Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

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.

A study of the mixed layer of the South China Sea based on the multiple linear regression

Multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea （SCS） based on the simple ocean data assimilation （SODA） dataset. The spatio-temporal distributions of the MLD, the buoyancy flux （combining the NHF and the NFF） and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions （the three factors） was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid.

Application of Multiple Linear Regression and Manova to Evaluate Health Impacts Due to Changing River Water Quality

Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated water. This study focuses on the application of statistical techniques, Multiple Linear Regression model and MANOVA to assess health impacts due to pollution in Cauvery river stretch in Srirangapatna. In this study, using Multiple Linear Regression, it is found that health impact level is 60.8% dependent on water quality parameters of BOD, COD, TDS, TC and FC. The t-statistics and their associated 2-tailed p-values indicate that COD and TDS produces health impacts compared to BOD, TC and FC, when their effects are put together across all the six sampling stations in Srirangapatna. Further Pearson correlation Matrix shows highly significant positive correlation amongst parameters across all stations indicating possibility of common sources of origin that might be anthropogenic. Also graphs are plotted for individual parameters across all stations and it reveals that COD and TDS values are significant across all sampling stations, though their values are higher in impact stations, causing health impacts.

A Universal Selection Method in Linear Regression Models

In this paper we consider a linear regression model with fixed design. A new rule for the selection of a relevant submodel is introduced on the basis of parameter tests. One particular feature of the rule is that subjective grading of the model complexity can be incorporated. We provide bounds for the mis-selection error. Simulations show that by using the proposed selection rule, the mis-selection error can be controlled uniformly.

Multiple linear regression models of urban runoff pollutant load and event mean concentration considering rainfall variables

Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.

Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing＇an Mountains, in northeast China. The response variables were the area burned by lightning- caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log- linear model （log-transformation on both response and predictor variables）, and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R2, AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regressionmodel and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at at = 0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the .main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum rela- tive humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.

Economic modeling of mechanized and semi-mechanized rainfed wheat production systems using multiple linear regression model

Mathematical modeling of economic indices is a challenging topic in crop production systems.The present study aimed to model the economic indices of mechanized and semimechanized rainfed wheat production systems using various multiple linear regression models.The study area was Behshahr County located in the east of Mazandaran Province,Northern Iran.The statistical population included all wheat producers in Behshahr County in 2016/17 crop year.Five input variables were human labor,machinery,diesel fuel,chemical(chemical fertilizers and chemical pesticides)costs,and the income was considered to be the output.The results showed that the cost of wheat production in the semimechanized system was higher than that of the mechanized system.In both systems,the highest cost was related to agricultural machinery input.Moreover,seed cost was lower in the mechanized system than that of the semi-mechanized system.The net return indicator was 993.68$ha1 and 626.71$ha1 for the mechanized and semi-mechanized systems,respectively.The average benefit to cost ratio was 3.46 and 2.40 for the mechanized and semi-mechanized systems,respectively,demonstrating the greater profitability of the mechanized system.The results of the evaluation of five types of regression models including the Cobb-Douglas,linear,2FI,quadratic and pure-quadratic for the mechanized and semi-mechanized production systems indicated that in the developed Cobb-Douglas model,the R2-value was higher than that of the quadratic model while RMSE and MAPE of the quadratic model were determined to be smaller than that of the Cobb-Douglas model.Therefore,the best model to investigate the relationship between input costs and the income of wheat production in both mechanized and semi-mechanized systems was the quadratic model.

Construction of Multiple Linear Regression Prediction Model of PRETCO-A Scores and Its Positive Backwash Effect on Teaching

PRETCO-A is a standardized English proficiency test set up to evaluate the English application ability of the students in higher vocational college.In order to improve the passing rate,a multiple linear regression prediction model is constructed in this paper.A significance test was first performed on the regression model and the regression coefficient to verify a high correlation among the variables.The confirmed model was then put into application to predict the students’scores and identify the students who may fail the exam,leading to targeted tutoring assistance given to those students in advance.Finally,60 students with predicted scores lower than 60 points were selected as research samples,and randomly divided into the control group and the experimental group,30 students in each group.Finally,the experimental group students were given 40 teaching hours of precision assistance and targeted training,while the control group did not engage in any teaching intervention.The experimental results indicate that the pass rate of experimental group is 20%higher than the control group,which means the backwash effect of the test prediction is positive.The prediction model is proved to be scientific and reliable for teaching.

感性工学视域下外卖配送电动车造型设计

目的提出符合用户感性需求特征的外卖配送电动车造型设计方案。方法基于感性工学理论,将感性需求转化成定性分析与定量分析,采用SDM(语意差异法)方法制作调查问卷,收集并筛选描述外卖配送电动车造型的感性意象词汇与样本图片,并结合主成分分析法,提取影响造型设计的主要因子和外卖电动车造型元素,构建感性意象数学模型;通过多元线性回归法,借助SPSS统计软件对四个主要造型构成要素与感性意象进行量化处理。结果对外卖配送电动车的前面板、前大灯、车身架和座垫的造型元素展开分析,设计出与用户“时尚”和“速度”情感需求最匹配的梯形前面板、变异多边形前大灯、多边形车身架和细长型座垫造型元素。结论感性工学不仅能有效挖掘用户需求,而且还能直观表达出感性感受与数值的对应关系,对外卖配送电动车造型要素进行有目的的设计和调整。

芙蓉李果实成熟期间的综合品质评价指标筛选与表观预测模型构建

目的科学评价芙蓉李果实成熟期间的营养品质,建立色度值表观特征与营养品质的关系。方法以福建省主栽品种芙蓉李为研究对象,对其成熟期间果糖、葡萄糖、蔗糖、苹果酸、奎尼酸、琥珀酸、柠檬酸、富马酸、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷、多酚、黄酮、类胡萝卜素等13个品质指标进行分析和综合评价。结果芙蓉李成熟期间,各品质指标的含量变化存在显著差异(P<0.05),综合运用相关分析、因子分析、绝对因子分析-多元线性回归(absolute principal component scores-multiple linear regression,APCS-MLR)分析筛选可反映芙蓉李综合品质的主要指标。因子分析提取出3个主因子,贡献率分别为52.677%、23.468%、11.649%,累计贡献率为87.794%。综合APCS-MLR等数理统计分析,主因子1主要对果糖、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷贡献较大,贡献率分别为53.00%、73.85%、55.54%;主因子2主要对蔗糖、富马酸、果糖、柠檬酸的贡献率较大,分别为28.26%、18.70%、16.14%、15.59%;主因子3主要对多酚(29.13%)和黄酮(28.28%)有较大贡献率;选取3个主因子总贡献率高于60%的果糖、葡萄糖、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷作为综合品质评价的主要指标。分别对已筛选出的4个主要评价指标与色度值进行多元线性逐步回归分析,建立4个主要指标与色度值的表观预测模型,各模型均具有较好的拟合度,预测值与实测值的均方根误差较小;进一步验证结果表明,通过色度值对4个指标的预测具有较高的可靠性和准确性。结论本研究筛选出的主要指标及预测模型可更加简单、便捷地评价芙蓉李果实成熟期间的综合品质。

基于SVM的富水岩溶区双线盾构隧道下穿既有线临界安全距离研究

双线盾构隧道在极复杂的岩溶地质中下穿既有线施工时,会对地层造成较大扰动,从而增加既有地表沉降过大或坍塌的风险,严重威胁既有线的运营安全。同时,隧道穿越富水岩溶区域时,需要保持一定距离以避免与溶洞相交,确保双线盾构隧道围岩的稳定性。利用FlAC^(3D)软件建立不同工况组合下双线盾构隧道与左侧溶洞的临界安全距离数值模型,分析不同地下水位对既有线沉降和围岩塑性区范围的影响,并采用正交试验和多元线性回归方法建立双线盾构隧道在不同围岩等级、地下水埋深、开挖隧道埋深和溶洞孔径组合下与溶洞的临界安全距离计算公式。此外,还基于SVM模型对正交试验结果进行机器学习,得到可以预测岩溶地区双线盾构隧道临界安全距离的智能预测系统,实现双线盾构隧道下穿既有线临界安全距离的非线性智能预测。结果表明:当地下水位位于既有线以上时,其水位越高,既有线的沉降量越大、围岩塑性区范围越大,地下水对盾构隧道与溶洞的临界安全距离有很大影响,应用SVM模型和多元线性回归方法确定双线盾构隧道与溶洞之间的临界安全距离是行之有效的。

基于Excel软件利用矩阵运算模拟多元线性回归模型的应用分析

文章基于多元线性回归分析的基本原理,模拟多元线性回归方程的矩阵,研究专业基础课程与专业核心课程的相关性.利用Excel软件中的矩阵函数的运算,对兰州城市学院财务管理专业110名学生的线性代数、概率论与数理统计和财务管理学课程卷面成绩为样本数据进行正态性分析.结果表明,课程成绩服从正态分布,据此建立卷面成绩的多元线性回归预测模型,通过对数据样本的方差分析和对统计量进行检验和统计推断,表明方程拟合度较好,以此帮助师生预测财务管理学课程成绩,针对三门课程预测的线性关系,对教师提出了一些教学建议.

基于GPRS无线通讯技术的自动化灌溉系统设计

针对传统的大水漫灌等灌溉方式灌水不均、容易造成农作物病害或涝死、浪费水资源和人工成本较高的问题,基于GPRS无线通讯技术对自动化灌溉系统进行了设计。为了获取有效的灌溉数据,同时能够对数据进行统计、分析和预测,设计了自动灌溉数据信息的预处理方法,并采用多元线性回归预测模型对灌溉数据进行预测。为了验证该自动化灌溉系统的性能,对其进行了数据采集试验和灌溉预测试验,结果表明:系统对灌溉数据监测和预测的准确率均较高。

基于LSTM的多因素石灰窑煅烧带温度预测研究

针对石灰窑煅烧过程易出现燃烧不平衡的问题以及石灰窑煅烧系统的滞后性,提出了大数据分析+神经网络的解决方案。利用大数据分析对石灰窑多源历史数据进行数据融合插补,采用多元线性回归方程分析空间因素对温度的影响,通过时间滑窗提取特征,在此基础上利用长短期记忆神经网络(LSTM)算法构建多因素模型,并采用自适应运动估计算法进行优化。实验结果表明:较单因素LSTM模型,多因素LSTM模型有效提高了石灰窑温度预测精度,现场可根据预测值提前调整工艺参数,实现了石灰窑局部温度预测。

微胶囊相变材料改良粉砂土的导热系数及预测模型

【目的】针对季节冻土地区渠道冻融破坏,分析微胶囊相变材料(microencapsulated phase change materials,mPCM)改良粉砂土层渠基的温度场,对改良粉砂土的导热系数进行研究。【方法】以mPCM为改良剂,掺入渠基粉砂土形成mPCM改良粉砂土;对mPCM改良粉砂土进行导热系数实验和内部结构表征;采用多元线性回归和支持向量机(support vector machine,SVM)方法分别建立mPCM改良粉砂土的导热系数预测模型。【结果】mPCM改良粉砂土导热系数与含水率、干密度、mPCM掺量有关,且受冰水相对含量、冰水相变潜热、mPCM相变潜热和mPCM填充密实作用的影响,具有明显的温度效应;mPCM改良粉砂土导热系数的变化与实验温度和mPCM相变温度有关,可分为快速降低、缓慢降低和逐步上升3个阶段;多元线性回归和SVM模型均能较好地拟合预测mPCM改良粉砂土的导热系数,但SVM模型更适用于表征mPCM改良粉砂土导热系数各影响因素间的非线性关系。【结论】mPCM改良粉砂土的导热系数提高能够有效调控渠基土温度场,减轻渠道冻害,且SVM模型能更加准确地进行导热系数预测。

渤海海域井壁取心裂解烃S_(2)烃类损失恢复回归分析

地化录井受工程、地质条件及人为因素的影响,往往造成岩石样品从井底到地表的烃类损失,不能很好地反映地下储层的真实含油气信息,因此需要一种合理准确的方法进行烃类损失恢复。针对渤海海域不同地区不同层位的岩屑值(自变量)与壁心值(因变量)之间的关系,基于最小二乘法、梯度下降法及其衍生算法,以多元线性回归和非线性回归两种方式来拟合研究区井壁取心数据。多元线性回归模型可使用标准方程法、岭回归、LASSO(Least Absolute Shrinkage and Selection Operator)及弹性网进行回归拟合,非线性回归模型可使用梯度下降法和分段函数的拟合方法。对不同回归分析方法进行分析对比可知,岭回归在计算线性关系的烃类损失方面具有较好的效果,决定系数r^(2)均超过0.7;基于岭回归分段函数拟合和非线性回归模型y=x/(b+kx)适合非线性烃类损失恢复。与传统的烃类损失恢复方法相比,使用量化的方式对研究区烃类进行恢复,更加科学全面,具有广泛的应用前景。

影响中国电影行业发展的因素分析

我们应该很容易发现的一个现象就是自己身边的人都慢慢开始追求生活的品质了,这就是中国国民生活水平提高的表现,同时也是中国经济实力逐渐壮大的印记。而为了满足人民日益增长的生活品质需要,要在文化软实力方面增加输出。在文化产业中,人民大众最容易接触到的就是电影行业,而电影行业在文化产业中也非常典型和突出。当前,中国是世界第二大电影市场,增长率最快。为了更好地分析影响因素和预测行业走势,本文利用了统计分析工具Stata,建立多元线性回归模型进行分析,提出建议。

基于灰色回归模型广州市果蔬类生鲜农产品冷链物流需求预测

目的通过对不同预测方法的误差进行对比研究,选取预测精度较高的方法,促进部门科学化决策。方法从农产品供给、社会经济水平、冷链物流保障、居民规模与消费能力四大维度选取15个指标来构建影响因素指标体系,对影响因素与冷链物流需求进行灰色关联度分析。采用GM(1,1)、GM(1,6)与主成分-多元回归线性模型对果蔬类生鲜农产品冷链物流需求进行预测。结果GM(1,1)预测模型、GM(1,6)预测模型、主成分-多元回归线性预测模型的预测误差分别为2.97%、1.70%、2.53%。结论GM(1,6)预测模型预测精度最高,该模型适用于中短期的冷链物流需求预测,具有较高的应用价值。

基于多元线性回归模型估测蒙古黄芪药效成分含量

以蒙古黄芪种苗为试材,采用高光谱和多元线性回归的方法,研究了栽培和仿野生对蒙古黄芪黄芪甲苷、毛蕊异黄酮葡萄糖苷含量估测方法的影响,以期为实践中利用高光谱快速、准确估测栽培和仿野生蒙古黄芪的药效成分含量提供参考依据。结果表明:栽培和仿野生蒙古黄芪药效成分含量的原始高光谱数据所建立模型的稳定性和拟合度均是最好。估测黄芪甲苷含量模型的RMSE分别是0.0045、0.0085;R^(2)分别是0.761、0.879;所建模型分别是y=0.0054+0.0016x_(1)+0.0002x_(2)+0.0007x_(3)(R^(2)=0.903)、y=-0.1223+0.0040x_(1)+0.0001x_(2)-0.0009x_(3)(R^(2)=0.904)。估测毛蕊异黄酮葡萄糖苷含量模型的RMSE分别是0.0017、0.0040;R^(2)分别是0.860、0.868;所建模型分别为y=0.0731-0.0080x_(1)-0.0004x_(2)+4×10-6x_(3)(R^(2)=0.891)、y=0.0842-0.0007x_(1)-0.0001x_(2)+0.0002x_(3)(R^(2)=0.883)。