Comparison of conventional measures of skewness and kurtosis for small sample size

Yusoff, Sarah; Wah, Yap Bee

doi:10.1109/icssbe.2012.6396619

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…Table 1 below provides the results of the procedures undertaken to validate the model. The skewness and kurtosis ranged between −1.2 to 1.2, indicating that data is normally distributed [141,142]. The relationship between the variables exhibited a linear distribution pattern indicating linearity and homoscedasticity.…”

Section: Model Validationmentioning

confidence: 98%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions.

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Section: Model Validationmentioning

confidence: 98%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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“…Skewness and Excess Kurtosis (abbreviated “Kurtosis”) are measures of the distribution’s asymmetry and tailing with respect to a normally distributed population [ 45 , 46 ]. Skewness and kurtosis values of zero indicate perfectly normally distributed observed data.…”

Section: Results and Discussion–physical And Mechanical Propertiesmentioning

confidence: 99%

Analysis of Non-Destructive Indicating Properties for Predicting Compressive Strengths of Dendrocalamus sericeus Munro Bamboo Culms

et al. 2023

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Bamboo is a natural material with the potential for being used in sustainable construction. However, uncertainty in the bearing capacity of the bamboo results in nonstandard values for structural engineering design. This research studied the simple and multiple linear regression analyses for indicating properties to predict the axial compressive load capacity and strength of bamboo culms, which will be useful information for quality control during building construction and further structural grading. First, twelve basic physical properties were measured from 111 samples of Dendrocalamus sericeus Munro bamboo culms, and axial compressive load tests of the bamboo culms were performed. Then, the correlation significance of the physical properties to axial load capacity and strength (load per area) were analyzed by the Pearson correlation method. The results show that five parameters, i.e., linear mass, culm wall thickness, external diameter, moisture content, and density, were statistically significant with the responses (compressive load capacity and compressive strength) and then defined as “candidates for indicating properties”. Next, simple linear and multiple linear regression were analyzed to formulate the relationship between the significant indicating properties and the responses. From the simple linear regression analysis, linear mass can be best used as an indicating property for predicting the maximum compressive load. The maximum compressive strength was well associated with density. The multiple linear regression analysis shows an improvement in the response predictions with respect to the simple linear regression analysis with the higher R2-values. Finally, structural grading of the bamboo is determined.

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“…Besides, the power study and error analysis of the KW test considering sample sizes and data distribution notify the test's limitations [9,10]. Power plots were also computed for SW and KS tests and are analyzed for various data distributions to study tests' behavior against nonnormal distributions [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Selection of Stationarity Tests for Time Series Forecasting Using Reliability Analysis

Bawdekar

Prusty

2022

Mathematical Problems in Engineering

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Stationarity is an essential concept in time series forecasting. A reliable stationarity test that yields unbiased test outcomes is vital as it is the gateway before a suitable forecasting model development. Renewable generation time series is inherently seasonal, comprising trend components, and often volatile. These characterizing facets alongside time series length tend to bias stationarity tests’ outcomes. A critical comparison study to check the tests’ reliability is carried out in this paper using different synthetic data required for the case-to-case analysis. Based on the tests’ working, reliabilities are analyzed for different time series lengths and group sizes, varying from 200 to 1000 with an increment of 200. This provides information about changes in reliabilities of the tests for various time series lengths or group sizes. This comprehensive comparison report with a necessary set of well-illustrated figures, table, and thorough explanation of the obtained results is expected to help novice readers to select an apt combination of tests for stationarity check for renewable generation applications.

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Comparison of conventional measures of skewness and kurtosis for small sample size

Cited by 13 publications

References 8 publications

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Analysis of Non-Destructive Indicating Properties for Predicting Compressive Strengths of Dendrocalamus sericeus Munro Bamboo Culms

Selection of Stationarity Tests for Time Series Forecasting Using Reliability Analysis

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