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The aims of this study was to predict COVID-19 new cases using multiple linear regression model based on May to June 2020 data in Ethiopia. The COVID-19 cases data was collected from the Ethiopia Ministry of Health Organization Facebook page. Pearson’s correlation analysis and linear regression model were used in the study. And, the COVID-19 new cases was positively correlated with the number of days, daily laboratory tests, new cases of males, new cases of females, new cases from Addis Ababa city, and new cases from foreign natives. In the multiple linear regression model, COVID-19 new cases was significantly predicted by the number of days at 5%, the number of daily laboratory tests at 10%, and the number of new cases from Addis Ababa city at 1% levels of significance. Then, the researchers recommended that Ethiopian Government, Ministry of Health, and Addis Ababa city administrative should give more awareness and protections for societies, and they should open again more COVID-19 laboratory testing centers. And, this study will help the government and doctors in preparing their plans for the next times.

We introduce a multiple linear regression model methodology for selecting best subsets of independent variables subject to active budget constraints. Computational studies on multiple data sets to ...

The ridge regression estimator (RRE) is a widely used estimation method for the multiple linear regression model when the explanatory variables are correlated. The situation becomes problematic for...

We present an innovative activity that uses data about LEGO sets to help students self-discover multiple linear regressions. Students are guided to predict the price of a LEGO set posted on Amazon.com (Amazon price) using LEGO characteristics such as the number of pieces, the theme (i.e., product line), and the general size of the pieces. By starting with graphical displays and simple linear regression, students are able to develop additive multiple linear regression models as well as interaction models to accomplish the task. We provide examples of student responses to the activity and suggestions for teachers based on our experiences. Supplementary materials for this article are available online.

A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log PN) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as “TFE-MLR”, presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors

Air quality index (AQI) is an indicator usually issued on a daily basis to inform the public how good or bad air quality recently is or how it will become over the next few days, which is of utmost importance in our life. To provide a more practicable way for AQI prediction, so that residents can clear about air conditions and make further plans, five imperative meteorological indicators are elaborately selected. Accordingly, taking these indicators as independent variables, a fuzzy multiple linear regression model with Gaussian fuzzy coefficients is proposed and reformulated, based on the linearity of Gaussian fuzzy numbers and Tanaka’s minimum fuzziness criterion. Subsequently, historical data in Shanghai from March 2016 to February 2018 are extracted from the government database and divided into two parts, where the first half is statistically analyzed and used for formulating four seasonal fuzzy linear regression models in views of the special climate environment of Shanghai, and the second half is used for prediction to validate the performance of the proposed model. Furthermore, considering that there is beyond dispute that triangular fuzzy number is more prevalent and crucial in the field of fuzzy studies for years, plenty of comparisons between the models based on the two types of fuzzy numbers are carried out by means of the three measures including the membership degree, the fuzziness and the credibility. The results demonstrate the powerful effectiveness and efficiency of the fuzzy linear regression models for AQI prediction, and the superiority of Gaussian fuzzy numbers over triangular fuzzy numbers in presenting the relationships between the meteorological factors and AQI.

This study aims to determine the effect of income, belief, and religiosity on the interest in paying zakat income (study of residents of Makamhaji Kartasura). This study uses a quantitative approach to the population of Makamhaji Kartasura Village residents. The sampling technique uses probability sampling to analyze the data using a multiple linear regression model. The results showed that income had no significant effect on the interest in paying zakat income. In contrast, trust and religiosity have a significant effect on the interest in paying zakat income. Meanwhile, simultaneous income, belief, and religiosity have influenced the residents of Makamhaji Kartasura Village to pay zakat income. This research model contributes to explaining the role of trust and religiosity on increasing zakat income.

Balsalobre-Fernández, C, Muñoz-López, M, Marchante, D, and García-Ramos, A. Repetitions in reserve and rate of perceived exertion increase the prediction capabilities of the load-velocity relationship. J Strength Cond Res 35(3): 724-730, 2021-This study aimed to (a) analyze the relationships between relative load (i.e., %1 repetition maximum; 1RM) and movement velocity, repetitions in reserve (RIR) and rate of perceived exertion (RPE) in competitive powerlifters and (b) examine whether a multiple linear regression model with the movement velocity, RIR, and RPE as predictor variables could improve the goodness of fit of the load-velocity relationship. Ten competitive powerlifters performed an incremental loading test (from 50 to 100% 1RM) on the full-squat, hip-thrust, and bench press exercises. Barbell velocity was measured using a linear position transducer, while RIR and RPE were registered immediately after each set. Velocity (r2: 0.747-0.887), RIR (r2: 0.857-0.928), and RPE (r2: 0.908-0.933) were moderately to highly related to relative load. A higher amount of variance of the relative load was explained when the RIR and RPE were added to velocity in a multiple regression model in comparison with the load-velocity relationship (r2: 0.924-0.947). Moreover, it was observed that, in all cases, individual load-velocity, load-RIR, and load-RPE relationships had higher r2 scores than the generalized load-velocity relationship. Incorporating the RIR and RPE as predictors of the relative load along with movement velocity into a multiple linear regression was shown to provide better estimations of the %1RM than using a linear load-velocity relationship.

This article proposes robust versions of Mallows’ Cp criterion to select the best variables for a multiple linear regression model with a small number of variables in the presence of outliers. The ...

To address the drawbacks of the traditional Parker test in multivariate linear models: the process is cumbersome and computationally intensive, we propose a new heteroscedasticity test. A new heteroskedasticity test is proposed using the fitted values of the samples as new explanatory variables, reconstructing the regression model, and giving a new heteroskedasticity test based on the significance test of the coefficients, it is also compared with the existing Parker test which is improved using the principal component idea. Numerical simulations and empirical analyses show that the improved Parker test with the fitted values of the samples proposed in this paper is superior.

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Purpose of this observation to find out effect of economic growth on poverty levels in the period 2010-2019. The variable used is the percentage of poverty level as the dependent variable and the Gross Regional Domestic Product (GRDP) and the Open Unemployment Rate as independent variables. The data used in this study are secondary data obtained from BPS North Sumatra. The analytical method used is a multiple linear regression model or Ordinary Ordinary Least Square (OLS). In processing data, the authors use the help of Eviews 8.1 software. Based on the estimation results it is found that there is no correlation between the rate of economic growth and the poverty rate in North Sumatra and the variable Open Unemployment Rate has a positive and significant effect on poverty levels in North Sumatera Province.

To circumvent the problem of multicollinearity, biased estimation method has been suggested to improve the precision of estimators. In this paper, we study types of biased estimators that can help to reduce the effect of multicollinearity on estimation. A simulation study is carried out to study the relative effectiveness of certain types of biased estimators in comparison to some proposed estimated ridge parameter (k) that have been shown in the literature . Moreover, a real data set has been considered to support the simulation results based on the estimated mean square error criterion.

Background Evaluation of 24-hour collection accuracy is based on urinary creatinine excretion (UCr), usually using wide ranges indexed by weight. Equations that predict the expected UCr are also available. Aim To generate an equation for estimating UCr in Chilean population and evaluate its performance in comparison to existing formulas. Material and methods A total of 464 24-hour urine collections from outpatients aged between 15 and 88 years old were used. Ninety percent of collections (n = 418) were randomly extracted to assess the association between absolute UCr values with sex, age, height and weight of participants. A formula was created to estimate the 24-hour UCr using a multiple linear regression model. In the remaining 10% of urine collections (n = 46), the performance of this formula and others reported in the literature were tested. Results Age, sex and weight were significantly associated with 24-hour UCr values. The new equation was able to predict UCr values with a similar accuracy than CKD-EPI and Walser equations and outperformed other equations. Conclusions Our equation developed with Chilean values predicts 24-hour UCr values accurately.

We propose a new approach to simultaneously test the assumptions of independence and goodness-of-fit for a multiple linear regression model Y=β′X+W, say H0, vs. H1: H0 is false. Our approach is bas...

In this paper, we introduce the median-of-means approach for estimating unknown parameters in multiple linear regression model and the regression model with autocorrelated error. The proposed estim...

Measles is one of the infectious caused by virus. The disease is easily transmitted and has become one of the main causes of child mortality especially toddlers. In 2016, Jakarta experienced the highest measles case in the last ten years and found the difference in the number of measles cases in each sub-district of Jakarta. This can be caused by the existence of effect of spatial location i.e. spatial heterogeneity. Geographically weighted regression (GWR) is a method that can be applied to address the presence of spatial heterogeneity in the process of developing the model. In this study, the weighting function used was the Gaussian kernel. The modelling process generated 42 local models at sub-district level. Explanatory variables that influence the incidence rate of measles in toddlers (Y) significantly are the percentage of immunization coverage measles (X1), the total annual rainfall (X4), and the percentage of the number of toddlers (X5). In this study, the GWR model is better than multiple linear regression model which were indicated by higher value of and smaller value of AIC.

This study aims to relate information about the waiting times of ride-sourcing services, with specific reference to Uber, using socioeconomic variables from São Paulo, Brazil. The intention is to explore the possibility of using this measure as an accessibility proxy. A database was created with the mean waiting time data per district, which was aggregated to a set of socioeconomic and transport infrastructure variables. From this database, a multiple linear regression model was built. In addition, the stepwise method selected the most significant variables. Moran's I test confirmed the spatial distribution pattern of the measures, motivating the use of a spatial autoregressive model. The results indicate that physical variables, such as area and population density, are important to explain this relation. However, the mileage of district bus lines and the non-white resident rate were also significant. Besides, the spatial component indicates a possible relation to accessibility.

In this study, a new hybrid model, bootstrap multiple linear regression (BMLR) is suggested to investigate the potential of bootstrap resampling technique for daily reservoir inflow prediction. The proposed model compares with three other models: Multiple linear regression (MLR), wavelet multiple linear regression (WMLR) and wavelet bootstrap multiple linear regression (WBMLR). River stage data of monsoon season (1st July 2010 to 30 September 2010) from three gauging stations of Chenab river basin are used. In wavelet transformation, input vectors are decomposed using discrete wavelet transformation (DWT) into discrete wavelet components (DWCs). Then suitable DWCs are used to provide input to MLR model to develop WMLR model. Bootstrap technique coupled with MLR model to build up BMLR model. While WBMLR model is the conjunction of suitable DWCs and bootstrap technique to MLR model. Performance indices namely root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient of efficiency (NSC), and persistence index (CP) are used in study to evaluate the performance of model. Results showed that hybrid model BMLR produce significantly better results on performance indices than other models MLR, WMLR and WBMLR.

In this article, we develop finite sample inference based on multiply imputed synthetic data generated under the multiple linear regression model. We consider two methods of generating the synthetic data, namely posterior predictive sampling and plug-in sampling. Simulation results are presented to confirm that the proposed methodology performs as the theory predicts and to numerically compare the proposed methodology with the current state-of-the-art procedures for analysing multiply imputed partially synthetic data. AMS 2000 subject classification: 62F10, 62F25, 62J05

This research is to test and analyze the effect of environmental accounting disclosures and environmental performance on firm value in the LQ-45 Index company listed on the Indonesia Stock Exchange for the period 2014-2017. This research is quantitative research. The research sample was 15 companies included in the LQ-45 index, the year of observation from 2014-2017 which had been carried out using a purposive sampling technique. The analysis technique has been carried out using multiple linear regression. The results of the study indicate that the disclosure variables of environmental accounting and environmental performance have a significant effect on firm value. The multiple linear regression model in this study has a value of R2 as 21.5%, which means that the value of the company can be explained using the variable value of disclosure of environmental accounting and environmental performance, where the remaining 79.5% is influenced by other variables outside the model which has been studied.

Comprehensive multivariate statistical techniques (i.e. analysis of variance, correlation analysis, principal component analysis and factor analysis, and multiple linear regression model) w...

In the study of realistic problems, the change of the dependent variable is often affected by several important factors. At the same time, it is necessary to use two or more influencing factors as independent variables to explain the change of the dependent variable. This paper combines the mathematical model of multiple linear regression, and uses the least squares method to perform unbiased estimation of the regression coefficients. The theoretical equations and properties of the unbiased estimation are obtained, and the unbiasedness of the least square estimator of the multiple linear regression model is proved. The advantages of multiple linear regression models are discussed and analyzed.

Purpose This study aims to propose a fuzzy linear regression (FLR) model to deal with the vagueness or fuzziness of the underlying relationship between silk cocoon and yarn quality. Design/methodology/approach Shell ratio percentage, defective cocoon percentage and cocoon volume are considered as significant independent variables to predict the quality of silk cocoons. Input and output parameters of the FLR model are considered as non-fuzzy, but the underlying relationship between the variables is assumed to be fuzzy. Findings The fuzzy regression model shows its superiority against conventional multiple linear regression model for estimation of silk cocoon characteristics. It is inferred that the fuzziness in underlying relationship between the parameters can be handled efficiently by FLR model. Originality/value A rigorous experimental work has been carried out on 40 lots of mulberry silk cocoons to generate real-world data set to characterize silk cocoons’ quality in a fuzzy environment.

Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.

Introduction Vascular aging is an independent risk factor for cardiovascular diseases, which has always been a research hotspot. This study aims to establish a Multiple Linear Regression (MLR) model using radial artery pulse wave characteristic parameters to assess vascular aging. Methods Data from 111 males and 117 females were used to propose a new method for extracting pulse wave characteristic parameters called, Equal Pressure Pulse Transit Time (EP-PTT). Firstly, 10 EP-PTTs were extracted from pulse waves which were used to describe the shape characteristics of the pulse signal. Secondly, 10 EP-PTTs were fed into MLR model, which were used to optimize the model. Lastly, the predicted age of all subjects was calculated by the optimal model. We compared the correlation coefficients of predicted age with Pulse Transit Time (PTT) and Augmentation Index (AIx) with the correlation coefficients of chronological age with PTT and AIx. Results 9 EP-PTTs were relevant to predicting age in men and all EP-PTTs were age-related in women (P Conclusions The predicted age can better reflect vascular aging than chronological age. This proved the validity of the proposed method for assessing vascular aging.

The results of the study devoted to the evaluation of reliability of the multiple linear regression model for safflower seed yields prediction were presented. Regression model reliability was assessed by the direct comparison of the modeled yields values with the true ones, which were obtained in the field trials with safflower during 2010-2012. The trials were dedicated to study of the effect of various cultivation technology treatments on the safflower seed productivity at the irrigated lands of the South of Ukraine. The agrotechnological factors, which were investigated in the experiments, include: A – soil tillage: A1 – disking at the depth of 14-16 cm; A2 – plowing at the depth of 20-22 cm; B – time of sowing: B1 – 3rd decade of March; B2 – 2nd decade of April; B3 – 3rd decade of April; C – inter-row spacing: C1 – 30 cm; C2- 45 cm; C3 – 60 cm; D – mineral fertilizers dose: D1 – N0P0; D2 – N30P30; D3 – N60P60; D4 – N90P90. Regression analysis allowed us to create a model of the crop productivity, which looks as follows: Y = –1.3639 + 0.0213Х1 + 0.0017Х2 – 0.0121Х3 + 0.0045Х4, where: Y is safflower seed yields, t ha-1; Х1 – soil tillage depth, cm; Х2 – sum of the positive temperatures above 10°С; Х3 – inter-row spacing, cm; Х4 – mineral fertilizers dose, kg ha-1. A direct comparison of the modeled safflower seed yield values with the true ones showed a very slight inaccuracy of the developed model. The maximum amplitude of the residuals averaged to 0.27 t ha-1. Therefore, we conclude that multiple linear regression analysis can be successfully used in purposes of agricultural modeling.

From fitting (training) a multiple linear regression model with p basis function predictors (e.g., polynomial, trigonometric) we study a type of confidence band covering an entire set of the respon...

Tiller angle is an important influencing factor in rice plant architecture that affects planting density and yield per unit area. Molecular tools to predict tiller angle contribute to breeding programs, which aim at optimizing rice plant architecture. In this study, several single-nucleotide polymorphism (SNP) markers related to tiller angle were developed and used with a model population to define a linear regression model for the prediction of tiller angle in rice. The resulting linear regression model, consisting of eight SNP markers as independent variables, was assessed using an independent test population. Overall, the regression model achieved an adjusted R2 of 0.51 and exhibited consistent predictive accuracy with an R2 of 0.61. Three of the eight independent variables, namely, PIN2-1, LIC1-1, and TAC1, contributed substantially to the linear regression model. These three major effect markers were also major determinants of tiller angle in the independent test population. Allelic combinations of the three major effect markers modulated tiller angle in the range of 5.6&ndash, 19&deg, The DNA markers and linear regression model developed in this study will facilitate rice breeding programs for improving plant architecture.

Background The development of the minimum clinical important difference (MCID) can make it easier for researchers or doctors to judge the significance of research results and the effect of intervention measures, and improve the evaluation system of efficacy. This paper is aimed to calculate the MCID based on anchor and to develop MCID for esophageal cancer scale (QLICP-ES). Methods The item Q29 (How do you evaluate your overall health in the past week with 7 grades answers from 1 very poor to 7 excellent)of EORTC QLQ-C30 was used as the subjective anchor to calculate the score difference between each domain at discharge and admission. MCID was established according to two standards, "one grade difference"(A) and "at least one grade difference"(B), and developed by three methods: anchor-based method, ROC curve method and multiple linear regression models. In terms of anchor-based method, the mean of the absolute value of the difference before and after treatments is MCID. The point with the best sensitivity and specificity-Yorden index at the ROC curve is MCID for ROC curve method. In contrast, the predicted mean value based on a multiple linear regression model and the parameters of each factor is MCID. Results Most of the correlation coefficients of Q29 and various domains of the QLICP-ES were higher than 0.30. The rank of MCID values determined by different methods and standards were as follows: standard B > standard A, anchor-based method > ROC curve method > multiple linear regression models. The recommended MCID values of physical domain, psychological domain, social domain, common symptom and side-effects domain, the specific domain and the overall of the QLICP-ES were 7.8, 9.7, 4.7, 3.6, 4.3, 2.3 and 2.9, respectively. Conclusion Different methods have their own advantages and disadvantages, and also different definitions and standards can be adopted according to research purposes and methods. A lot of different MCID values were presented in this paper so that it can be easy and convenient to select by users.

We introduce a new family of Wald-type tests, based on minimum Rényi pseudodistance estimators, for testing general linear hypotheses and the variance of the residuals in the multiple regression model. The classical Wald test, based on the maximum likelihood estimator, can be seen as a particular case inside our family. Theoretical results, supported by an extensive simulation study, point out how some tests included in this family have a better behaviour, in the sense of robustness, than the Wald test. Finally, we provide a data-driven procedure for the choice of the optimal test given any data set.

Background One-handed lifting commonly occurs in the industry. Specific guidelines of proper heights during one-handed lifting could be valuable information to design or to assess the risk of work environment. Objective The objective of this study was to determine the maximum acceptable height and comfortable height during one-handed vertical lifting by gender, participant height, hand, and object weight. Methods Based on the psychophysical method, 72 males and 50 females, divided into four different height groups, determined their maximum acceptable and comfortable heights by each hand (left and right) and various object weights (1 kg, 3 kg, 5 kg, and 8 kg). Results Males revealed significantly greater maximum acceptable heights (males: 157 cm; females: 135 cm) and higher comfortable heights (males: 104 cm; females: 96 cm) compared to females. The participants' heights, which hand was used to lift, and the object weight were significant factors in determining the maximum acceptable height for both males and females. The multiple linear regression model of the maximum acceptable height showed more robust predictive power (R2 = 0.55) compared to the comfortable height (R2 = 0.20) as a function of gender, participant height, hand, and object weight. Conclusions Results suggest that gender, participant height, hand, and object weight are important variables to consider when determining the proper surface height of one-handed vertical lifting. Using the robust predictive model, an appropriate maximum acceptable height could be suggested based on the material handler's anthropometric information and object weight.

This study seeks to determine the influence of process variables: consumption percentage in the mixture, pasilla percentage in the mixture, storage time, humidity percentage in the product for consumption, humidity percentage in the pasilla, humidity percentage in roasted coffee, average humidity in finished product, average color in roasted coffee, and average color in finished product, for the shrinkage of packed coffee in a coffee processing plant of Arabica type. Using a multiple linear regression model, the study stated that the variables of humidity percentage of roasted coffee and color of roasted coffee have a statistically significant relationship with a confidence of 95% (p-value < 0.05). It was concluded that these variables explain 99.95% of the variability in the shrinkage, and the relation of the shrinkage with the humidity percentage is inversely proportional, but the relation of this variable with the color of roasted coffee is directly proportional. The tests applied to the model wastes proved that the model is suitable for predicting the shrinkage in the process.

Traffic accidents are one of the principal causes of the loss of abundant human and natural resources and destruction of infrastructure in all countries, especially developing one. This research aims to measure the relationship between the range of variables related to the types of traffic accidents (crash, rollover, run over, and others), with the most common causes that lead to these accidents (road, vehicle, driver, and pedestrian) in Iraq. This relationship based on the hypothesis that the variance of these accidents is the result of the variation of their causes, this relationship was tested by using the multiple regression analysis model for tow periods of study time (1979–2000) and (2010–2017). This test demonstrated a strong correlation between traffic accidents and the causes of accidents. The rate of correlation of variables in the general estimated models between 95% and 100%, which mean there is a strong positive correlation between traffic accidents and road users, driver and pedestrian (human factors), and these overshadowed the relationship between traffic accidents and their causes, roads, and vehicles (physical factors), which is explained by the significant deterioration in the traffic regulations and laws.

Performance is a tool to evaluate the success of the company in running its business. This study aims to examine the performance factors of Foreign Exchange Banks Listed on the Indonesia Stock Exchange. Performance factors in this study were measured by the ratio of CAR, NPL, LDR, BOPO, GWM while Performance measured by ROA. This study uses quantitative analysis with multiple linear regression model to test the influence of these factors on the performance of foreign exchange banks simultaneously or partially. The results showed that there was a significant and simultaneous influence between CAR, NPL, LDR, BOPO, GWM on Foreign Exchange Bank ROA. Coefficient of determination simultaneously CAR, NPL, LDR, BOPO, GWM affect ROA of 85,6% and the rest influenced by other factors. Partially CAR, NPL and GWM have no significant effect on ROA of Foreign Exchange Bank. while the LDR and BOPO have a negative and significant effect on the ROA of Foreign Exchange Bank.

This study aims to examine the influence of school organizational culture, communication and teacher competence on teacher performance either partially or simultaneously. The sample in this study were teachers of SMK YP Satrya Budi Karang Rejo, Simalungun Regency, totaling 45 people. Data analysis techniques in this study using quantitative techniques. The data in this study are primary data. Data was collected using a questionnaire. The analytical method used is the Multiple Linear Regression model. To test the hypothesis simultaneously the F test is used and partially the t test is used. The results of the tests carried out with SPSS obtained multiple regression equations: Y = 1.822 + 0.179 X1 + 0.207 X2 + 0.237 X3 which means a constant value of 1.822 which indicates that if the school's organizational culture, communication and teacher competence are assumed to be zero, then the value of performance teacher is 1,822. If the independent variable increases by 1%, the teacher's performance will increase. The results of the t-test hypothesis of the research variable are the Organizational Culture variable obtained tcount 2.143 with a significant probability of 0.038 less than the level of confidence (level of significant) = 0.05, Communication variable obtained tcount 2.071 with significant probability 0.045 less than the level of confidence (level of significant) = 0.05, the variable Teacher Competence obtained tcount 2.207 with a significant probability of 0.033 less than the level of confidence (level of significant) = 0.05. So the decision taken is to reject H0 and accept Ha, meaning that school organizational culture, communication and teacher competence have a positive and significant effect on teacher performance at YP Satrya Budi Karang Rejo Vocational School, Simalungun Regency.

This research aimed to analyze the effect of marketing public relations and service quality on corporate image of PT. Delta Abadi Sentosa in Banjarmasin. This research employed explanatory research type. The population of this study was the customers of PT. Delta Abadi Sentosa Banjarmasin. The number of samples was 100 respondents taken by using purposive sampling. The data were collected through observation, questionnaires, and documentation. The instrument test was done by using validity and reliability tests. Moreover, multiple linear regression model was employed to analyze the data. The results of the analysis showed that simultaneously marketing public relations and service quality affected corporate image. In addition, marketing public relations had a positive and significant influence on the corporate image of PT. Delta Abadi Sentosa Banjarmasin. Moreover, service quality also had a positive and significant influence on corporate image of PT. Delta Abadi Sentosa Banjarmasin . Keywords: Marketing Public Relations, Service Quality, Corporate Image

In order to explore the impact of the live video host’s face value on the consumption of viewers watching the live video, the author formed a 28-person research team from 20:00 pm to 22:00 pm every evening to conduct a 336-hour follow-up survey. A total of 488 valid online questionnaires and 56 personal in-depth interview records were collected. The author classifies the big data obtained in the survey, and then establishes a multiple linear regression analysis model, using a stepwise regression analysis method. At the same time, the Eviews10.0 is used to objectively analyze and accurately verify the classified big data. The conclusion is that the beautification degree of the live video host’s face, eyes and ears is positively correlated with the live video sales revenue, while the live video host’s mouth shape is negatively correlated with the live video sales revenue.

The multiple linear regression model contains more than one predictor variable and it shows the relationship among multiple variables. In the existing research field of rural-urban income disparity, the method of multiple regression analysis is mainly employed. But the linear relationship among variables is estimated mainly depending on principal component analysis. Principal component analysis is used to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The principal component analysis is widely used for feature extraction to reveal the most main factors from the multiple aspects. A multiply linear regression model integrating principal components analysis is proposed to address on the income gap between the city and country. The influential factors are given and the analysis results are discussed in this paper. The experimental results on income data from 1990 to 2013 show that the proposed method is effective in predicting the income ratio and analyzing the influential factors.