A study on the relationship between students' course scores and starting wages based on principal component regression

There is little research examining the confluence of what communication channel is used for which purpose with which person. This study examines the “setting” for communication that includes what is communicated (e.g. positive or negative messages), the nature of the relationship (close versus distant), and the information channel. The respondents to a web-based questionnaire ( n = 627) were Norwegian smartphone users aged 16–35 years. Respondents evaluated mobile communication services that they used in specific social settings by “checking off” all that apply. Two methods of analysis are used to examine the material. First, a Principal Component Regression validated the main method, namely a mixed model for the Analysis of Variance. Results show the probability of using a mobile communication service is based on the effects of social group, communication purpose, communication channel, and their interaction. The relationship to the interlocutor was found to have the strongest effect on channel choice.

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Principal component regression of carcass traits in meat line funaab alpha chicken genotype

10.21203/rs.3.rs-985513/v1 ◽

2021 ◽

Author(s):

Mathew Wheto ◽

Nkiruka Goodness Chima ◽

Henry T Ojoawo ◽

Matthew A Adeleke ◽

Sunday O Peters ◽

...

Keyword(s):

Carcass Traits ◽

Principal Component Regression ◽

Principal Component ◽

Liver Weight ◽

Morphometric Traits ◽

Mean Values ◽

Weight Prediction ◽

The Relationship ◽

Single Predictor ◽

Variance Explained

Abstract This study aimed to assess the relationship among carcass traits of meat line FUNAAB Alpha chicken genotype, to identify the components that defined bled weight in them using multivariate principal component regression. A total of 14 different carcass traits from sixty-eight birds were recorded and subjected to one-way analysis of variance to vet for sex effect. Phenotypic relationships among the carcass traits were also established to pave way for the principal component analysis. The results reveal significant effects between the traits measured. The male significantly (P<0.05) had greater mean values for the traits measured. Correlations among the considered carcass traits were found to be positive and significant ranging from r = 0.406 (LrWt) - 0.981 (EdWt) for the female chicken; r = 0.330 (Head Wt) - 0.978 (BdWt) for the male chicken. The extracted components PC1 to PC7 contributed 95.66% with PC1 accounting for 68.68% of the variability in the original parameters. Communality estimates varied from 0.466 (thigh weight) to 0.983 (liver weight). In the principal component regression models, Eviscerated weight accounted for 95% of the variation observed in bled weight. The use of PC1 as a single predictor, explained 96.4% of the variability, whilst combining PC1 and PC4 showed improvements in the variance explained (R2 = 96.7%) with a lower Mallow's cp (5.31). Using the principal components scores from the chicken morphometric traits was more appropriate than using the original traits in bled weight prediction.

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Southern Hemisphere Pressure Relationships during the 20th Century—Implications for Climate Reconstructions and Model Evaluation

Geosciences ◽

10.3390/geosciences9100413 ◽

2019 ◽

Vol 9 (10) ◽

pp. 413 ◽

Cited By ~ 2

Author(s):

Logan Clark ◽

Ryan Fogt

Keyword(s):

20Th Century ◽

Southern Hemisphere ◽

High Latitude ◽

Principal Component Regression ◽

Austral Summer ◽

Principal Component ◽

Direct Observations ◽

Climate Reconstructions ◽

Pcr Method ◽

The Relationship

The relationship between Southern Hemisphere middle and high-latitude regions has made it possible to generate observationally-based Antarctic pressure reconstructions throughout the 20th century, even though routinely collected observations for this continent only began around 1957. While nearly all reconstructions inherently assume stability in these relationships through time and in the absence of direct observations, this stationarity constraint can be fully tested in a model setting. Seasonal pressure reconstructions based on the principal component regression (PCR) method spanning 1905–2013 are done entirely within the framework of the Community Atmospheric version 5 (CAM5) model in this study in order to evaluate this assumption, test the robustness of the PCR procedure for Antarctic pressure reconstructions and to evaluate the CAM5 model. Notably, the CAM5 reconstructions outperformed the observationally-based reconstruction in every season except the austral summer. Other tests indicate that relationships between Antarctic pressure and pressure across the Southern Hemisphere remain stable throughout the 20th century in CAM5. In contrast, 20th century reanalyses all display marked changes in mid-to-high latitude pressure relationships in the early 20th century. Overall, comparisons indicate both the CAM5 model and the pressure reconstructions evaluated here are reliable estimates of Antarctic pressure throughout the 20th century, with the largest differences between the two resulting from differences in the underlying reconstruction predictor networks and not from changes in the model experiments.

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Use of Body Linear Measurements to Estimate Live Weight in Communal Beef Cattle

Journal of Environmental and Agricultural Studies ◽

10.32996/jeas.2021.2.2.2 ◽

2021 ◽

Vol 2 (2) ◽

pp. 11-20

Author(s):

Soul Washaya ◽

Wesley Bvirwa ◽

Godfrey Nyamushamba

Keyword(s):

Body Weight ◽

Principal Component Regression ◽

Live Weight ◽

Principal Component ◽

Body Measurements ◽

Linear Regression Models ◽

Linear Measurements ◽

Smallholder Farms ◽

Accuracy Of Prediction ◽

The Relationship

Body measurements are important criteria in the selection of elite animals for breeding. The objective of this study was to determine the relationship, accuracy of prediction of body weight from body measurements, and identifying multicollinearity from three beef breeds. Four classes of stock (bull, cows, steers, and heifers) were considered. Correlation, simple, and multiple linear regression models were fitted with body weight (BW) as the dependent variable and body length (BL), heart girth (HG), height at wither (HW), muzzle circumference (MC), and shank circumference (SC) as the independent variables. The BW of the animals ranged from 218 to 630 kg, the least being heifers and bulls were the heaviest. The pairwise phenotypic correlations showed a high and significant positive relationship between BW and body dimensions (r = 0.751- 0.96; P<0.01). However, negative correlations were observed between BW with BL and MC of r = -0.733 and -0.703 and -0.660, -0.650, for cows and heifers, respectively. Regressing BW on BL, HG, and HW measurements gave statistically significant (P<0.01) equations with R2 ranging from 0.60 to 0.79. Collinearity, as portrayed by high variance inflation factors (VIFs), tolerance values, and low eigenvalues, was evident in four of the variables. It was concluded that the regression model was useful in BW prediction for smallholder farms and the relationship between BW and other body measurements was influenced by breed and class of stock. It is recommended that ridge regression or principal component regression be used in cases where multicollinearity exisists.

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Application of Multivariate Analyses to NIR Spectra of Gelatinized Starch

Applied Spectroscopy ◽

10.1366/0003702924123665 ◽

1992 ◽

Vol 46 (9) ◽

pp. 1420-1425 ◽

Cited By ~ 81

Author(s):

D. Bertrand ◽

C. N. G. Scotter

Keyword(s):

Discriminant Analysis ◽

Near Infrared ◽

Multivariate Analyses ◽

Principal Component Regression ◽

Principal Component ◽

Effect Of Temperature ◽

Linear Discriminant ◽

Starch Gelatinization ◽

Gelatinized Starch ◽

The Relationship

This paper describes an approach for studying collections of near-infrared spectra by using multivariate analyses. The method is illustrated with the use of two sets of spectra of gelatinized starch, recorded in the transmission mode between 650 and 1235 nm. The first set consisted of 99 spectra of partly gelatinized samples (from 24.5 to 100% gelatinization). Application of principal component analysis (PCA) made it possible to identify an outlying sample and to identify the importance of spectral variations due to the effect of scattering. Hence, it was possible to eliminate the scatter variations. From principal component regression (PCR), it was shown that the relationship between corrected spectra and gelatinization was not linear. Discriminant analysis was applied to seven classes of starch gelatinization. Only five samples out of 98 were incorrectly identified. The second set of samples was designed for studying the effect of temperature variation on the spectra of fully gelatinized starch samples. It was possible to show from PCR that the relationship between the spectra and temperature was linear. The “spectral patterns” assessed from discriminant analysis of starch gelatinization and from the PCR of temperature were compared.

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Thermal Positioning Error Modeling of Servo Axis Based on Empirical Modeling Method

Micromachines ◽

10.3390/mi12020201 ◽

2021 ◽

Vol 12 (2) ◽

pp. 201

Author(s):

Yang Li ◽

Hexuan Shi ◽

Shijun Ji ◽

Fusheng Liang

Keyword(s):

Machine Tool ◽

Principal Component Regression ◽

Thermal Error ◽

Principal Component ◽

Modeling Method ◽

Error Modeling ◽

Empirical Modeling ◽

Positioning Error ◽

The Relationship ◽

Servo Axis

In order to investigate the thermal effect of a servo axis’ positioning error on the accuracy of machine tools, an empirical modeling method was proposed, which considers both the geometric and thermal positioning error. Through the analysis of the characteristics of the positioning error curves, the initial geometric positioning error was modeled with polynomial fitting, while the thermal positioning error was built with an empirical modeling method. Empirical modeling maps the relationship between the temperature points and thermal error directly, where the multi-collinearity among the temperature variables exists. Therefore, fuzzy clustering combined with principal component regression (PCR) is applied to the thermal error modeling. The PCR model can preserve information from raw variables and eliminate the effect of multi-collinearity on the error model to a certain degree. The advantages of this modeling method are its high-precision and strong robustness. Experiments were conducted on a three-axis machine tool. A criterion was also proposed to select the temperature-sensitivity points. The fitting accuracy of the comprehensive error modeling could reach about 89%, and the prediction accuracy could reach about 86%. The proposed modeling method was proven to be effective and accurate enough to predict the positioning error at any time during the machine tool operation.

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Entrepreneurial competencies and financial performance of farmers in South Africa

South African Journal of Economic and Management Sciences ◽

10.4102/sajems.v20i1.1640 ◽

2017 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Simone Nieuwoudt ◽

Johannes I.F. Henning ◽

Henry Jordaan

Keyword(s):

South Africa ◽

Financial Performance ◽

Performance Indicator ◽

Principal Component Regression ◽

Principal Component ◽

Ordinary Least Squares ◽

Operating Efficiency ◽

Least Squares Regression ◽

Entrepreneurial Competencies ◽

The Relationship

Aim:The main objective of this study was to explore the relationship between the entrepreneurial competencies of farmers and their financial performance. Setting: The study was conducted in South Africa among farmer clients of a commercial financial organisation.Methods: The financial performance of the farmers was calculated by means of financial ratios which were used to compile a single performance indicator: operating efficiency. The operating efficiency indicator was calculated using a financial-based data envelopment analysis. An entrepreneurial competencies instrument was used to measure the entrepreneurial competencies of the farmers. Ordinary least squares regression was used within the principal component regression framework to explore the relationship between entrepreneurial competencies and financial performance.Results: The results indicate there is a positive relationship between entrepreneurial competencies and financial performance of farmers. Each of the individual competencies also indicated positive correlation between the entrepreneurial competencies and financial performance.Conclusion: An increase in specific entrepreneurial competencies behaviour may increase the operating efficiency of the farm. Educational opportunities exist to educate farmers on the potential benefits of using entrepreneurial behaviour to their advantage (to benefit their operating efficiency). Sectors involved with agriculture, for example agricultural advisors, financial advisors and educational institutes, should emphasise the importance of utilising the competencies of farmers.

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COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression

Mathematics ◽

10.3390/math9111237 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1237

Author(s):

Christian Acal ◽

Manuel Escabias ◽

Ana M. Aguilera ◽

Mariano J. Valderrama

Keyword(s):

Intensive Care ◽

Principal Components ◽

Missing Values ◽

Principal Component Regression ◽

Principal Component ◽

Prediction Equation ◽

Functional Responses ◽

Principal Components Regression ◽

Functional Principal Components ◽

The Relationship

The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components’ regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables.

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What Does Human Development Index Rating Mean in Terms of Individualism/Collectivism?

European Psychologist ◽

10.1027//1016-9040.8.2.97 ◽

2003 ◽

Vol 8 (2) ◽

pp. 97-100 ◽

Cited By ~ 3

Author(s):

Maria José Sotelo ◽

Luis Gimeno

Keyword(s):

Human Development ◽

Human Development Index ◽

Principal Component ◽

General Idea ◽

Development Index ◽

Low Levels ◽

The Relationship

The authors explore an alternative way of analyzing the relationship between human development and individualism. The method is based on the first principal component of Hofstede's individualism index in the Human Development Index rating domain. Results suggest that the general idea that greater wealth brings more individualism is only true for countries with high levels of development, while for middle or low levels of development the inverse is true.

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HUBUNGAN KEPADATAN BIVALVIA DENGAN PARAMETER LINGKUNGAN DI PESISIR TANJUNG PURA KABUPATEN BANGKA TENGAH

Akuatik Jurnal Sumberdaya Perairan ◽

10.33019/akuatik.v13i2.1434 ◽

2020 ◽

Vol 13 (2) ◽

pp. 112-121

Author(s):

Sudiyar . ◽

Okto Supratman ◽

Indra Ambalika Syari

Keyword(s):

Negative Impact ◽

Principal Component ◽

Environmental Parameters ◽

Data Retrieval ◽

Distribution Patterns ◽

Random System ◽

System Method ◽

Relationship Of ◽

The Relationship ◽

Anadara Granosa

The destructive fishing feared will give a negative impact on the survival of this organism. This study aims to analyze the density of bivalves, distribution patterns, and to analyze the relationship of bivalves with environmental parameters in Tanjung Pura village. This research was conducted in March 2019. The systematic random system method was used for collecting data of bivalves. The collecting Data retrieval divided into five research stasions. The results obtained 6 types of bivalves from 3 families and the total is 115 individuals. The highest bivalve density is 4.56 ind / m², and the lowest bivalves are located at station 2,1.56 ind / m², The pattern of bivalve distribution in the Coastal of Tanjung Pura Village is grouping. The results of principal component analysis (PCA) showed that Anadara granosa species was positively correlated with TSS r = 0.890, Dosinia contusa, Anomalocardia squamosa, Mererix meretrix, Placamen isabellina, and Tellinella spengleri were positively correlated with currents r = 0.933.

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