Isolated Area Load Forecasting using Linear Regression Analysis: Practical Approach

This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system.

Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).

Risk factor analysis of post-ERCP cholangitis: A single-center experience

Background: Endoscopic retrograde cholangiopancreatography(ERCP) may have complications. Our study aimed to investigate the risk factors and prevention of post-ERCP cholangitis.Methods: We retrospectively analyzed 4234 cases undergone ERCP in the Affiliated Drum Tower Hospital of Nanjing University Medical School from January 2008 to December 2013. Patient-related factors and procedure-related factors were analyzed to find the risk factors of post-ERCP cholangitis. The time point of post-ERCP cholangitis was also analyzed. Univariate and multivariate analyses were performed to define the independent risk factors of post-ERCP cholangitis.Results: The success rate of ERCP was 96.8%(4099/4234). The overall complication rate was 9.4%(399/4234). Post-ERCP cholangitis occurred in 102 cases(2.4%, 102/4234). The most dangerous time of post-ERCP cholangitis was from 24 h–48 h after ERCP(45.1%, 46/102). Univariate analysis revealed that age, hypertension, diabetes, previous ERCP history, biliary stent insertion, pancreatography, endoscopic sphincterotomy, balloon dilation and hilar obstruction were risk factors of post-ERCP cholangitis(P < 0.05). Multivariate analysis indicated that age, previous ERCP history and hilar obstruction were independent risk factors(P < 0.05). While endoscopic stone extraction was the potential protective factor.Conclusions: Many risk factors are involved in post-ERCP cholangitis. Among them, old age, previous ERCP history and hilar obstruction were independently related to this post-ERCP complication.

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.

A Vehicle Traveling Time Prediction Method Based on Grey Theory and Linear Regression Analysis

Vehicle traveling time prediction is an important part of the research of intelligent transportation system. By now, there have been various kinds of methods for vehicle traveling time prediction. But few consider both aspects of time and space. In this paper, a vehicle traveling time prediction method based on grey theory （GT） and linear regression analysis （LRA） is presented. In aspects of time, we use the history data sequence of bus speed on a certain road to predict the future bus speed on that road by GT. And in aspects of space, we calculate the traffic affecting factors between various roads by LRA. Using these factors we can predict the vehicle＇s speed at the lower road if the vehicle＇s speed at the current road is known. Finally we use time factor and space factor as the weighting factors of the two results predicted by GT and LRA respectively to find the final result, thus calculating the vehicle＇s traveling time. The method also considers such factors as dwell time, thus making the prediction more accurate.

Finite Element Analysis and Linear Regression of Maximum Temperature for Inner Wall of Chimney Foundation

The uniform design method was adopted and the twenty-four groups of different geometric and physical pa-rameters were chosen. The finite element model was built. Comparisons between the simulation results and the test re-sults prove that the simulation results are correct. The distribution of the temperature field of the chimney foundationwas analyzed. The multivariate linear regression of the hightest tomperature was performed on the inner wall of thechimney foundation by the numerical calculated results. The fitting property of the highest temperature with six influ-ence factors was obtained. A simple method for the calculation of the temperature field of the chimney foundation wasprovided.

Multiple regression analysis of risk factors related to radiation pneumonitis

BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression analysis on the influencing factors of radiation pneumonitis.METHODS Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or not.Among them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control group.General characteristics,and radiation and imaging examination data of the two groups were collected and compared.Due to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other factors.RESULTS The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation pneumonitis.CONCLUSION We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy parameters as risk factors for radiation pneumonitis.Comprehensive evaluation and examination should be carried out before radiotherapy to effectively prevent radiation pneumonitis.

COX MULTIVARIATE REGRESSION ANALYSIS OF RECURRENCE FACTORS FOR COLONIC CARCINOMA

Objective: To determine the independent prognostic factors in the recurrence of colonic carcinoma after curative resection. Methods: Two hundred and one patients undergoing curative resections for colonic carcinoma were investigated by univariate and Cox multivariate regression analyses. Ten factors contributed to the rate were analyzed. Results: Dukes stages, obstruction, postoperative chemotherapy as well as the growth manner of the tumor were significantly associated with the recurrence rate of colonic carcinoma (P<0.05) by univariate analysis, while Dukes stages, obstruction, and postoperative chemotherapy were significant factors by the multivariate analysis. Conclusion: Dukes stages, obstruction, and postoperative chemotherapy are independent prognostic factors in the recurrence of colonic carcinoma.

Predicting urbanization level by main element analysis and multiple linear regression---taking Xiantao district in Hubei Province as an example

In this paper we firstly select main factors relating to urbanization level of Xiantao District in Hubei Province by main element, then, make model of urbanization level by analysis of multiple liner regression, and lastly predict its urbanization level

Logistic Regression Analysis the Risk Factors of Peripherally Inserted Central Catheter Related Blood Stream Infection of Tumor Patients

Objective: Our object is to study risk factors of tumor patients’ PICC catheter-related blood stream infection. Method: a retrospective analysis of data of 586 PICC catheterized patients was implemented, a univariate analysis of general data and catheterizing data of tumor patients was then carried out, and data of single factors with statistical significance were incorporated into multi-factor Logistic regression model for analysis. Results: PICC catheter-related blood stream infection occurred to 16 patients, and occurrence rate was 2.73%. Multi-factor Logistic regression analysis results showed that number of puncturing times, positioning method and maintenance frequency were risk factors for tumor patients’ peripherally inserted central catheter catheter-related blood stream infection, and odds risk values were respectively 8.762, 9.253 and 10.324. Conclusion: for tumor patients implanted with peripherally inserted central catheters, using ECG positioning during strict sterile operation and catheterizing process to avoid repeated puncturing and increasing maintenance frequency could effectively reduce occurrence of PICC catheter-related blood stream infection.

Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model

This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.

Logistic Regression Analysis of Influencing Factors on Serum ALT and HCV RNA Changes in Patients with Chronic Hepatitis C

Objective This study was undertaken to investigate the influencing factors on serum ALT level and hepatitis C virus(HCV)RNA titer in chronic hepatitis C(CHC)patients.Methods All patients enrolled into this study were anti-HCV positive.Retrospective tracing method was applied to detect serum ALT level and HCV RNA titer and to collect general information of the patients such as genders,age groups,interferon medication history,infection pathways,height and weight.Then the multi-factor analysis was adopted with the application of binominal logistic regression mode.Results The abnormal rate of ALT level was positively correlated to HCV RNA and gender while negatively correlated to interferon medication history and age group,with Wald value of the 4 factors as 39.604,11.823,18.991 and 7.389,respectively.The positive rate of HCV RNA was negatively correlated to interferon medication history and gender while positively correlated to ALT level,with corresponding Wald value of the 3 factors as81.394,7.618 and 27.562,respectively.Conclusions The normal ALT level in HCV infected patients was associated with viral load,age,gender and interferon medication history,while the normal rate of HCV RNA titer was closely associated with gender,interferon medication history and ALT level.

Population Quantity Variations of Oriental Fruit Fly (Bactrocera dorsalis Hendel) on the Basis of Stepwise Regression Analysis

[Objective] The research aimed to study the significant influence factors of the population variations of oriental fruit fly. [Method] Using stepwise regression analysis, the population variations law of oriental fruit fly in Jianshui County of Yunnan province and the meteorological factors that caused its occurrence were analyzed. And the regression model was built. Finally, the regression model was tested on the basis of the data in Jianshui County of Yunnan Province during 2004-2006.[Result] The main meteorological factors that influenced the occurrence of oriental fruit fly were relative humidity, the lowest monthly temperature and rainfall. [Conclusion] This study will provide certain reference for the prediction researches on the time, quantity and occurrence peak of oriental fruit fly.

Dividend policy determinants of Indian FMCG sector： A factorial analysis

Profitability has always been considered as a primary indicator of dividend payout by a company. There are factors other than profitability namely cash flows, debt equity ratio, retained earnings, sales growth, share prices of a company, capital expenditure and beta etc. that also affect dividend decisions of an organization. Existing literature suggests that dividend payout is positively related to profits, cash flows while CAPEX （capital expenditure） retained earnings, sales growth, share prices, beta, interest paid and debt equity ratio have inverse relationship. A set of 21 key variables have been identified that affect the dividend payout of a firm. Researchers in the past have used several proxies to represent these determinants. Authors have tried to find out which proxy variable is most relevant in the present scenario. The paper attempts to give a focused overview of the important dividend theories and empirically analyze the determinants of dividend behavior of Indian FMCG （Fast moving consumer goods） sector. The relationship between key variables has been explored with the aid of statistical techniques of factor analysis. Thus, the main theme of this study is to examine the various factors that influence the dividend policy decisions of FMCG firms in India.

Sociodemographic characteristics and risk factor analysis of Demodex infestation(Acari:Demodicidae)

To identify sociodemographic characteristics and risk factor of Demodex infestation,756 students aged 13-22 years in Xi'an,China were sampled for the school-based cross-sectional study.Demodex was examined using the cellophane tape method(CTP).The results showed that the total detection rate of Demodex was 67.6%.Logistic regression analysis revealed that five variables(gender,residence,sharing sanitary ware,frequency of face-wash per day,and use of facial cleanser) were found to be uncorrelated with Demodex infestation,whereas three variables(age,skin type,and skin disease) were found to be independent correlates.Students aged over 18 years had 22.1 times higher odds of Demodex infestation compared to those under 16 years and students aged 16-18 years also had 2.1 times higher odds compared to those aged 13-15 years.Odds of having a Demodex infestation for oily or mixed skin were 2.1 times those for dry or neutral skin.Students with a facial skin disease had 3.0 times higher odds of being infested with Demodex compared to those without.The inception rate of students with facial dermatoses increased in parallel with increasing mite count.The inception rates were 21.3%,40.7%,59.2%,and 67.7% in the negative,mild,moderate,and severe infestation groups,respectively(χ2=60.6,P<0.001).Specifically,the amount of infested mites and inception rate of acne vulgaris were positively correlated(R2=0.57,moderate infestation odds ratio(OR)=7.1,severe infestation OR=10.3).It was concluded that Demodex prevalence increases with age,and Demodex presents in nearly all adult human.Sebaceous hyperplasia with oily or mixed skin seems to favour Demodex proliferation.Demodex infestation could be associated with acne vulgaris.The CTP is a good sampling method for studies of Demodex prevalence.

Analysis of environmental factors about cerebral stroke

In this paper, the disease of cerebral stroke is studied mainly. According to the basic information of the patient, Linear fitting method is applied to calculate between the number of sick and age by using MATLAB software. According to data of pressure, temperature and humidity, nondimensionalizing by normalization method, linear regressions are analysed between the number of the patient and pressure, temperature, humidity. According to the important feature and the key indicators of cerebral stroke, high-risk groups are proposed to the early warning and intervene.

AN ANALYSIS ON LOCATION FACTORS AFFECTING THE SPATIAL DISTRIBUTION OF SHANGHAI LAND VALUES

With a technique of GIS(Geographical Information System) and a method of multiple linear regression analysis, the spatial distribution of Shanghai land values and its relationship with some location factors are examined in this paper. The database of land values consists of 166 land parcels leased during 1993-1994. The land value map shows that the spatial distribution of Shanghai land values has some distinct spatial features. Firstly, the highest land value areas are located along Nanjing Road, Huaihai Road and the Bund. Secondly, there is no conspicuous peak land value intersection within the CBD(Central Business District). Finally, The land values of Pudong are much lower than those of Puxi. The regression model between Shanghai land values and the selected location factors shows that Nanjing Road is the first important location factor affecting the spatial distribution of the land values, the city center is the second one and Huaihai Road is the third one. The Bund, the regional shopping centers and the transportation nodes have little influence on the land values. This relationship between Shanghai land values and the location factors is determined by the distribution of commercial activities, the characteristics of the CBD and the spatial form of the city.

Analysis of Factors Influencing Development of Resource-saving and Environment-friendly Agriculture in China

By selecting the data from 1989 to 2009 in China, we conduct regression analysis on the total output value of agriculture, the usage amount of chemical fertilizer, total power of agricultural machinery, effective irrigation area, and the damage area. The results show that the usage amount of chemical fertilizer and the damage area, the great obstacle to the construction of resource-saving and environment-friendly agriculture, have significant impact on the output value of agriculture; total power of agricultural machinery and effective irrigation area have insignificant impact on the output value of agriculture. Based on these, the suggestions for promoting the development of resource-saving and environment-friendly agriculture are put forward as follows: transform agricultural production mode, and optimize the industrial structure of agriculture; promote scientific use of chemical fertilizer, and promote the ability of agriculture to resist risks; promote the development of biogas in rural areas, and carry out the comprehensive use of biological material resources in rural areas.

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.

Quality of life of hospitalized patients after lung cancer operation and analysis of influencing factors

Objective:To explore the current status and influencing factors of quality of life in patients with lung cancer after surgery in a tertiary hospital in Hainan province.Methods:To investigate the influencing factors of quality of life of lung cancer patients after surgery in a tertiary hospital in Hainan province by cross‑sectional survey method.Results:The scores of insomnia,appetite loss,constipation and pain in 186 lung cancer patients after surgery in a tertiary hospital in Hainan Province were significantly higher than the reference value.Multiple linear regression analysis showed that older patients(>60 years)had lower scores in physical function domain(β=-0.193),and female patients had more appetite loss symptoms(β=0.245).Compared with other minority ethnic groups,Han ethnic group had lower scores in role function domain(β=0.179),more severe fatigue symptoms(β=-0.162),and higher general health level(β=0.166).Patients with employee medical insurance had lower scores of emotional function(β=0.194),cognitive function(β=0.281),the lowest score in social function(β=0.188),and severe pain in other parts(β=-0.227).Smokers had less cough symptoms(β=0.175)and more arm and shoulder pain symptoms(β=-0.21)than non‑smokers.Patients with secondhand smoke exposure had lower cognitive function scores(β=-0.158)and more obvious symptoms of oral ulcer(β=0.185).Patients who drank alcohol frequently(drinking frequency>1 time/day)had more severe cough symptoms(β=0.27).Patients with small number of children(0‑1)had milder cough symptoms(β=0.178).Patients who did not understand the disease had obvious symptoms of arm and shoulder pain(β=0.151).Patients with early pathological stage(stageⅠ‑Ⅱ)had more severe shortness of breath(β=-0.159)and pain(β=-0.181).The symptoms of appetite loss were more obvious in patients living in cities(β=0.192).The symptoms of peripheral neuropathy were more obvious(β=0.174).Patients who often consumed pickulated food had severe pain symptoms(β=-0.219),and pain in other parts was obvious(β=-0.149).Male patients had obvious alopecia symptoms(β=-0.306).Conclusion:Age,ethnicity,residence,type of medical insurance,number of children,pathological stage of lung cancer,smoking,second‑hand smoke exposure,alcohol consumption,and frequent consumption of pickled food were related to the quality of life of lung cancer patients in hospital after surgery.Medical staff and family members should pay attention to the emotional communication of patients during the treatment of lung cancer patients in hospital after surgery.Patients should avoid exposure to smoking,alcohol and second‑hand smoke,and reduce consumption of pickled food.