A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility

AbstractA generalized linear model (GLM) has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment of Public Service Electric and Gas, the largest public utility in New Jersey. Utilizing a perfect-prognosis approach, the model consists of equations derived from a backward-eliminated multiple-linear-regression analysis of observed electrical equipment damage as the predictand and corresponding surface observations from a variety of sources including local storm reports as the predictors. Weather modes, defined objectively by surface observations, provided stratification of the data and served to increase correlations between the predictand and predictors. The resulting regression equations produced coefficients of determination up to 0.855, with the lowest values for the heat and cold modes, and the highest values for the thunderstorm and mix modes. The appropriate GLM equations were applied to an independent dataset for model validation, and the GLM shows skill [i.e., Heidke skill score (HSS) values greater than 0] at predicting various thresholds of total accumulated equipment damage. The GLM shows higher HSS values relative to a climatological approach and a baseline regression model. Two case studies analyzed to critique model performance yielded insight into GLM shortcomings, with lightning information and wind duration being found to be important missing predictors under certain circumstances.

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PENGARUH LINGKUNGAN KERJA DAN KOMPENSASI TERHADAP KINERJA PEGAWAI PADA DINAS PENDIDIKAN, PEMUDA DAN OLAHRAGA KABUPATEN MAJENE

JURNAL LENTERA BISNIS ◽

10.34127/jrlab.v9i2.385 ◽

2020 ◽

Vol 9 (2) ◽

pp. 121

Author(s):

Sri Indira Hartawati ◽

Meutia A Sahur

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Work Environment ◽

Employee Performance ◽

Linear Regression Analysis ◽

Youth Sports ◽

Multiple Linear Regression Analysis ◽

Department Of Education ◽

Test Reliability ◽

Regression Equations

This research was conducted at the Department of Education, Youth and Sports of Majene Regency with the title The Effect of Work Environment and Compensation on Employee Performance. The formulation of the problem used by researchers is How the influence of the Work Environment on Employee Performance at the Education and Youth Sports Office of Majene Regency, How the influence of Compensation on Employee Performance at the Education and Youth Sports Office of Majene Regency, which variables have more influence on Employee Performance at the Education and Youth Office Majene District Sports. The research method, namely the population and sample used in this study were all employees of the Department of Education and Youth Sports of Majene Regency, which amounted to about 50 people, while the analysis method used the Validity Test, Reliability Test, Multiple Linear Regression Analysis This analysis was used to determine how much influence it had. independent variables, namely: compensation (X1), and work environment (X2) on the dependent variable, namely Employee Performance (Y). Multiple linear regression equations, Partial Significance Test (t test) and Simultaneous Test (F test). The results obtained from this study are the work environment has a significant effect on employee performance at the Department of Education and Youth Sports of Majene Regency, compensation has an effect on employee performance at the Education and Youth Sports Office of Majene Regency. and the work environment has a more dominant influence on employee performance at the Department of Education and Youth Sport, Majene Regency.Keywords: Work Environment, Compensation, Employee Performance

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Towards reproducibility of traditional fermented sausages: Texture profile analyses and modelling

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq181224027j ◽

2020 ◽

Vol 26 (1) ◽

pp. 79-87

Author(s):

Marija Jokanovic ◽

Bojana Ikonic ◽

Predrag Ikonic ◽

Vladimir Tomovic ◽

Tatjana Peulic ◽

...

Keyword(s):

Principal Component Analysis ◽

Linear Regression Analysis ◽

Principal Component ◽

Component Analysis ◽

Multiple Linear Regression Analysis ◽

Sensory Characteristics ◽

Small Scale ◽

Regression Equations ◽

Textural Characteristics ◽

Fermented Sausages

The aim of this study was to investigate textural characteristics of three traditional dry fermented sausages (Sremski kulen, Lemeski kulen and Petrovsk? klob?sa) manufactured in different small-scale facilities in northern Serbia, and to correlate them with physicochemical and sensory characteristics. The sample sausages were supplied by different local traditional producers. The textural characteristics were correlated with physicochemical and sensory characteristics using multiple linear regression analysis and principal component analysis. Differences in physicochemical characteristics reflected even more notable differences in texture characteristics. Regarding regression equations, obtained results showed that moisture content was significant for hardness, springiness and cohesiveness. Hardness was also influenced by fat content, while chewiness was influenced by protein content. Principal component analysis separated samples of Petrovsk? klob?sa, as the group with the most reproducible analysed characteristics. Obtained results of statistical analyses should provide knowledge for possible improvements of the traditional production, in a way that these sausages could be produced in different facilities with consistent textural characteristics.

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Performance evaluation of precipitation prediction skill of NCEP Global Forecasting System (GFS) over Indian region during summer monsoon 2008

MAUSAM ◽

10.54302/mausam.v61i2.795 ◽

2021 ◽

Vol 61 (2) ◽

pp. 139-154

Author(s):

V. R. DURAI ◽

S. K. ROY BHOWMIK ◽

B. MUKHOPADHYAY

Keyword(s):

Summer Monsoon ◽

Model Performance ◽

Daily Rainfall ◽

Forecast Model ◽

Skill Score ◽

Rain Gauge ◽

Geographic Region ◽

Rainfall Analysis ◽

Rainfall Prediction ◽

Quantitative Precipitation Estimates

The study provides a concise and synthesized documentation of the current level of skill of the NCEP GFS day-1 to day-5 precipitation forecasts during Indian summer monsoon of 2008, making detailed inter-comparison with daily rainfall analysis from the use of rain gauge observations and satellite (KALPANA-1) derived Quantitative Precipitation Estimates (QPE) obtained from IMD. Model performance is evaluated for day-1 to day-5 forecasts of 24-hr accumulated precipitation in terms of several accuracy and skill measures. Forecast quality and potential value are found to depend strongly on the verification dataset, geographic region and precipitation threshold. Precipitation forecasts of the model, when accumulated over the whole season, reproduce the observed pattern. However, the model predicted rainfall is comparatively higher than the observed rainfall over most parts of the country during the season. The model showed considerable skill in predicting the daily and seasonal mean rainfall over all India and also over four broad homogeneous regions of India. The model bias for rainfall prediction changes from overestimation to underestimation at the threshold of 25 mm/day except for day-1 forecast. Model skill falls dramatically for occurrence rainfall thresholds greater than 10 mm/day. This implies that the model is much better at predicting the occurrence of rainfall than they are at predicting the magnitude and location of the peak values. Various skill score and categorical statistics for the NCEP GFS model rainfall forecast for monsoon 2008 are prepared and discussed.

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DFT Based QSAR Study of Enzyme Ribonucleoside Diphosphate Reductase

E-Journal of Chemistry ◽

10.1155/2010/254539 ◽

2010 ◽

Vol 7 (3) ◽

pp. 953-961 ◽

Cited By ~ 1

Author(s):

Mohiuddin Ansari ◽

Ahmad Khalid Raza Khan ◽

Suhail Ahmad Khan

Keyword(s):

Total Energy ◽

Chemical Potential ◽

Linear Regression Analysis ◽

Dft Method ◽

Multiple Linear Regression Analysis ◽

Heat Of Formation ◽

Regression Equations ◽

Quantum Chemical Descriptors ◽

Qsar Study ◽

Ribonucleoside Diphosphate Reductase

Quantum chemical descriptors such as heat of formation, energy of HOMO, total energy, absolute hardness and chemical potential in different combinations have been used to develop QSAR models of inhibitors of enzyme ribonucleoside diphosphate reductase, RDR. The inhibitors are mainly derivatives of 1-formylisoquinoline thiosemicarbazone and 2-formylpyridine thiosemicarbazone. The values of various descriptors have been evaluated with the help of Win MOPAC 7.21 software using DFT method. Multiple linear regression analysis has been made with the help of above mentioned descriptors using the same software. Regression equations have been found to be successful models as indicated by the regression coefficientr2having the value as high as 0.96 and cross validation coefficientrCV2having the value approaching 0.95. The value of these two coefficients is indicative of high order of reliability for the proposed prediction. The results obtained are also validated on account of the closeness of observed and predicted inhibitory activities. The best combination of descriptors is heat of formation, total energy and energy of HOMO. Thus the prediction of suitability of inhibitors of the enzyme RDR can be made with the help of the best regression equation.

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Quantitative control of flowering time in wheat cultivars by vernalization and photoperiod sensitivities

Australian Journal of Agricultural Research ◽

10.1071/ar9730657 ◽

1973 ◽

Vol 24 (5) ◽

pp. 657 ◽

Cited By ~ 17

Author(s):

JR Syme

Keyword(s):

Environmental Changes ◽

Day Treatment ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Regression Equations ◽

Wheat Cultivars ◽

Sowing Time ◽

P Values ◽

Long Day ◽

Cultivar Variation

Vernalization sensitivity (V) and photoperiod sensitivity (P) were measured in a range of wheat cultivars by the hastening of development brought about by seed vernalization and long-day treatment, respectively. Using multiple linear regression analysis V and P accounted for 77-94 % of cultivar variation in the time from sowing to ear emergence, or to anthesis, over 19 sowings at three sites. From the regression equations, the optimum combination of P and V for adaptation to variable sowing time was estimated. In two cases this was predicted to be a wheat with high V and low P values. Seasonal changes in the coefficients of the multiple regression equations were related to environmental changes in temperatures and photoperiod.

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A Study of the Effect of the Front-End Styling of Sport Utility Vehicles on Pedestrian Head Injuries

Applied Bionics and Biomechanics ◽

10.1155/2018/8502507 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Guanjun Zhang ◽

Qin Qin ◽

Zheng Chen ◽

Zhonghao Bai ◽

Libo Cao

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Statistical Significance ◽

Head Injuries ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Design Stage ◽

Regression Equations ◽

Feature Parameters ◽

Front End

Background. The number of sport utility vehicles (SUVs) on China market is continuously increasing. It is necessary to investigate the relationships between the front-end styling features of SUVs and head injuries at the styling design stage for improving the pedestrian protection performance and product development efficiency. Methods. Styling feature parameters were extracted from the SUV side contour line. And simplified finite element models were established based on the 78 SUV side contour lines. Pedestrian headform impact simulations were performed and validated. The head injury criterion of 15 ms (HIC15) at four wrap-around distances was obtained. A multiple linear regression analysis method was employed to describe the relationships between the styling feature parameters and the HIC15 at each impact point. Results. The relationship between the selected styling features and the HIC15 showed reasonable correlations, and the regression models and the selected independent variables showed statistical significance. Conclusions. The regression equations obtained by multiple linear regression can be used to assess the performance of SUV styling in protecting pedestrians’ heads and provide styling designers with technical guidance regarding their artistic creations.

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Test and analysis of the porosity of cotton fiber assembly

Journal of Engineered Fibers and Fabrics ◽

10.1177/15589250211024225 ◽

2021 ◽

Vol 16 ◽

pp. 155892502110242

Author(s):

Li Yong ◽

Li Jian ◽

Liu Xian ◽

Wu Bei

Keyword(s):

Pore Structure ◽

Cotton Fiber ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Regression Equations ◽

Void Space ◽

Area Index ◽

Textile Materials ◽

Fiber Assembly ◽

White Area

Porosity is one of the most important properties of textile materials that ensures their comfort and usability. The internal pore structure of the cotton fiber assembly is complex and changeable, surface pore is difficult to explain its pore structure. It is intended to develop a method to predict the pore morphology of cotton fiber assembly. Pore image of the multi-layer fiber assembly is collected by a fiber photography instrument, used the Image Pro Plus 6.0 software to analyze, and obtained the white area indicators of image which can be applied to describe void space of fiber assembly. Using multiple linear regression analysis method, the regression equation of the white area index of image and porosity index of cotton fiber assembly is established. The results indicate that the white area index can largely be explained by three pore index namely the porosity ε, mean length of fiber between the adjacent contacts B and fiber tortuosity coefficient. Appropriate regression equations can be formulated for the pore of white area index which can aid in predicting the pore texture. Comparing the data indicators, it is found that mean length of fiber between the adjacent contacts B and the porosity ε, fiber tortuosity coefficient τ, and air permeability q have good linear correlation.

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Prediction of sheep milk chemical composition using milk yield, pH, electrical conductivity and refractive index

Journal of Dairy Research ◽

10.1017/s0022029917000772 ◽

2018 ◽

Vol 85 (1) ◽

pp. 78-82

Author(s):

Athanasios I Gelasakis ◽

Rebecca Giannakou ◽

Georgios E Valergakis ◽

Paschalis Fortomaris ◽

Antonios Kominakis ◽

...

Keyword(s):

Electrical Conductivity ◽

Refractive Index ◽

Milk Yield ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Milk Quality ◽

Regression Equations ◽

Initial Development ◽

Total Solids ◽

Solids Content

This Research Communication addresses the hypothesis that fat, protein, lactose and total solids content can be predicted using daily milk yield (DMY), pH, electrical conductivity (MEC) and refractive index (RI) of milk as predictors. It also addresses the possibility of these measurements being used for on-farm benchmarking activities towards selecting the highest yielding animals and flocks regarding milk quality traits (MQT). A total of 308 purebred Frizarta ewes were used for the study. From each individual ewe, a composite milk sample was collected. pH, MEC and RI of milk were measured and the samples were assayed for fat, protein, lactose and total solids content, using an automatic infrared milk analyser. The predictive value of DMY, pH, MEC and RI of milk on its MQT was assessed using multiple linear regression analysis. Significant regression equations were produced for all of the studied traits. RI and MEC were significant and reliable predictors for all studied MQT, whereas DMY was a significant predictor for most MQT with the exception of protein content. pH was a marginally significant predictor for some of the MQTs at the initial development of the equations but proved unreliable after bootstraping. Using these equations a number of ewes varying from 75 (for fat) to 97 (for protein) out of the 100 highest MQT yielders were correctly predicted, whereas, none of the ewes out of the 100 lowest MQT yielders was mispredicted as a high yielder for protein-, lactose- and total solids- content. Three out of 100 lowest fat-yielders were mispredicted as high fat-yielders. Similar equations can be used for benchmarking activities towards selecting the highest protein-, fat-, lactose- and total solids- yielding animals and flocks in cases where laboratories for MQT analyses are not readily available or the cost of chemical analyses is high. The method can be regarded as a handy tool for the dairy industry to readily assess milk quality at the farm level.

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Analisis Faktor – Faktor yang Mempengaruhi Produksi Getah Pinus di Kabupaten Poso

d'CARTESIAN ◽

10.35799/dc.8.2.2019.24195 ◽

2019 ◽

Vol 8 (2) ◽

pp. 127

Author(s):

Jesi A. Lateka ◽

Tohap Manurung ◽

Jantje D. Prang

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Group Work ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Regression Equations ◽

Land Area ◽

Factors Affecting ◽

Group Workers ◽

Central Sulawesi

JESI APRIANI LATEKA. Analysis of Factors Affecting Pine Gum Production in Poso District. Supervised by JANTJE D. PRANG as main supervisor and TOHAP MANURUNG as a co-supervisor. Poso Regency is a center of productivity for pine resin in Central Sulawesi, so it is very important to know the factors that influence the production of pine sap in Poso District. Referring to several previous studies, the accumulation of various factors can cause a problem or an event triggered by various previous events, to predict the use of multiple linear regression equations that can summarize these various factors. The data used in this study are data on the land area of each group (X1 ), the number of group workers (X2) and the number of hours of group work (X3). Based on the results of multiple linear regression analysis there is a symptom of multicollinearity between the variables of the number of workers and the number of hours worked, therefore to overcome this one variable is taken, namely the number of workers. The results of the analysis show that the variable area of land and the number of labor simultaneously affect the production of pine sap with a determination coefficient of 93%. Kata kunci: Analysis of Multiple Linear Regression, Pine Forest in Poso District

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Analysis of Window Components Affecting U-Value Using Thermal Transmittance Test Results and Multiple Linear Regression Analysis

Advances in Civil Engineering ◽

10.1155/2018/1780809 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7

Author(s):

Sang-Tae No ◽

Jun-Sik Seo

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Thermal Performance ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Design Stage ◽

Regression Equations ◽

Test Results ◽

Window System

Currently, global warming is accelerating, and many countries are trying to reduce greenhouse emission by enforcing low energy building. And the thermal performance of the windows is one of the factors that greatly influence the heating and cooling energy consumption of buildings. According to the development of the window system, the thermal performance of the windows is greatly improved. There are simulations and tests for window thermal performance evaluation techniques, but both are time consuming and costly. The purpose of this study is to develop a convenient method of predicting U-value at the window system design stage by multiple linear regression analysis. 532 U-value test results were collected, and window system components were set as independent values. As a result, the number of windows (single or double) among the components of the window has the greatest effect on the U-value. In this research, two regression equations for predicting U-value of window system were suggested, and the estimated standard errors of equations were 0.2569 in single window and 0.2039 in double window.

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