IMPACTS OF BUILDING FUNCTION ON NORMALIZED-STEAM CONSUMPTION: ANALYSIS OF FLOOR AREA NORMALIZATION VERSUS LINEAR REGRESSION ON HEATING DEGREE-DAYS IN A HEATING-DOMINATED CLIMATE

ABSTRACT Linear regression analysis is one the most common methods for weather-normalizing energy data, where energy versus degree-days is plotted, quantifying the impacts of outside temperature on buildings’ energy use. However, this approach solely considers dry-bulb temperature, while other climate variables are ignored. In addition, depending on buildings’ internal loads, weather impact can be less influential, making the linear regression method not applicable for energy data normalization in internally driven buildings (such as research laboratory buildings, healthcare facilities, etc.). In this study, several existing buildings from different categories, all located on the University of Massachusetts Amherst campus and exposed to the same weather conditions in a heating-dominated climate, were analyzed. For all cases, regression of monthly steam use on heating degree-days and floor-area normalized steam data were used, investigating applicability of the former when the latter changes. It was found that internal loads can skew steam consumption, depending on the building functionality, making the effect of degree-days negligible. For laboratory-type buildings, besides heating and domestic hot water production, steam is also used for scientific experiments. Here, daily occupancy percentage, even during weekends and holidays, was higher than that of other buildings, indicating the intensity of scientific experiments performed. This significantly impacted steam consumption, resulting in higher floor-area-normalized steam usage. In these cases, steam use did not provide an outstanding correlation to heating degree-days. Whereas, for cases with other functionality-types and lower floor-area normalized steam, coefficients of determination in regressions were high. This study concludes that even for buildings located in the same climate, depending on how building functionality and occupancy schedule influence floor-area normalized steam use, multivariate linear regression can provide more accurate analysis, rather than simple linear regression of steam on heating degree-days.

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Multivariate analytical methods for simultaneous estimation of Atenolol and Hydrochlorothiazide in bulk and tablet dosage form

IP International Journal of Comprehensive and Advanced Pharmacology ◽

10.18231/j.ijcaap.2021.037 ◽

2021 ◽

Vol 6 (4) ◽

pp. 205-208

Author(s):

Sreenivasa Charan Archakam ◽

Keerthisikha Palur ◽

Praveen Kumar Arava

Keyword(s):

Linear Regression ◽

Matrix Method ◽

Linear Regression Analysis ◽

Molar Absorptivity ◽

Simultaneous Estimation ◽

Linear Regression Method ◽

Spectrophotometric Methods ◽

The Matrix ◽

Ft Ir ◽

Tablet Dosage

The present study aimed to develop simple, accurate and precise FTIR and UV spectrophotometric methods for the quantification of Atenolol and Hydrochlorothiazide in bulk and tablet dosage forms. FT-IR method like classical least squares (CLS) was developed within the range of 2366.69-3433.44; 1564.40-1673.30 cm- UV methods like Cramer’s matrix method (method-I) and linear regression analysis (Method II) were developed and they are based upon constructing the matrix set by using molar absorptivity values at 275.60 nm and 270.40 nm. The assay values for FTIR- CLS method were 102% and 108 % for Atenololand Hydrochlorothiazide respectively. Cramer’s matrix method results were found to be 95.15% and 104% for Atenolol and Hydrochlorothiazide respectively and for linear regression method they were found to be 98.50% and 106% (w/w).

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Die toepaslikheid van deeltitelheffings in Suid-Afrika

Journal of Economic and Financial Sciences ◽

10.4102/jef.v5i1.308 ◽

2012 ◽

Vol 5 (1) ◽

pp. 103-118

Author(s):

Christiaan Smit ◽

Merwe Oberholzer ◽

Suria Ellis

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Linear Regression Analysis ◽

Operating Costs ◽

Floor Area ◽

Cost Behaviour ◽

The Cost

This study challenges the fairness of sectional title schemes’ levies that are ordered by the Sectional Title Act (95 of 1986) to be allocated according to the participation quota (floor area) of units. Studies have shown that larger units in a sectional title scheme tend to subsidise the levies of smaller units. A log-transformed linear regression analysis was performed to investigate the cost behaviour of 113 sectional title schemes. The results revealed that 86% of the variation in the operating costs of a scheme is attributable to the variation in the area of a scheme, while 87% of the variation in operating costs is attributable to a variation in the number of units in a scheme. The conclusion is that the area and the number of units in a scheme are equally significant drivers of operating costs. Therefore, the study recommended that the levies should be allocated on a 50/50 basis with regard to area and number of units.

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Linear and Non-linear Regression Analysis for the Adsorption Kinetics of SO2 in a Fixed Carbon Bed Reactor – A Case Study

10.20944/preprints202112.0007.v1 ◽

2021 ◽

Author(s):

Anna M. Kisiela-Czajka ◽

Bartosz Dziejarski

Keyword(s):

Linear Regression ◽

Activated Carbons ◽

Coal Dust ◽

Linear Regression Analysis ◽

Heterogeneous Surface ◽

Hard Coal ◽

Linear Regression Method ◽

Order Kinetic ◽

Lignite Fly Ash ◽

Non Linear

Kinetic parameters of SO2 adsorption on unburned carbons from lignite fly ash and activated carbons based on hard coal dust were determined. The model studies were performed using the linear and non-linear regression method for the following models: pseudo first and second-order, intraparticle diffusion, and chemisorption on a heterogeneous surface. The quality of the fitting of a given model to empirical data was assessed based on: R2, R, Δq, SSE, ARE, χ2, HYBRID, MPSD, EABS, and SNE. It was clearly shown that it is the linear regression that more accurately reflects the behaviour of the adsorption system, which is consistent with the first-order kinetic reaction – for activated carbons (SO2+Ar) or chemisorption on a heterogeneous surface – for unburned carbons (SO2+Ar and SO2+Ar+H2O(g)+O2) and activated carbons (SO2+Ar+H2O(g)+O2). Importantly, usually, each of the approaches (linear/non-linear) indicated a different mechanism of the studied phenomenon. A certain universality of the χ2 and HYBRID functions has been proved, the minimization of which repeatedly led to the lowest SNE values for the indicated models. Fitting data by any of the non-linear equations based on the R or R2 functions only, cannot be treated as evidence/prerequisite of the existence of a given adsorption mechanism.

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The Influence of Leadership, Organization Behavior, Compensation, And Work Discipline on Employee Performance in Non-Production Departments PT. Team Metal Indonesia

Journal of Research in Psychology ◽

10.31580/jrp.v1i4.1116 ◽

2019 ◽

Vol 1 (4) ◽

pp. 13-17

Author(s):

Rejeki Bangun ◽

Sri Langgeng Ratnasari ◽

Lukmanul Hakim

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Quantitative Research ◽

Employee Performance ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Linear Regression Method ◽

Organization Behavior ◽

Simultaneous Test ◽

Simultaneous Influence

This research is to get information about leadership, organization behavior, compensation, work discipline and four factors to employee performance in Non-Production Department PT. Team Metal Indonesia. This research used a questionnaire for 94 respondents and used SPSS version 20. This research is quantitative research, statistics analysis. Multiple Linear Regression method with quantitative analysis. Based on SPSS data analysis, the researcher got the result Multiple Linear Regression analysis to leadership (X1) is 2.071 with significant 0.041, organization behavior (X2) is 0.817 with significant 0.416, compensation (X3) is -0.726 with significant 0.470 and work discipline (X4) is 2.985 with significant number 0.004. Result of the simultaneous test (F test) 8.083. Conclusion for this research are first hypothesis leadership has influence to employee performance, second hypothesis organization behavior has influence on employee performance but not significant, third hypothesis compensation has influence to employee performance but not significant, fourth hypothesis work discipline has partial influence to employee performance and last hypothesis that these four variables (leadership, organization behavior, compensation, and work discipline) have simultaneous influence on employee performance

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Pengaruh Kinerja Keuangan Terhadap Return Saham Dengan Menjadikan Kebijakan Dividen Sebagai Variabel Moderate Pada Perusahaan Yang Terdaftar Di Jakarta Islamic Indeks ( JII 2007 – 2011)

Akuisisi: Jurnal Akuntansi ◽

10.24127/akuisisi.v13i1.131 ◽

2017 ◽

Vol 13 (1) ◽

Author(s):

Angga Kurniawan

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Financial Performance ◽

Multiple Linear Regression ◽

Stock Returns ◽

Stock Return ◽

Dividend Policy ◽

Linear Regression Analysis ◽

Linear Regression Method ◽

Total Turnover

The company's financial performance is one of the factors seen by investors as a consideration to invest funds in a company by making the financial statements as a source of information. Good and bad corporate performance can show how much profit can be earned each year and will have an impact on management in setting dividend policy for investors. This study was conducted to determine the effect of financial performance on stock returns by making dividend policy as a moderate variable. Objects taken in this study amounted to 7 companies listed on the Jakarta Islamic Index (JII) for 5 consecutive years from 2007-2011. Dependent variable of this research is stock return, independent variable include Current Ratio, Return on Asset, Return on Equity, Debt to Equity Ratio, and Total Turnover Asset, while dividend policy (Dividend Payout Ratio) as moderate variable. The method used in this study is Multiple Linear Regression Method and Moderate Regression Analysis Method (Moderate Regrestion Analysis).Based on the results of multiple linear regression analysis seen from the value of significance shows that CR, ROA, ROE, and DER does not significantly influence the stock return, whereas TATO shows the result that there is a significant influence on stock return. The result of moderate regression analysis shows that the dividend policy is not able to moderate the effect of financial performance on stock return. Keywords: Financial performance (CR, ROA, ROE, DER, TATO), stock return, dividend policy (DPR).

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Analisis Pengaruh Faktor Kebutuhan Energi Listrik Tahun 2015 Terhadap Daya Yang Tersambung Dan Energi Yang Terjual Menggunakan Regresi Linear Sederhana (Studi Kasus Pada PT. PLN (Persero) Unit Area Pelayanan Dan Jaringan (APJ) Palembang)

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v9i1.439 ◽

2018 ◽

Vol 9 (1) ◽

Author(s):

Nazori Suhandi ◽

Irma Yuliawati ◽

Indah Charista

Keyword(s):

Data Mining ◽

Time Series ◽

Linear Regression ◽

Linear Regression Analysis ◽

Electric Energy ◽

Electrical Energy ◽

Regression Method ◽

Linear Regression Method ◽

Simple Linear Regression ◽

Level Of Significance

AbstractThe availability of electrical energy is a very important aspect and even become a parameter to support the successful development of a region. Proper management of electrical energy resources and directed clearly will make the potential possessed of an area developed and utilized optimally. Population growth and economic development of a region can be influenced by the use of electrical energy. The supply of electricity must be taken into account so that the electrical energy can be available in an amount that suits your needs. Demand for the use of electricity in Indonesia will always increase with economic growth in addition to the development of electrical energy is also influenced by the development of the population in terms of quantity of customers to be electricity. Predicting methods such as using time series method (Gustriansyah, 2017) or data mining methods. The purpose of this research is to know how to overcome the influence of electricity usage (VA) connected with electric energy sold (KWh). Research done by simple linear regression method to facilitate writer in processing data. Based on the calculation result using simple linear regression method can be concluded 99.2% of the variation of electric power connected can be explained by the variable amount of electrical energy sold. While the rest (100% - 99.2% = 0.8%) is explained by other causes. And the level of significance <0.05 so that the regression model can be used to predict the electrical energy sold.Keywords : Linear regression, analysis, electrical energy AbstrakKetersediaan energi listrik merupakan aspek yang sangat penting dan bahkan menjadi suatu parameter untuk mendukung keberhasilan pembangunan suatu daerah. Pengelolaan sumber daya energi listrik yang tepat dan terarah dengan jelas akan menjadikan potensi yang dimiliki suatu wilayah berkembang dan termanfaatkan secara optimal. Pertumbuhan populasi dan perkembangan ekonomi suatu wilayah dapat dipengaruhi penggunaan energi listrik. Penyediaan listrik harus diperhitungkan sehingga energi listrik dapat tersedia dalam jumlah yang sesuai dengan kebutuhan Anda. Permintaan untuk penggunaan energi listrik di Indonesia akan selalu meningkat dengan pertumbuhan ekonomi disamping pengembangan energi listrik juga dipengaruhi oleh perkembangan populasi dalam hal kuantitas pelanggan yang akan dialiri listrik. Metode untuk memprediksi seperti menggunakan metode time series (Gustriansyah, 2017) atau metode data mining. Adapun tujuan dari penelitian ini adalah untuk mengetahui bagaimana cara mengatasi pengaruh penggunaan tenaga listrik (VA) yang terhubung dengan energi listrik yang terjual (KWh). Penelitian dilakukan dengan metode regresi linier sederhana agar memudahkan penulis dalam mengolah data. Berdasarkan hasil perhitungan menggunakan metode regresi linier sederhana dapat disimpulkan sebesar 99,2% dari variasi daya listrik yang terhubung dapat dijelaskan oleh variabel jumlah energi listrik yang terjual. Sedangkan sisanya (100% - 99,2% = 0,8%) dijelaskan oleh penyebab lain. Dan tingkat signifikansi <0,05 sehingga model regresi dapat digunakan untuk memprediksi energi listrik yang terjual.Kata kunci: Regresi linier, analisis, energi listrik

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Pengaruh Perusahan Mebel Terhadap Perekonomian di Kabupaten Gorontalo

Gorontalo Development Review ◽

10.32662/golder.v2i2.681 ◽

2019 ◽

Vol 2 (2) ◽

pp. 97

Author(s):

Usman Musa Sjahrain

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Significant Influence ◽

Raw Materials ◽

Linear Regression Analysis ◽

Linear Regression Method ◽

Test Results ◽

Simple Linear Regression ◽

Average Income ◽

Independent Test

This article aims to determine the effect on the economy of the furniture company in Gorontalo regency. To analyze this article, using a multiple linear regression method. Through multiple linear regression that capital factor (X1), technology (X2) and raw materials (X3) used furniture (X) together (simultaneously) very significant influence on the Economy particularly Income (Y). Statistics prove the suitability of this model F dual independent test: F2hit = 7.99> Fdaf = 2.89, the hypothesis H1 is accepted on the real level = 0.05 or 5% gave a very significant effect on test results. Double determination coefficient: R2 = 0.6820 or 68.20%. This means that 68.20 percent of the variation of income (Y) can be explained by the capital factor (X1), technology and raw materials (X3) together (simultaneously) used a furniture company (X). Separately simple linear regression analysis that capital factor (X1) used furniture (X) has a very significant influence on the Economy particularly Income (Y). This model is proven by independent testing F1 statistics: Fdaf = 0.30 > F1hit = 0.24 H1 hypothesis is accepted on a real level = 0.05 or 5% gave a very significant effect on test results. The field data show a furniture company in Luwoo village made to do a variety of production, using a workforce of 203 people or 8.90 percent. The average income ranges from 60.000 IDR to 100,000 IDR per day.

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The Effect Of Reward And Punishment To The Performance Of PT. Telesindo Shop Tanjungpinang Employees

Journal of Business Management Review ◽

10.47153/jbmr21.822021 ◽

2021 ◽

Vol 2 (1) ◽

pp. 001-011

Author(s):

Evita Sandra

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Employee Performance ◽

Linear Regression Analysis ◽

Test Validity ◽

T Test ◽

Linear Regression Method ◽

Test Results ◽

Analysis Model ◽

Validity And Reliability

This study aims to determine the influence of awards and penalties for the performance of PT Telesindo Shop Tanjungpinang Employees. The population is 59 people with a sample of 59 employees of PT. Telesindo Shop Tanjungpinang Employees. The data analysis model uses descriptive analysis, data quality test( validity and reliability test) , multiple linear regression analysis, t test, F test and determinant coefficient test. The test results showed the performance of employees of PT. Telesindo Shop Tanjungpinang Employees with multiple linear regression method shows Y= -2,975 + 0.584 X1 + 0.761 X2 + e with the value of the regression coefficient of the reward variable (X1) = 0.584, the coefficient of regression of the penalty variable (X2) = 0.761. T test results obtained award variable (X1) t calculate > t table (4,862 > 2,003) and sig value. 0,000. Variable penalty (X2) t calculate > t table (2,413 > 2,003) and sig value. 0.019. The results showed that variable awards and punishments simultaneously and partially had a positive and significant effect on the performance of employees of PT Telesindo Shop Tanjungpinang Employees, with contributions from R Square 0.383 or 38.3% of employee performance was affected by awards and penalties, while the remaining 61.7% were influenced by other factors that were not participated in this study.

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PLANNING OF 5G NETWORK PATH LOSS IN GEOMETRY BASED STOCHASTIC CONCEPT BY USING LINEAR REGRESSION METHODS

Kursor ◽

10.21107/kursor.v10i4.245 ◽

2020 ◽

Vol 10 (4) ◽

Author(s):

Achmad Ubaidillah ◽

S. Ida Kholida

Keyword(s):

Linear Regression ◽

Linear Regression Analysis ◽

Network Planning ◽

Path Loss ◽

Regression Method ◽

Linear Regression Method ◽

Regression Methods ◽

5G Network ◽

Forecasting System ◽

Better Than

This research is a continuation of several previous studies that made 5G network planning using the Free Space Reference Path Loss model. In this study, a 5G network path loss planning was made using the Geometry Based Stochastic model. A forecasting system is created that connects the path loss with the distance between the transmitter and the receiver antenna using the linear regression method. It is important to look at 5G network planning on a different side. The result shows that the path loss value in the light of sight condition is better than the non-light of sight condition with the lowest value of 94.4271 dB at the frequency of 28 GHz and 99.5856 dB at the 73 GHz frequency. Linear Regression analysis shows that the best path loss calculation is the frequency 28 GHz of LOS conditions with MSE is 0.001 and the standard deviation error is 0.0319.

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Home Thermal Modeling: Cooling Energy Consumption and Costs in Saudi Arabia

Computer and Information Science ◽

10.5539/cis.v9n4p22 ◽

2016 ◽

Vol 9 (4) ◽

pp. 22

Author(s):

Areej A. Malibari ◽

Amjad H. Gamlo

Keyword(s):

Saudi Arabia ◽

Linear Regression ◽

Thermal Model ◽

Cooling System ◽

Linear Regression Analysis ◽

Grid Method ◽

Data Fitting ◽

Regression Method ◽

Linear Regression Method ◽

Non Linear

Objectives: The consumption of electricity and its costs are expected to be increased in Saudi Arabia due to its rapid growth in population. As the Kingdom is characterized by extreme hot climate, a massive amount of electricity consumed by the residential sector goes to power air conditioners. To control this huge amount of energyconsumedin homes, thermal models have been generated with two or more parameters. Methodology: The households’ surveys have been conducted in order to collect the data. The Non-linear regression analysis has been carried out to obtain the outcomes of study. Moreover, household surveys have been conducted for data collection. The grid algorithm and the non-linear regression have been used to learn the parameters in the model to simulate the weather in Saudi Arabia. The temperature loggers have been placed in the houses to observe the behavior of residents of using cooling system. The web forecast has been used to analyze the temperature of cities on hourly basis. Results: Simple thermal model has been built using two parameters by applying the grid and non-linear regression methods for data fitting. Then the thermal model with envelope has also been created using four parameters by applying non-linear regression method for data fitting. Conclusion: It has been evaluated through outcomes that thermal model with envelope is better as compared to simple thermal model. Moreover, the data fitting by non-linear regression method has also been observed to perform better than data fitting by grid method.

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