Factor Analysis

Alkarkhi, Abbas F.M.; Alqaraghuli, Wasin A.A.

doi:10.1016/b978-0-12-814262-2.00009-1

Cited by 4 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The FA was employed to reproduce the original variability embodied in the independent variable resulting in fewer random variables called factors. The factors estimated are linked to the initial input data variance by calculating linear models ( Cruz and Topa, 2009 ; Alkarkhi and Alqaraghuli, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Sweet Potato New Varieties Screening Based on Morphology, Pulp Color, Proximal Composition, and Total Dietary Fiber Content via Factor Analysis and Principal Component Analysis

et al. 2022

View full text Add to dashboard Cite

A sample set of 18 sweet potatoes [Ipomoea batatas (L.) Lam] segmented into six registered cultivars and 12 new varieties were evaluated. The 142 tuberous roots were obtained from a sweet potato germplasm bank (BAG-sweet potato; -27.417713768824555 and -49.64874168439556), specifically from plants belonging to a sweet potato breeding program. All samples were characterized according to their morphology, instrumental pulp color, proximate composition, and total dietary fiber. The analytical results were submitted to parametric and non-parametric statistical tests for sample variance data comparison. Moreover, the screening of the cultivars and new varieties was performed by exploratory statistical analysis, factor analysis (FA), and principal component analysis (PCA). From the sixteen independent variables that characterized the samples, the exploratory FA identified thirteen that had a communality greater than 0.7, with 92.08% of assertiveness. The PCA generated 4 principal components able to account for 84.01% of the explanatory variance. So, among the six registered cultivars, SCS372 Marina and SCS370 Luiza showed the capability to be employed as cultivars for production. Among the 12 sweet potato new varieties, samples 17025-13, 17125-10, and 17117 met the requirements for patent and registration. These results will be useful to farmers who wish to use these sweet potatoes in the development of their crops.

show abstract

Section: Methodsmentioning

confidence: 99%

Sweet Potato New Varieties Screening Based on Morphology, Pulp Color, Proximal Composition, and Total Dietary Fiber Content via Factor Analysis and Principal Component Analysis

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Es una técnica multivariante que se utiliza para describir las relaciones entre diferentes variables en estudio (variables observables) con nuevas variables denominadas factores, donde el número de factores es menor que el número de variables originales (Alkarkhi & Alqaraghuli, 2019). Para que sea adecuada para el análisis factorial, la correlación debe tener ciertas propiedades; principalmente debe haber suficiente interrelación (correlación) entre las variables.…”

Section: Otros Métodos Análisis Factorial Múltiple (Mfa)unclassified

CHAPTER 33. Coffee Volatile and Aroma Compounds – From the Green Bean to the Cup

Yeretzian¹,

Opitz²,

Smrke³

et al. 2019

Coffee

View full text Add to dashboard Cite

show abstract

Investigating key factors influencing decision-making in the design of buildings and places: A survey of stakeholders’ perception

Stanitsa

Hallett

Jude

2022

Archit. Struct. Constr.

View full text Add to dashboard Cite

Despite the impact of decision-making in the design of buildings and places, there is limited understanding concerning how decisions are best made, or how these should be evaluated and optimised. Additionally, technological advancements have increased human-to-machine interactions, altering existing decision-making processes. By understanding how novel technologies affect decisions, it motivates the development of the process, tools, and metrics. The aim of this paper is to investigate, quantify, and rank the relative importance of the decision-making factors contributing to the design of building and urban projects. A survey was conducted to gain an insight of stakeholders’ perceptions as to which are the influencing factors affecting decision-making processes in the design of buildings and places. Ten distinct factors were generated , of which, four were ranked as highly important for all stakeholder types, namely: Potential for Dynamic Operation, Thoroughness, Recency of Tools and Control. This study provides a new means to evaluate performance of decision-making processes, when these are undertaken, by developing and applying a quantitative data-driven, evidence-based methodological framework. The recipients of the findings will be the urban planners, designers, and academics who are interested in improving existing approaches in design and final decision outcomes utilising novel technologies.

show abstract

Factor Analysis

Cited by 4 publications

References 1 publication

Sweet Potato New Varieties Screening Based on Morphology, Pulp Color, Proximal Composition, and Total Dietary Fiber Content via Factor Analysis and Principal Component Analysis

Sweet Potato New Varieties Screening Based on Morphology, Pulp Color, Proximal Composition, and Total Dietary Fiber Content via Factor Analysis and Principal Component Analysis

CHAPTER 33. Coffee Volatile and Aroma Compounds – From the Green Bean to the Cup

Investigating key factors influencing decision-making in the design of buildings and places: A survey of stakeholders’ perception

Contact Info

Product

Resources

About