The importance of descriptive analysis

Ferreira, Álida Rosária Silva

doi:10.1590/0100-6991e-20202682

Cited by 2 publications

(2 citation statements)

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“…These statistical metrics offered a quick overview, revealing patterns, outliers, and the overall nature of the data collected. It was a crucial step in this study that helped understand the dataset before advanced analyses, which guided the research in identifying trends and making informed decisions regarding subsequent analytical approaches [26][27][28].…”

Section: Description Of Data Setmentioning

confidence: 99%

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

Sarpong,

Akowuah,

Darko

et al. 2024

Preprint

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Machine performance modeling and optimization have emerged as crucial steps for process enhancement and efficiency. This study explored machine learning to model and optimize the cassava grating chamber of cassava grater for the quality production of gari. This domain remains unexplored thus far. A total of 196 graters were studied. Key variables studied included tooth diameter (TD), tooth height (TH), inter-tooth spacing (ITS), drum speed (DS), clearance (C), and moisture content of cassava (MC). Geometric mean diameter (GMD) represented mash quality. Feature importance rankings emphasized TH (0.488784), C (0.243284), TD (0.112682), ITS (0.103547), DS (0.036261), and MC (0.015442) in determining particle size (GMD) of grated mash. Machine learning models efficiently interpreted these attributes, including gradient boost regressor, linear regression, neural network, and random forest. The gradient boost regressor was the best predictive model, achieving 95.34% accuracy, RMSE (0.3291), and MAE (0.2303). The study provides a GMD predictive equation and optimized parameters for specific gari size production, offering valuable insights for tailored machinery in the cassava grating activity.

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Section: Description Of Data Setmentioning

confidence: 99%

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

Sarpong,

Akowuah,

Darko

et al. 2024

Preprint

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show abstract

“…The result was visualized in table 4. Meanwhile, we did not use all the descriptive statistics results to answer the problems so that we merely used mean and standard deviation to determine the result as mean and standard deviation are sufficiently sensitive for the changes (Ferreira, 2020).…”

Section: Phasementioning

confidence: 99%

The Distributions of Vocational High School Teachers’ Advanced Digital Competence (ADC)

Rahmawati¹,

Abdullah²,

Widiaty³

et al. 2022

JTET

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Digital competence is one of the significant skills to be possessed by the teachers as the impact of rapid technology development in many aspects such as economy, education, health, and environment. Various digital competence measurements and frameworks had been developed to ensure that someone has an advance digital skill and one of those is DigComp 2.1 by European Commission. Many previous studies had been studied about teachers’ digital competence levels influenced by some factors such as age, gender, working period, and educational background. However, it is infrequently analysed that the advance digital competence (ADC) of Vocational High School (VHS) teachers is differentiated by engineering field and non-engineering field, area or region and its school quality. Moreover, this study aimed: (a) to show the ADC level analysis between VHS teachers in engineering and non-engineering school, (b) to delineate the ADC level of VHS teachers based on the islands where the school is located, and (c) to represent the impact of school quality towards the VHS teachers’ ADC level. The data was analysed using a quantitative approach with a descriptive statistics method. Additionally, the instruments of the survey to collect the data were adapted from the DigCompEdu framework in part of advanced level and measured using Likert scale. The participants of this study were 392 respondents but, it was classified and divided into some fields based on VHS fields, islands and school accreditation. The result of this study expounded that VHS teachers in the engineering field have higher scores at the ADC level even though there are some ADC components that should be upgraded by the VHS teachers in both fields. Meanwhile, islands and school accreditation had no significant impact yet showed the opposite result.

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The importance of descriptive analysis

Cited by 2 publications

References 0 publications

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

A Machine Learning-Driven Modeling and Optimization Approach for Enhancing Cassava Mash Production Quality in Cassava Graters.

The Distributions of Vocational High School Teachers’ Advanced Digital Competence (ADC)

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