skitmore, Yuying Zhong, Baabak Ashuri
Descriptive statistics are first summarized to understand the frequency, mean, standard deviation, range, and distribution of CSC credit scores for each evaluation period since 2014. The total frequency represents the total number of CSCs participating in the credit evaluation during each period. The Shapiro–Wilk test (Shapiro and Wilk 1965) is conducted to check the data for normality as it is considered the most powerful compared with other normality tests (Razali and Wah 2011). To answer the first research question, the average credit scores of the 20% highest, the 20% lowest, and all CSCs are calculated. The non-parametric (distribution-free) Mann–Kendall (MK) test (Mann 1945, Kendall 1975) with an existing Python package (Hussain and Mahmud 2019) is used to assess whether to accept or reject the null hypothesis (i.e. the average of credit scores has no monotonic trend, which is measured by the cumulative comparison of each later-observed average score to all average scores observed earlier). The MK test is also applied to the total frequency to assess whether to accept or reject the null hypothesis (i.e. the number of CSCs participating in the credit evaluation has no monotonic trend, which is measured by the cumulative comparison of each later-observed total frequency to all total frequencies observed earlier).
Grain size analysis and characterization of sedimentary environment of the surface sediments along the Syrian Coast, Umm al-Tuyour (Latakia)
Published in Marine Georesources & Geotechnology, 2022
The graphic kurtosis is a quantitative measure used to describe the departure from normality of distribution. It is the peakedness of the distribution and measures the ratio between the sorting in the tails and central portion of the curve. If the tails are better sorted than the central portion, then it is termed as leptokurtic, whereas, it is platykurtic in opposite case. If both are equally sorted then mesokurtic condition prevails (Folk and Ward 1957; Ramanathan et al. 2009). The kurtosis values for the Umm al-Tuyour samples ranged between 0.69 (platykurtic) and 1.37 (leptokurtic) with a mean of 0.99 (mesokurtic) (Table 2). 41% of the samples fall under mesokurtic, 32% represent platykurtic, while 27% are leptokurtic. Friedman (1961) suggested that the high or low values of kurtosis imply that part of the sediment achieved its sorting elsewhere in a high-energy environment. Changes in the kurtosis values is a reflection of the flow characteristics of the depositing medium (Sahu 1964; Baruah, Kotoky, and Sarma 1997).
Laurel essential oil: biological activities and application for semolina preservation against the red flour beetle Tribolium castaneum (Tenebrionidae)
Published in International Journal of Environmental Health Research, 2022
Soumaya Haouel-Hamdi, Abir Soltani, Mohamed Ben Hamedou, Olfa Bachrouch, Maha Ben Abada, Chokri Messaoud, Mohamed Ali El Annabi, Jazia Sriti Eljazi, Emna Boushih, Majdi Hammami, Ferid Limam, Jouda Mediouni Ben Jemâa
The statistical analyses were performed using SPSS statistical software version 20.0. All values given were the mean of three replications, and were expressed as the mean ± standard deviation (). Significant differences between the mean values (P ≤ 0.05) were determined using the Student–Newman–Keuls Test. For each parameter (percentage mortality, AChE inhibition, volatile ffraction,and semolina characteristics), data were subjected to two-way ANOVA, with exposure time and space occupation conditions as main fixed factors plus their interactions. The means were separated using the Least Significant Difference (LSD) (P < 0.05). Where necessary, data were transformed by common logarithm or square root to meet the assumptions of normality. Correlation analyses (Pearson’s correlation coefficient) were performed between percentage mortality and exposure time and space occupation conditions.