A multiple regression model for prediction of optimal dose of Moringa Oleifera in faecal sludge dewatering

Abstract Moringa Oleifera (MO) is a highly effective conditioner in the dewatering of Fecal sludge (FS). However, the model for the prediction of its optimal dose has not yet been documented. This article presents the results of the developed model for the prediction of MO optimal doses. The developed model was based on assessing the FS parameters and MO stock solution. The FS samples were obtained from a mixture of a pit latrine and septic tank and were analyzed at the water quality laboratory of the University of Dar es Salaam. The multiple linear regression model was used to establish a relationship between MO optimal dose as a function of FS characteristics (pH, Electrical Conductivity, Total Solids and Total Suspended Solids) and concentration of MO stock solution. The results indicated that the main contributing factors which determine the MO optimal dose were the concentration of MO stock solution, followed by pH of FS. The model results showed a good agreement between the predicted and observed MO optimal dose with a coefficient of determination of R2 = 0.72 and 0.9 for calibration and validation respectively. Therefore, the model can be adapted to determine the MO optimal dose without running the Jar-test experiment.

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Decrease in Serum Urate Level Is Associated With Loss of Visceral Fat in Male Gout Patients

Frontiers in Endocrinology ◽

10.3389/fendo.2021.724822 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zijing Ran ◽

Xiaomei Xue ◽

Lin Han ◽

Robert Terkeltaub ◽

Tony R. Merriman ◽

...

Keyword(s):

Regression Model ◽

Visceral Fat ◽

Multiple Linear Regression Model ◽

Serum Urate ◽

Multiple Regression Model ◽

Final Visit ◽

Visceral Fat Area ◽

Serum Urate Level ◽

Lowering Therapy ◽

The Relationship

ObjectiveTo clarify the relationship between serum urate (SU) decrease and visceral fat area (VFA) reduction in patients with gout.MethodsWe retrospectively analyzed 237 male gout patients who had two sets of body composition and metabolic measurements within 6 months. Subjects included had all been treated with urate-lowering therapy (ULT) (febuxostat 20–80 mg/day or benzbromarone 25–50 mg/day, validated by the medical record). All patients were from the specialty gout clinic of The Affiliated Hospital of Qingdao University. The multiple linear regression model evaluated the relationship between change in SU [ΔSU, (baseline SU) – (final visit SU)] and change in VFA [ΔVFA, (baseline VFA) – (final visit VFA)].ResultsULT resulted in a mean (standard deviation) decrease in SU level (464.22 ± 110.21 μmol/L at baseline, 360.93 ± 91.66 μmol/L at the final visit, p <0.001) accompanied by a decrease in median (interquartile range) VFA [97.30 (81.15–118.55) at baseline, 90.90 (75.85–110.05) at the final visit, p < 0.001]. By multiple regression model, ΔSU was identified to be a significant determinant variable of decrease in VFA (beta, 0.302; p = 0.001).ConclusionsThe decrease in SU level is positively associated with reduced VFA. This finding provides a rationale for clinical trials to affirm whether ULT promotes loss of visceral fat in patients with gout.

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Effect of Strategic Intent on Performance of Small and Medium Scale Printing Press Firms in Abuja, Nigeria

Entrepreneurship Review ◽

10.38157/entrepreneurship-review.v1i2.166 ◽

2020 ◽

Vol 1 (2) ◽

pp. 26-37

Author(s):

Felicia Eze ◽

Murat Akyüz ◽

Opusunju Michael Isaac

Keyword(s):

Central Bank ◽

Regression Model ◽

Mission Statement ◽

Control Variable ◽

Multiple Regression Model ◽

Printing Press ◽

Coefficient Of Determination ◽

Access To Finance ◽

Medium Scale ◽

Strategic Intent

Purpose: This study investigates the effect of strategic intent on the performance of small and medium scale printing firms in Federal Capital Territory (FCT), Abuja, Nigeria. Methods: The population of the study included all the small and medium scale printing press in Abuja which is 226 and the sample size of 68. A multiple regression model was formulated to estimate the effect of strategic intent (vision, mission, and objectives) on performance (growth) of small and medium scale printing press firms in Abuja. The study also adopted a control variable such as finance to have a better coefficient of determination. Findings: The study found that strategic intent had a positive and significant effect on the growth of small and medium scale printing press firms in Nigeria. The study also found that finance (collateral, access to finance, and insufficient finance) had a negative and insignificant effect on the growth of small and medium scale printing press firms in Nigeria. Implication: Small and medium printing press firms in Abuja, FCT should communicate their vision, mission statement, and objectives to their employees. The microfinance banks in collaboration with the central bank of Nigeria should minimize collateral conditions in obtaining microcredit from microfinance banks.

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Whole Facility Measurement for Quantifying Energy Saving in an Office Building, Malaysia

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.785.676 ◽

2015 ◽

Vol 785 ◽

pp. 676-681 ◽

Cited By ~ 2

Author(s):

Nor Shahida Razali ◽

Nofri Yenita Dahlan

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Energy Saving ◽

Linear Regression Model ◽

Energy Use ◽

Multiple Linear Regression Model ◽

Office Building ◽

Coefficient Of Determination ◽

Independent Variable

This paper presents the concept of International Performance Measurement and Verification Protocol (IPMVP) for determining energy saving at whole facility level for an office building in Malaysia. Regression analysis is used to develop baseline model from a set of baseline data which correlates baseline energy with appropriate independents variables, i.e. Cooling Degree Days (CDD) and Number of Working Days (NWD) in this paper. In determining energy savings, the baseline energy is adjusted to the same set condition of reporting period using energy cost avoidance approach. Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression for each independent variable. Results show that NWD has coefficient of determination, R2 higher than CDD which indicates that NWD has stronger correlation with the energy use than CDD in the building. Finding also shows that the R2 for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.

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Pengaruh PAD, Dana Perimbangan dan PMA terhadap Tingkat Kemajuan Daerah Kabupaten dan Kota di Provinsi Sulavesi Selatan

10.31227/osf.io/e7har ◽

2018 ◽

Author(s):

Muhammad Syukri

Keyword(s):

Regression Model ◽

Quantitative Data ◽

Applied Research ◽

Descriptive Analysis ◽

Multiple Linear Regression Model ◽

Multiple Regression Model ◽

Simultaneous Test ◽

South Sulawesi ◽

Direct Benefits ◽

Research Procedure

The purpose of this study is to determine the effect of locally-generated revenue (PAD), balancing funds and Foreign Direct Investment (FDI) to Progress Regions Level districts and cities in South Sulawesi Province either simultaneously or partially. Type of research used is applied research with quantitative data. The data used obtained from the Statistics Central Institution (BPS), which covers 24 districts and cities of South Sulawesi Province in 2016 in the form of thousands of rupiah. The research procedure is (1) Descriptive Analysis. (2) Establish multiple regression model. (3) Partial test and simultaneous test, and (4) determine the coefficient determination. Based on the simultaneous test of multiple linear regression model that PAD (X1), Balance Funds (X2) and FDI (X3) have significant effect to regional progress level (Y). And partial model testing, only PAD (X1) and fund balancing (X2) which significantly influence to regional progress level (Y). Meanwhile, FDI (X3) has no significant effect on regional progress level (Y). Therefore, the cultivation of FDI should be targeted and can provide direct benefits for the community in improving their welfare that will impact on improving regional progress. The suggestions needed in the development of further research that is required addition of other variables that can affect the level of regional progress.

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Extents of Predictors for Land Surface Temperature Using Multiple Regression Model

The Scientific World JOURNAL ◽

10.1155/2020/3958589 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

R. M. Yuvaraj

Keyword(s):

Surface Temperature ◽

Regression Model ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Areas ◽

Climate Models ◽

Multiple Linear Regression Model ◽

Multiple Regression Model ◽

Landsat 8 ◽

Study Region

Land surface temperature (LST) is a key factor in numerous areas such as climate change, land use/land cover in the urban areas, and heat balance and is also a significant participant in the creation of climate models. Landsat data has given numerous possibilities to understand the land processes by means of remote sensing. The present study has been performed to identify the LST of the study region using Landsat 8 OLI/TIRS satellite images for two time periods in order to compare the data. The study also attempted to identify and predict the role and importance of NDVI, NDBI, and the slope of the region on LST. The study concludes that the maximum and minimum temperatures of 40.44 C and 20.78 C were recorded during the November month whereas the maximum and minimum LST for month March has increased to 42.44 C and 24.57 C respectively. The result indicates that LST is inversely proportional to NDVI (−6.369) and slope (−0.077) whereas LST is directly proportional to NDBI (+14.74). Multiple linear regression model has been applied to calculate the extents of NDVI, NDBI, and slope on the LST. It concludes that the increase in vegetation and slope would result in slight decrease in temperature whereas the increase in built-up will result in a huge increase in temperature.

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Parameter Estimation of Bayesian Multiple Regression Model with Informative Inverse Gamma Prior Distribution: Application to Malaria Symptom Dataset

Asian Journal of Probability and Statistics ◽

10.9734/ajpas/2020/v7i130174 ◽

2020 ◽

pp. 71-86

Author(s):

Drinold Aluda Mbete

Keyword(s):

Parameter Estimation ◽

Regression Model ◽

Multiple Regression ◽

Prior Distribution ◽

Least Squares Method ◽

Average Distance ◽

Multiple Linear Regression Model ◽

Multiple Regression Model ◽

Inverse Gamma ◽

Highest Posterior Density

Objectives: The study aims to develop a Bayesian multiple regression model with informative inverse gamma prior and t the model to malaria symptom dataset.Place and Duration of Study: The study was carried out in Masinde Muliro University of Science and Technology (MMUST). The study used 300 malaria related symptom dataset obtained from Health service records of different patients (students) between the time period of 1st January, 2015 to 20th December, 2015.Methodology: Multiple linear regression model with Bayesian parameter estimation is used. The Normal prior distribution for θ parameter and inverse gamma prior distribution for the σ2 parameter is derived. Gibbs sampler and Metropolis Hasting algorithm is used with Markov Chain Monte Carlo (MCMC) method to produce an iteration of about 102,491 with Burn-in of 2500 and thinning of 10 that resulting to eective sample size of 90000.Results: The results shows that all the estimated posterior predictive p-values are between 0.05 and 0.95 indicating an adequate t for the individual observation of the data in the model. The results also reveals that the data values and the average distance between the data values and the mean tend to be close to each other and the estimated coeffcient of θ′s approximately 95%draws fall within each of the corresponding highest posterior density intervals. Conclusion: Though the Least Squares method is sucient for estimating the coeffcients of the regression parameters, the Bayesian estimates recorded comparatively very small standard errors making the Bayesian method more robust in analysing symptom dataset.

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Georeferencing skills and student profile. Results from a multivariate regression model

Proceedings of the ICA ◽

10.5194/ica-proc-4-115-2021 ◽

2021 ◽

Vol 4 ◽

pp. 1-6

Author(s):

Gian Pietro Zaccomer ◽

Maurizia Sigura

Keyword(s):

Regression Model ◽

Spatial Data ◽

New Technologies ◽

Multiple Regression Model ◽

Student Knowledge ◽

Student Profile ◽

On Line ◽

Profile Characteristics ◽

The University ◽

Academic Year

Abstract. The currently IT tools provides increased opportunities to organize professional and recreational activities by interactive maps easily accessible for users. An experiment was conducted to understand if the great potential offered by new technologies match with the ability to produce good quality spatial data by users. The goal was to assess whether their knowledge of GIS affected the quality of their mapping activity by online map-based survey with maps as part of on line questionnaire. The attention was paid to university students as target of potential users of these tools, considering different skills acquired during their studies: from theoretical courses of geography, to theoretical and practical courses (dedicated labs) on GIS. The experiment involved more than 200 students of the University of Udine during the academic year 2019–2020. In this framework, a further study was developed investigating factors playing a role in students’ ability to complete the proposed exercises. The analysis was based on a multiple regression model which assumes the number of exercises completed as a dependent variable, and the student profile characteristics (gender, type of student, knowledge of GIS, and other IT skills) as independent variables. The estimated models pointed out both students’ willingness and deep knowledge of GIS as main factors effected the students’ ability to complete the proposed exercises. Fewer effects were associated to their gender and residence.

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ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KINERJA KARYAWAN PT. BIO NUSANTARA TEKNOLOGI Jl. Lintas Utara Km. 19 Bengkulu Tengah

EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis ◽

10.37676/ekombis.v5i2.386 ◽

2017 ◽

Vol 5 (2) ◽

Author(s):

Merta Kusuma ◽

Tm Said

Keyword(s):

Regression Model ◽

Work Environment ◽

Significant Influence ◽

Multiple Regression Model ◽

Coefficient Of Determination ◽

Environment Variable ◽

Regression Test ◽

Training Variable ◽

Permanent Employees ◽

And Training

Merta Kusuma, Tm. Said:The purpose of this research is to know the influence of motivation, work environment, selection, and training toward employee’s performance in PT BIO Nusantara Teknologi. Population of this research was 550 permanent employees in PT BIO Nusantara Teknologi. Sampling techniques that used in this research were Nonprobability and Insidental sampling. To determined the sample used Solvin formula, so there was 84 people as samples. Data collection techniques were observation questionnaire, and interview. Data analysis techniques used by researcher were qualitative and quantitative analysis which consists of a multiple regression model and coefficient of determination. The result of this research used regression test Y= 6.003 + 0.230 X1 + 0.134 X2 + 0.188 X3 + 0.232 X4. While the result of determination R = 0.472 and R2 = 0223. T test significant level < 0,05 so, there was significant influence, if significant level > 0,05 so, there was not significant influence, the result of each t sig was 0,021 for motivation variable, 0,034 for work environment variable, 0,045 for selection, and 0,049 for training variable. The four variables were < 0,05 so there was significant influence. Next, F test, if F sig < 0,05 and (Fanova > F table) so, Ho was rejected ad H1 was accepted, the result of F sig=(0,000) < 0,05 and (5,673>2,337)so, the regression model can be used to predict employee’s performance variable or it can be said that the motivation variable (X1), work environment(X2), selection(X3), and training(X4) were influenced employee’s performance(Y) together in PT BIO Nusantara Teknologi.Key Words: Motivation, Work Environment, Selection, Training and Employee’s Performance.

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Recent Changes in Downward Longwave Radiation at King Sejong Station, Antarctica

Journal of Climate ◽

10.1175/2008jcli1876.1 ◽

2008 ◽

Vol 21 (22) ◽

pp. 5764-5776 ◽

Cited By ~ 17

Author(s):

Hi Ku Cho ◽

Jhoon Kim ◽

Yeonjin Jung ◽

Yun Gon Lee ◽

Bang Yong Lee

Keyword(s):

Time Series ◽

Regression Model ◽

Air Temperature ◽

Multiple Linear Regression Model ◽

Longwave Radiation ◽

Multiple Regression Model ◽

Specific Humidity ◽

Annual Data ◽

Mean Values ◽

Downward Longwave Radiation

Abstract Effects of cloud, air temperature, and specific humidity on downward longwave irradiance and their long-term variabilities are examined by analyzing the measurements made at the King Sejong Station in the Antarctic Peninsula during the period of 1996–2006. It has been shown that the downward longwave irradiance (DLR) is significantly correlated with three variables: air temperature, specific humidity, and cloudiness. Based on the relationship of the three variables with DLR, a multiple linear regression model has been developed in order to evaluate the relative contribution of each of the variables to the variation of DLR. The three variables together explained 75% of all the variance in daily mean DLR. The respective contribution from specific humidity and cloudiness to the variation of DLR was 46% and 23%; thus most of the DLR variability can be explained by the variations in the two variables. The annual mean of longwave cloud forcing shows 52 W m−2 with no remarkable seasonal cycle. It is also noted that the overcast cloud effect gives an increase by 65 W m−2 with respect to clear-sky flux throughout the year. It is suggested that the multiple regression model can be used to estimate the radiative forcings of variables influencing the DLR variability. A highly significant decrease in DLR with an average of −1.52 W m−2 yr−1 (−0.54% yr−1) is found in an analysis from the time series of the deseasonalized monthly mean values. Accordingly, the atmospheric flux emissivity, air temperature, and specific humidity have also decreased in their time series, while the cloudiness has increased insignificantly. Consequently, it may be concluded that the recent decrease in DLR is mainly attributed to the net cooling effect due to the decrease in air temperature and specific humidity, which overwhelm the slight warming effect in cloudiness. Analysis of mean monthly trends for individual months shows that, as for the annual data, the variations in DLR are mostly associated with those of air temperature, specific humidity, and cloudiness.

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The Effect of Distance Teaching on Academic Achievement in the Online Course of Introduction to Computers Science

Frontiers of Contemporary Education ◽

10.22158/fce.v1n2p23 ◽

2020 ◽

Vol 1 (2) ◽

pp. p23

Author(s):

Ying-Sheng Kuo

Keyword(s):

Regression Model ◽

Student Performance ◽

Online Teaching ◽

Multiple Linear Regression Model ◽

Multiple Regression Model ◽

Distance Teaching ◽

Final Test ◽

Final Exam ◽

Model Combining ◽

On Line

For distance teaching, if the teacher can obtain the predicted scores that students may obtain in the final exam at this time, the students whose predicted scores do not meet the standard can be found. Teachers can strengthen the teaching of this type of students in the teaching process, which will greatly improve the overall teaching efficiency of teachers. The target of this research is the students of the course of the “Introduction to Computers Science” at Open University of Kaohsiung. This course is a distance teaching based on online teaching, and the questionnaire and final test are also completed on line. A total of 95 students filled out the questionnaire online, accounting for 77.24% of the electives. This study aims to assess the measurement of students’ pre-learning computer experience, software operation ability, learning motivation, and computer attitude, and analyze their relationship with the performance of subsequent distance teaching on the course of the “Introduction to Computers Science”, and find a multiple regression model to predict student performance. It was found that a multiple linear regression model combining 7 independent variables was statistically significantly related to the final test scores.

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