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.

Enterprise Value Valuation-BYD as an Example Based on SWOT Model and Multiple Regression Model

BYD is one of the largest new energy vehicle companies in China.Analyzing its scenario and the factors that affect its value helps to understand and identify development opportunities and potential problems.On one hand,this paper makes a qualitative analysis of BYD,using SWOT model to study the internal capability and external environment of BYD.On the other hand,the multiple regression model is used for quantitative analysis of BYD’s enterprise value,and the model is established based on three factors:enterprise fundamentals,investor behavior and psychology,and macroeconomic policy uncertainty,and the stepwise regression is carried out.The results show that the increase of institutional investors’shareholding ratio,the increase of investor sentiment index,and the increase of M2 growth rate will increase the overall enterprise value,while the increase of economic policy uncertainty will decrease the enterprise value.

Stability of mine ventilation system based on multiple regression analysis

In order to overcome the disadvantages of diagonal connection structures that are complex and for which it is difficult to derive the discriminant of the airflow directions of airways, we have applied a multiple regression method to analyze the effect, of changing the rules of mine airflows, on the stability of a mine ventilation system. The amount of air （ Qj ） is determined for the major airway and an optimum regression equation was derived for Qi as a function of the independent variable （ Ri ）, i.e., the venti- lation resistance between different airways. Therefore, corresponding countermeasures are proposed according to the changes in airflows. The calculated results agree very well with our practical situation, indicating that multiple regression analysis is simple, quick and practical and is therefore an effective method to analyze the stability of mine ventilation systems.

New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis

There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.

Prediction of cavity growth rate during underground coal gasification using multiple regression analysis

During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by controllable （operation pressure, gasification time, geometry of UCG panel） and uncontrollable （coal seam properties） factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 1 l UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

Factors Affecting Minimum Foot Clearance in the Elderly Walking: A Multiple Regression Analysis

Because falls among the elderly can cause serious injury leading to a bedridden state, methods to maintain motor function in the elderly and prevent falls are important. Among falls by the elderly are tripping falls, in which the forefoot gets caught on the floor to cause the fall. Minimum foot clearance (MFC), the smallest distance between the floor and the foot during the swing phase of gait, has been given attention as a cause of tripping falls, as elderly people at risk of falling have been reported to have low MFC. No research has been done, however, to examine what geriatric factors determine MFC. In this study, various measurements were taken on muscle strength, joint angles, and other characteristics as factors possibly influencing the height of MFC, a multiple regression analysis was performed with MFC as the dependent variable, and those factors with high degrees of influence were extracted. The results revealed that the height of MFC is highly influenced by strength of the hip flexors and angle of ankle dorsiflexion. The results of this study should be taken into consideration when having elderly people do fall prevention training.

Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al2O3, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.

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

Multiple Regression Analysis of Influencing Factors on Yield of New Sugarcane Variety Yuetang 03-373

[Objectives]The purpose of this study was to provide reference for cultivation and promotion of a new sugarcane variety Yuetang 03-373,on the basis of analyzing and summarizing the characters of the variety.[Methods]Correlation,multiple regression and path analyses were performed for the yield and yield components of Yuetang 03-373.[Results]Correlation analysis shows that cane yield was significantly correlated with millable stalk number,stalk length and stalk diameter,and among them,the correlation with millable stalk number was the strongest.Multiple regression and path analyses show that millable stalk number contributed the most to cane yield,followed by stalk length,and stalk diameter contributed the least.The regression equation of cane yield against the three yield components was y=-2.8713+1.5497x1+5.8990x2-395.4294x3(R=0.9672**).[Conclusions]Millable stalk number and stalk length were the important and major factors for high yield of Yuetang 03-373,indicating that Yuetang 03-373 is a sugarcane variety of millable stalk type.In cultivation,full play should be given to the advantage of Yuetang 03-373 in millable stalk number,as well as stalk length(plant height),in order to achieve the purpose of increasing yield.

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.

Regression Analysis of the Number of Association Rules

The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

The elementary explore of multiple regression techniques about the fiberglass filter paper＇s influence on the PM2.5 assaying

The thesis had a deep research about the fiberglass filter paper＇s influence on the PM2.5 assaying. It has chosen XRF to make a quantitative analysis. Based on multiple regression theory it regard fiberglass filter paper＇s quality, element content and the quality of the loaded sample as independent variable, while the element＇s quality that the sample has collected as dependent variable. Furthermore, it has established four multiple quadric response surface models which concerning Ca by using of Mathematica and Matlab： Y = 0.8649-2.094x1-2.08x2 -1.375x3-10.58x1x2＋8.53x1x3＋1.549x2x3-3.443x1^2＋6.555x2^2＋6. 547x3^2; Y = 0.8649-2.094x1-2.08x2-1.375x3; Y = 0.8649 -2.094x2-2.08x2-1.375x3-3.443x1^2＋6.525x2^2＋6.547x3^2 ; Y =0.8649-2.094x1-2.08x2-1.375x3-10.58x1x2＋8.53x1x3＋1.549x2x3. After comparison it has finally found the best model. In combining with the sample it present a multiple data fitting analysis method which could adjust the fiberglass filter paper model accordingly.

Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC,multiple criteria evaluation and Bayesian approaches

We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems （GIS） -based approaches： logistic regression and Akaike＇s Information Criterion （AIC）, Multiple Criteria Evaluation （MCE）, and Bayesian Analysis （specifically Dempster-Shafer theory）. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified （overall accuracy）, the failure to predict a species where it occurred （omission error） and the prediction of presence where there was absence （commission error）. Our overall accuracy showed the logistic regression approach was the most accurate （74.51%）. The multiple criteria evaluation was intermediate （39.22%）, while the Dempster-Shafer （D-S） theory model was the poorest （29.90%）. However, omission and commission error tell us a different story： logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach （e.g. Dempster-Shafer） that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans .

An Adaptive Design for Six Sigma(ADFSS): a Simulated Annealing and Regression Analysis Embedded Approach

Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into four major phases viz. identify, design, optimize, and validate （IDOV）. And an adaptive design for six sigma （ADFSS） incorporating the traits of artifidai intelligence and statistical techniques is presented. In the identify phase of the ADFSS, fuzzy relation measures between customer attributes （CAs） and engineering characteristics （ECs） as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers （FLCs）. These two measures are then used to establish the cumulative impact factor for ECs. In the next phase （ i. e. design phase）, a transfer function is developed with the aid of robust multiple nonlinear regression analysis. Furthermore, 1this transfer function is optimized with the simulated annealing （ SA ） algorithm in the optimize phase. In the validate phase, t-test is conducted for the validation of the design resulted in earlier phase. Finally, a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.

Investigation for Smartfood’s Market Ability Based on Regression Analysis

Product Development at Smartfood is considering adding a new low-carbohydrate food product labeled K-Pack to their product line.Hence,the company needs to estimate the product’s profit potential and choose the best marketing strategy.In order to investigate the different factors’impact on sales,we establish a multiple regression model.In our model,we consider selling price,advertisement payment,and selling location as independent variables and the sale is a dependent variable.Based on the empirical test,we found that the sales of the product increase as the selling price and the ads investment increase,and locations near bakery sections lead to higher sales.We also found that the final profits increase as the selling price and the ads investment increase.As a result of the analysis made with the model,we provide the company with a full-scale estimation of sales and suggestions on their marketing strategy:higher price,higher advertisement payment,and selling locations near the stores’bakery sections.Compared with the original prediction made by the company’s staff,the methods followed are more accurate and provide suggestions more specifically.

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.

Statistical Analysis of Leaf Water Use Efficiency and Physiology Traits of Winter Wheat Under Drought Condition

Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency （WUE） and physiological traits （photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.） of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area.

Suggestion of advanced regression model on friction angle of fault gouge in South Korea

Although friction characteristics of fault gouge are important to understand reactivation of fault,behavior of earthquake,and mechanism of slope failure,analysis results of fault gouge have low accuracy mostly than those of soils or rocks due to its high heterogeneity and low strength.Therefore,to improve the accuracy of analysis results,we conducted simple regression analysis and structural equation model analysis and selected major influential factors of friction characteristics among many factors,and then we deduced advanced regression model with the highest coefficient of determination(R^(2)) via multiple regression analysis.Whereas most coefficients of determination in simple regression analysis are below0.3-0.4,coefficient of determination in multiple regression analysis is remarkably large as 0.657.

Dimensioning a stockpile operation using principal component analysis

Mineral processing plants generally have narrow tolerances for the grades of their input raw materials,so stockpiles are often maintained to reduce material variance and ensure consistency.However,designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their grades.In this paper,we address this issue by applying principal component analysis(PCA)to reduce the dimensions of the input data.The study was conducted in three steps.First,we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original variance.Next,we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in MATLAB.We used the variance reduction ratio as the primary criterion for evaluating the efficiency of the stockpiles.Finally,we used multiple regression to identify the relationships between stockpile efficiency and various design parameters and analyzed the regression results based on the original input variables and principal components.The results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.