The Research on and Application of the Multi-regression Technique in the Course of the Marketing Decision-making of Enterprises

The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.

The Research and Application of the Multi-regression Technique in the Course of Enterprises＇ Marketing Decision-making

The reformation of the economy system has led the functional departments and status of the enterprises into the variance state. Under the condition of the market economy, the kernel of the enterprises＇ functional department has diverted to that of marketing decision-making, which faces to market and meets with the need of consumption. Assuredly, the kernel of marketing decision-making is to prognosticate the future market requirement quantity of the production of enterprises accurately, so that it can ensure and realize the maximum of the enterprises＇ profit to increase. Applying the proof to test demonstration analytical method of economics and adopting the multi-regression technique, this paper analyzes the enterprises＇ production requirement quantity decision-making of the GMC （Global Management Challenge） and changes a great many of uncontrollable factors to the controllable ones of the enterprises. So, it can make the forecast order form closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object with the minimum of the cost and the maximum of the profit. And it can insure the realization of the profit increase of the enterorises mostly in the life-cvcle of the production.

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.

Changing Rural Development Inequality in Jilin Province, Northeast China

Rural development inequality is an important practical issue during the course of full establishment of a ′moderately well-off society′ in modern China,and the objective understanding and evaluation of the status and regional inequality of rural development can provide scientific basis for ′building a new countryside′ and coordination development of rural-urban regions.Based on the county-level data of 2000,2005 and 2009,this paper examines the rural development inequality of Jilin Province in Northeast China by establishing a rural development index.The spatio-temporal dynamic patterns and domain factors are discussed by using the method of exploratory spatial data analysis and multi-regression model.The results are shown as follows.Firstly,most of the counties were in lower development level,which accounted for 58.3%,62.5% and 66.7% of the total counties in 2000,2005 and 2009,respectively.The characteristics of spatial inequality were very obvious at county level.For example,rural development level of Changchun Proper and the proper of seven prefecture-level cities were much higher than that of the surrounding regions.The counties in the eastern and northern Jilin Province were the lowest regions of rural development level,while the middle counties were the rapid growth areas in rural economy.Secondly,Moran′s I of rural development index(RDI) was 0.01,–0.16 and –0.06 in 2000,2005 and 2009,respectively,which indicated that spatial agglomeration of RDI was not obvious in Jilin Province,and took on the characteristic of random distribution.The counties of both the units and its adjacent units have higher development level(HH) were transferred from the western areas to the eastern areas,while the countries of both the units and its adjacent units have lower development level(LL) were diffused from the eastern to middle and western Jilin Province.Finally,the result of multi-regression analysis showed that the improvement of agricultural production condition,development of agricultural economics and the adjustment of industrial structure were the domain factors affecting rural development inequality of Jilin Province in the later ten years.

Life-cycle CO_2 Emissions and Their Driving Factors in Construction Sector in China

As the construction sector is a major energy consumer and thus a significant contributor of CO_2 emissions in China,it is important to consider carbon reduction in this industry.This study analyzed six life-cycle stages and calculated the life-cycle CO_2 emissions of the construction sector in 30 Chinese provincial jurisdictions to understand the disparity among them.Results show that building materials production was the key stage for carbon reduction in the construction sector,followed by the building operation stage.External variables,e.g.,economic growth,industrial structure,urbanization,price fluctuation,and marketization,were significantly correlated with the emission intensity of the construction sector.Specifically,economic growth exhibited an inverted U-shaped relation with CO_2 emissions per capita and per area during the period examined.Secondary industry and land urbanization were negatively correlated with CO_2 emission intensity indicators from the construction sector,whereas tertiary industry and urbanization were positively correlated.Price indices and marketization had negative effects on CO_2 emission intensity.The policy implications of our findings are that cleaner technologies should be encouraged for cement providers,and green purchasing rules for the construction sector should also be established.Pricing tools(e.g.,resource taxes)could help to adjust the demand for raw materials and energy.

Responses of River Runoff to Climate Change Based on Nonlinear Mixed Regression Model in Chaohe River Basin of Hebei Province, China

Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956–2000. Compared with auto-regression model,linear multi-regression model and linear mixed regression model,NMR can improve forecasting precision remarkably. Therefore,the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well.

Investigation of the Calibration Function of Local Earthquake Magnitude (M_L) in the Fujian-Taiwan Region

The author carefully selected earthquakes with M_L=4.0～5.0, 215 occurring in the crust in the Taiwan region. The attenuation characteristics of maximum displacement recorded by the Fujian digital network have been obtained by multi-analysis as follows:logA=2.07+231.1/Δ (150km≤Δ≤650km) And the corresponding expression of calibration function is, R(Δ)=3.45-231.1(1/Δ-0.01) (150km≤Δ≤650km) Then, the author determined the magnitude and its error with the data from the Fujian network using the calibration function brought forward in 1997 and the above formula for 790 earthquakes occurring in the crust in the Taiwan region from September 1997 ～ August 2005. The result indicates that the average error of the network is 0.20 with the former and 0.18 with the latter. The average error is 0.13 with the latter with station correction. Compared with the magnitude determined by Taiwan seismologists, the magnitude value with the former is lower by 0.50 on average and that with the latter is higher by 0.08 on average.

Contributions of climate change and human activities to ET and GPP trends over North China Plain from 2000 to 2014

College of Resources and Environment/Sino-Danish Center, Univers Beijing 100049, China nstitute of Geographic Sciences and ty of Chinese Academy of SciencesAbstract： Quantifying the contributions of climate change and human activities to ecosystem evapotranspiration （ET） and gross primary productivity （GPP） changes is important for adaptation assessment and sustainable development. Spatiotemporal patterns of ET and GPP were estimated from 2000 to 2014 over North China Plain （NCP） with a physical and remote sensing-based model. The contributions of climate change and human activities to ET and GPP trends were separated and quantified by the first difference de-trending method and multivariate regression. Results showed that annual ET and GPP increased weakly, with climate change and human activities contributing 0.188 mm yr-2 and 0.466 mm yr-2 to ET trend of 0.654 mm yr-2, and -1.321 g C m-2 yr-2 and 7.542 g C m-2 yr-2 to GPP trend of 6.221 g C m-2 yr-2, respectively. In cropland, the increasing trends mainly occurred in wheat growing stage; the contributions of climate change to wheat and maize were both negative. Precipitation and sunshine duration were the major climatic factors regulating ET and GPP trends. It is concluded that human activities are the main drivers to the long term tendencies of water consumption and gross primary productivity in the NCP.

Dam deformation analysis based on BPNN merging models

Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.