Statistical Methods for Transport Demand Modeling

Profillidis, Vassilios; Botzoris, George

doi:10.1016/b978-0-12-811513-8.00005-4

Cited by 69 publications

(31 citation statements)

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“…When r xy = |1|, the dependent variable y is perfectly linearly correlated with the independent variable x. Then, following a decreasing order, a coefficient of |0.8| < r xy <|1| suggests a strong linear correlation; a coefficient of |0.3| < r xy <|0.6| suggests a moderate correlation; and a coefficient of 0 < r xy < |0.3| suggests a weak correlation, respectivelyProfillidis and Botzoris [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Bayesian Linear Regression Modelling for Sperm Quality Parameters Using Age, Body Weight, Testicular Morphometry, and Combined Biometric Indices in Donkeys

Martins‐Bessa

Quaresma

Leiva³

et al. 2021

Animals

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The aim of the present study is to define and compare the predictive power of two different Bayesian models for donkey sperm quality after the evaluation of linear and combined testicular biometry indices and their relationship with age and body weight (BW). Testicular morphometry was ultrasonographically obtained from 23 donkeys (six juveniles and 17 adults), while 40 ejaculates from eight mature donkeys were analyzed for sperm output and quality assessment. Bayesian linear regression analyses were considered to build two statistical models using gel-free volume, concentration, total sperm number, motility, total motile sperm, and morphology as dependent variables. Predictive model 1 comprised the covariate of age and the independent factors testicular measurements (length, height and width), while model 2 included the covariate of age and the factors of BW, testicular volume, and gonadosomatic ratio. Although goodness-of-fit was similar, the combination of predictors in model 1 evidenced higher likelihood to predict gel-free volume (mL), concentration (×106/mL), and motility (%). Alternatively, the combination of predictors in model 2 evidenced higher predictive power for total sperm number (×109), morphologically normal spermatozoa (%), and total motile sperm count (×109). The application of the present models may be useful to gather relevant information that could be used hereafter for assisted reproductive technologies.

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Section: Methodsmentioning

confidence: 99%

Bayesian Linear Regression Modelling for Sperm Quality Parameters Using Age, Body Weight, Testicular Morphometry, and Combined Biometric Indices in Donkeys

Martins‐Bessa

Quaresma

Leiva³

et al. 2021

Animals

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“…The Pearson correlation coefficient that is a measure of the strength of a linear association between two variables is denoted by r. It can take a range of values varying from −1 to +1. The stronger the association of the two variables is, the closer the Pearson correlation coefficient, r, is either +1 or −1 depending on whether the relationship is positive or negative, respectively [37].…”

Section: Synergetic Interaction Of Parameters-pearson Correletion Coefficient (R)mentioning

confidence: 99%

“…Values close to ±1 indicate that there is a large linear dependence between the two variables. The stronger the association of the two variables, the closer the Pearson correlation coefficient is to either +1 or −1 depending on whether the relationship is positive or negative, respectively [37].…”

Section: Synergetic Implication Of Variables As Expressed Through Pearson Coefficientsmentioning

confidence: 99%

Peach Seeds Pyrolysis Integrated into a Zero Waste Biorefinery: an Experimental Study

et al. 2021

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This study reports results of an experimental investigation of peach seeds for vegetable oils and biofuel production. Dry peach seeds have undergone hexane extraction for the recovery of oils/lipids and then pyrolyzed in a laboratory reactor. Pyrolysis parametric investigation was carried out to explore the effect of temperature, heating rate, and carrier gas flow on products' yield. Energetic characterization of products was performed to assess them as biofuels, compared also with biofuels derived from other feedstocks. Results showed that pyrolysis temperature and heating rate played important role on yield and the composition of pyrolysis products. Extraction of oils/lipids reached 38.6 % wt., while pyrolysis gas, oil, and char yielded as 10-25, 37-50, and 10-20 % wt., respectively, at different pyrolysis conditions. Pyrolysis gas, bio-oil, and biochar HHVs estimated values were in the ranges of 10.5-28.5, 29-33, and 19.5-22.2 MJ/kg, respectively, all higher than that of lignite (19.3 MJ/kg). Sulfur content was not traceable in both solid and liquid products, while nitrogen content did not exceed 8.5% wt. The degree of linear association between pyrolysis parameters/variables and products' yields and HHV was examined using the Pearson correlation coefficient. The results revealed that the cascade biorefinery approach applied for the valorization of peach seeds to produce oil/lipids and fuels, by sequential processing via hexane extraction and pyrolysis, can reach increased material and energy efficiencies varying from 73.5 to 93.7 % wt. and from 30.4 to 50 %, respectively. It can also increase the environmental and economic benefit of the agri-food sector while providing a green, closed loop, circular bioeconomy industrial practice.

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“…Engagement. Descriptive statistics is a method of sorting out and analyzing data through graphs or mathematical methods and then estimating and describing the relationship between data distribution, digital features, and random variables [29]. rough descriptive statistics, the minimum and maximum estimates are solved; the other data are outliers.…”

Section: Descriptive Analysis Of College Students' Cognitivementioning

confidence: 99%

A Data-Driven Optimization Model of Important Multidimensional Factors Affecting College Students’ Cognitive Engagement in Ideological and Political Theory Course

Wang

2021

Scientific Programming

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The optimization of important multidimensional factors is conducive to cognitive engagement, which is a crucial dimension of student engagement and plays a significant role in college students’ learning of the Ideological and Political Theory Course. However, because there are many influencing factors associated with cognitive engagement, the influence mechanism and analysis strategy of this kind of model are relatively complex. In order to solve this research gap, this paper establishes an optimization model affecting Chinese college students’ cognitive engagement in IPTC on the basis of sample collection and investigation. In this process, 4,700 questionnaires were distributed to 47 colleges and universities across the country, and copies were effectively recovered (N = 3992); the effective recovery rate was 84.94%. Cronbach’s alpha of 0.759 indicates that the scale has high reliability and Pearson’s correlation coefficient P ≤ 0.001 shows that the scale has good validity. The KMO value of 0.703 in the Bartlett sphere test also shows that the scale is suitable for factor analysis. Firstly, according to the method of factor analysis, there are six important factor dimensions affecting college students’ cognitive engagement in the IPTC, namely, attention and motivation factor dimension, behavior and value attainment factor dimension, interest and practicality factor dimension, personality and will factor dimension, evaluation and time factor dimension, and knowledge and strategy factor dimension. Then, through descriptive analysis, it is found that personality and will factor dimension (M = 6.5837) plays a relatively major role while knowledge motivation dimension (M = 6.3505) has a weak impact on cognitive engagement. Finally, from linear regression analysis, there is a significant positive correlation between cognitive engagement and other variables. In addition, undergraduates are slightly lacking motivation in the learning of the course, and vigorously strengthening college students’ cognitive engagement is still necessary, so as to effectively enhance the effectiveness of the IPTC in the future.

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Statistical Methods for Transport Demand Modeling

Cited by 69 publications

References 0 publications

Bayesian Linear Regression Modelling for Sperm Quality Parameters Using Age, Body Weight, Testicular Morphometry, and Combined Biometric Indices in Donkeys

Bayesian Linear Regression Modelling for Sperm Quality Parameters Using Age, Body Weight, Testicular Morphometry, and Combined Biometric Indices in Donkeys

Peach Seeds Pyrolysis Integrated into a Zero Waste Biorefinery: an Experimental Study

A Data-Driven Optimization Model of Important Multidimensional Factors Affecting College Students’ Cognitive Engagement in Ideological and Political Theory Course

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