多元线性回归在经济技术产量预测中的应用探析

多元线性回归法在经济技术产量的预测过程中具有较为突出的实践意义。以往在研究经济技术发展时常以单一的因素作为研究目标,导致预测结果无法发挥出较为可靠的指导作用,从而不利于我国整体经济增长率的提升。在此之上,文章简要分析了多元线性回归法的内涵,并重点论述了多元线性回归在经济技术产量预测中的应用内容及优化措施,从明确技术产量预测结果、扩大对外出口贸易范围、优化地区经济产业结构、全面刺激居民消费需求等方面展开细致讨论,由此改善当下经济技术发展现状。

Using Path Analysis to Identify the Influence of Climatic Factors on Spring Peak Flow Dominated by Snowmelt in an Alpine Watershed

Spring snowmelt peak flow （SSPF） can cause serious damage. Precipitation as rainfall directly contributes to the SSPF and influences the characteristics of the SSPF, while temperature indirectly impacts the SSPF by shaping snowmelt rate and determining the soil frozen state which partitions snowmelt water into surface runoff and soil infiltration water in spring. It is necessary to identify the important and significant paths of climatic factors influencing the SSPF and provide estimates of the magnitude and significance of hypothesized causal connections between climatic factors and the SSPF. This study used path analysis with a selection of five factors - the antecedent precipitation index （API）, spring precipitation （SP）, winter precipitation as snowfall （WS）, 〈0℃ temperature accumulation in winter （[ATNI）, and average 〉0℃temperature accumulation in spring （AT） - to analyze their influences on the SSPF in the Kaidu River in Xinjiang, China. The results show that {ATN}, AT and WS have a significant correlation with the SSPF, while API and SP do not show a significant correlation. AT and WS directly influence the SSPF, while as the influence of[ATN] on SSPF is indirect through WS and AT. The indirect influence of [ATN[ on SSPF through WS accounts for 69% of the total influence of [ATN] on SSPF. Compared to the multiple linear regression method, path analysis provides additional valuable information, including influencing paths from independent variables to the dependent variable as well as direct and indirect impacts of external variables on the internal variable. This information can help improve the description of snow melt and spring runoff in hydrologic models as well as the planning and management of water resources.

Pyrolysis Characteristics and Kinetics of Methyl Oleate Based on TG-FTIR Method

The thermal decomposition characteristics of methyl oleate were preliminarily investigated under nitrogen atmo-sphere by a thermogravimetric analyzer when the ester was heated at a heating rate of 10℃/min from room temperature to 600℃. Furthermore, the pyrolytic and kinetic characteristics of methyl oleate were intensively studied at different heating rates. The gaseous species obtained during thermal decomposition were also identiifed by the TG-FTIR coupling analysis. The results showed that the pyrolysis of methyl oleate proceeded in three stages, viz. the drying stage, the main pyrolysis stage and the residual pyrolysis stage. The initial decomposition temperature, the maximum weight loss temperature, the peak decomposition temperature and the rate of maximum weight loss of methyl oleate increased with the increasing heating rates. Gaseous CO, CO2 and H2O were the typical decomposition products from pyrolysis of methyl oleate. In addition, a kinetic model for thermal decomposition of methyl oleate was built up based on the experimental results using the Coats-Redfern integral method and the multiplelinear regression method. The activation energy, the preexponential factor, the reaction order and the kinetic equation for thermal decomposition of methyl oleate were obtained. Comparison of the experimental data with the calculated ones and analysis of statistical errors of pyrolysis ratios demonstrated that the kinetic model was reliable for studying the pyrolysis of methyl oleate. Finally, the kinetic compensation effect between the preexponential factors and the activation energy in the pyrolysis of methyl oleate was also conifrmed.

Development of a mass model in estimating weight-wise particle size distribution using digital image processing

Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.

The Effect of Corporate Social Responsibility on Employees＇ Organizational Commitment in High-tech Enterprises of Jiangsu Province--Based on the Mediating Role of Regulatory Focus

With China＇s rapid economic development, corporate social responsibility （CSR） plays greater impact on its direct stakeholder employees＇ organizational commitment （EOC）. A questionnaire survey was conducted on 426 employees of high-tech enterprises within Jiangsu Province. Multiple regression method was used to test the hypothesis based on the questionnaire reliability and validity analysis. The results show that the four dimensions of CSR including employees responsibility, products responsibility, environment responsibility and charitable responsibility have positive effect on the affective commitment;while the continuance commitment might be more influenced by the environment and charitable responsibility. The regulatory focus plays a moderate effect between CSR and EOC. Promote regulatory focus have positive impact on affective commitment and negative impact on continuance commitment. Defense regulatory focus is just on the opposite.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Study On the Influencing Factors of Energy-Saving and Environmental Protection Industries in Shanghai

By using principal component analysis, this paper selected some appropriate influencing indicators, and constructed multiple linear regression models to predict the development of energy-saving environmental protection industry（ESEPl） in Shanghai. The Influencing Factors can be categorized into comprehensive economic factors and environmental factors, and GDP of the second industries and the total industries GDP in comprehensive economic factors have the strongest correlation, while in the environmental index factors, the total discharge of waste water has the strongest correlation. On the basis of influencing factors study, the regression model shows that by the end of 2020, the industry investment will reach 89.788 billion RMB, which proves that the development of ESEPI in Shanghai would grow continuously and dramatically.

Economic Consequences of Gender Equality in Europe

This paper shows influence of gender equality on economy where it analyzed how gender equality in Europe has affected on the development of the frozen food industry and services related to childcare. The development of these industries has given a positive impulse to the development of the whole economy. In this analysis, it is used multiple regressions as one of the most important statistical methods. In the first part of this paper, it shows the connection among the growth of female employment, growth in frozen food expenditure and growth of GDP in United Kingdom. In the second part of paper, it shows the relationship among the growth of labor force participation of women, growth of number of kindergarten and growth of GDP in Hungary. To proof these relationships, it used a multiple regression model. This statistical model was tested by using the T schedule which showed that the model in both the analyses is correct. At the end of the paper, it presents that employment rate and GDP behaves in the same way in European Union. These analyses show that it is necessary to continue to strengthen gender equality if the policy makers want to achieve even greater economic growth. The issue of gender equality is a very important factor in creating employment policy, and statisticians should be more involved in process of employment policy and gender equality

Updating Methods for Real Time Flood Forecasting： A Comparison through Senegal River Basin Upstream Bakel

Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulting damages. Flood forecasting is a necessity. Flood forecasting plays also an important role in the implementation of flood management scenarios and in the protection of hydro electric structures. Many methods are applied. The most complete are based on the conservation laws of physics governing the free surface flow. These methods need a complete description of the geometry of the river and their implementation requires also huge investments. In practice the river basin can be considered as a system of inputs-outputs related by a transfer function. In this paper the authors first used a multiple linear regression model with constant parameters estimated by the ordinary least square method to simulate the propagation of the floods in the upstream part of the Senegal river basin. The authors then apply statistical and graphical criteria of goodness-of-fit to test the suitability of this model. Three procedures of parameters updating have then been added to this linear model： the Kalman filter method, the recursive least square method, and the stochastic gradient method The criteria of goodness-of-fit used above have shown that the stochastic gradient method, although more rudimentary, represents better the flood propagation in the head basin of the Senegal river upstream Bakel. This result is particularly interesting because data influenced by Manantali Dam are used.

Influence of confined water on the limit support pressure of tunnel face in weakly water-rich strata

In the process of shield tunneling through soft soil layers,the presence of confined water ahead poses a significant threat to the stability of the tunnel face.Therefore,it is crucial to consider the impact of confined water on the limit support pressure of the tunnel face.This study employed the finite element method(FEM)to analyze the limit support pressure of shield tunnel face instability within a pressurized water-containing layer.Subsequently,a multiple linear regression approach was applied to derive a concise solution formula for the limit support pressure,incorporating various influencing factors.The analysis yields the following conclusions:1)The influence of confined water on the instability mode of the tunnel face in soft soil layers makes the displacement response of the strata not significant when the face is unstable;2)The limit support pressure increases approximately linearly with the pressure head,shield tunnel diameter,and tunnel burial depth.And inversely proportional to the thickness of the impermeable layer,soil cohesion and internal friction angle;3)Through an engineering case study analysis,the results align well with those obtained from traditional theoretical methods,thereby validating the rationality of the equations proposed in this paper.Furthermore,the proposed equations overcome the limitation of traditional theoretical approaches considering the influence of changes in impermeable layer thickness.It can accurately depict the dynamic variation in the required limit support pressure to maintain the stability of the tunnel face during shield tunneling,thus better reflecting engineering reality.

Increasing trend of lightning activity in the South Asia region

Lightning is an important natural source of wildfires and oxynitride,and hence significantly influences ecological systems and atmospheric chemistry.Here,we choose South Asia,an important region for global water reallocation and global climate changes,to examine lightning variations based on the longest existing lightning dataset from the OTD/LIS observations.We identify a clear increase in lightning density in the research region,increasing at a rate of 0.096 fl km^(-2)a^(-1)over the last two decades.Multiple linear regression analysis is adopted to identify the main influencing factors among ten potential thermodynamic or microphysical factors and the crucial areas contributing to the increases in lightning.The surface latent heat flux along the west coast of the Indian subcontinent is the largest contributor,explaining52%of the lightning variance and contributing to a 0.025 fl km^(-2)a^(-1)increase.The sea surface temperature in the Arabian Sea,the convective available potential energy(CAPE)over the northwestern Indian subcontinent,and the wind shear along the northwestern coast also make important contributions to the lightning increase,indicating that the thermodynamic effects overwhelm the microphysical effects on lightning activity over the South Asia region.

Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation

Geological disasters not only cause economic losses and ecological destruction, but also seriously threaten human survival. Selecting an appropriate method to evaluate susceptibility to geological disasters is an important part of geological disaster research. The aims of this study are to explore the accuracy and reliability of multi-regression methods for geological disaster susceptibility evaluation, including Logistic Regression(LR), Spatial Autoregression(SAR), Geographical Weighted Regression(GWR), and Support Vector Regression(SVR), all of which have been widely discussed in the literature. In this study, we selected Yunnan Province of China as the research site and collected data on typical geological disaster events and the associated hazards that occurred within the study area to construct a corresponding index system for geological disaster assessment. Four methods were used to model and evaluate geological disaster susceptibility. The predictive capabilities of the methods were verified using the receiver operating characteristic(ROC) curve and the success rate curve. Lastly, spatial accuracy validation was introduced to improve the results of the evaluation, which was demonstrated by the spatial receiver operating characteristic(SROC) curve and the spatial success rate(SSR) curve. The results suggest that: 1) these methods are all valid with respect to the SROC and SSR curves, and the spatial accuracy validation method improved their modelling results and accuracy, such that the area under the curve(AUC) values of the ROC curves increased by about 3%–13% and the AUC of the success rate curve values increased by 15%–20%; 2) the evaluation accuracies of LR, SAR, GWR, and SVR were 0.8325, 0.8393, 0.8370 and 0.8539, which proved the four statistical regression methods all have good evaluation capability for geological disaster susceptibility evaluation and the evaluation results of SVR are more reasonable than others; 3) according to the evaluation results of SVR, the central-southern Yunnan Province are the highest sus-ceptibility areas and the lowest susceptibility is mainly located in the central and northern parts of the study area.

Denitrification Rates and Their Controlling Factors in Streams of the Han River Basin with Different Land-Use Patterns

Land-use patterns can affect various nutrient cycles in stream ecosystems, but little information is available about the effects of urban development on denitrification processes at the watershed scale. In the presented study, we investigated the controlling factors of denitrification rates within the streams of the Han River Basin, Korea, with different land-use patterns, in order to enhance the effectiveness of water resource management strategies. Ten watersheds were classified into three land-use patterns （forest, agriculture and urban） using satellite images and geographic information system techniques, and in-situ denitrification rates were determined using an acetylene blocking method. Additionally, sediment samples were collected from each stream to analyze denitrifier communities and abundance using molecular approaches. In-situ denitrification rates were found to be in the order of agricultural streams （289.6 mg N20-N m-2 d-1） 〉 urban streams （157.0 mg N20-N m-2 d-1） 〉 forested streams （41.9 mg N20-N m-2 d-l）. In contrast, the average quantity of denitrifying genes was the lowest in the urban streams. Genetic diversity of denitrifying genes was not affected by watershed land-use pattern, but exhibited stream-dependent pattern. More significance factors were involved in denitrification in the sites with higher denitrification rates. Multiple linear regression analysis revealed that clay, dissolved organic carbon and water contents were the main factors controlling denitrification rate in the agricultural streams, while dissolved organic carbon was the main controlling factor in the urban streams. In contrast, temperature appeared to be the main controlling factor in the forested streams.