Application of cluster analysis and stepwise regression in predicting the traffic volume of lanes

Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections,cluster analysis and stepwise regression are integrated to predict the traffic volume of lanes at non-detector isolated controlled intersections.First cluster analysis is used to cluster the lanes of non-detector isolated signal-controlled intersections and the lanes of all signal-controlled intersections with detectors.Then, by the results of cluster analysis,the traffic volume samples are selected randomly and stepwise regression is used to predict the traffic volume of lanes at non-detector isolated signal-controlled intersections.The method is tested by the traffic volume data of lanes of the road network of Nanjing city.The problem of predicting the traffic volume of lanes at non-detector isolated signal-controlled intersections was resolved and can be widely used in urban traffic flow guidance and urban traffic control in cities without enough intersections equipped with detectors.

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.

Analysis of York Pigs Feeding Behavior Using Stepwise Regression and Principal Component Regression

A statistical analysis was conducted on the feeding behavior of 106 York breeding pigs.Pearson correlation analysis,principal component correlation analysis and multiple stepwise regression equation methods were applied to establish regression equations of the York breeding pigs total feed intake per time and average feed intake per time with corrected fat thickness,feed conversion rate,and corrected daily gain.The results showed that:①there were three peak feed intake periods for the pigs,and the correlation coefficient between the feed intake and the corrected fat thickness of the pigs in the 24 h period was positive or negative,that is,increasing the number of feeding times and the feed intake was not necessarily conducive to the fat thickness accumulation,but the breeding goal of fat thickness could be achieved by controlling the feeding times and feed intake;②the average feed intake of pigs in the 60-90 kg body weight stage was 30%-50%higher than that of the 30-60 kg body weight stage,but the number of feeding times decreased,the peak feeding time was more concentrated,and the feeding duration per time was 3.0 min longer,indicating that as the weight of pigs increased,the feed intake increased significantly;and③the stepwise regression equations and the principal component equations showed that the feeding behavior of York pigs in the 30-90 kg growth stage was not only affected by the feeding time within 24 h,but also by environmental factors such as temperature and humidity.The feeding behavior of York pigs is a complex process of interaction between environmental factors and animal factors.

Stepwise Regression： An Application in Earthquakes Localization

In this paper, an overview of an important feature in statistics field has shown： the stepwise multiple linear regression. Likewise, a link between stepwise multiple linear regression and earthquakes localization has been descripted. Precisely, the aim of this research is showing how stepwise multiple linear regression contributes to solution of earthquakes localization, describing its conditions of use in HYPO71PC, a software devoted to computation of seismic sources’ collocation. This aim is reached treating a concrete case, that is computation of earthquakes localization happening on Mount Vesuvius, Italy.

Thinking on Breeding of Fecundity Genes in Guizhou Black Goats Through Cost-benefit Analysis of Mutton Sheep by SAS Multivariate Stepwise Regression

The total output value of mutton in Northwestern China has accounted for more than 60%of the total output value of animal husbandry over the years.It can be seen that the mutton industry in Northwest China not only plays a pivotal role in animal husbandry,but also plays an important role in Chinese agriculture.In this study,based on cost accounting theory,income-related theories and total factor productivity theory,using basic knowledge of statistics and economics,drawing on existing research results at home and abroad,and adopting a combination of qualitative analysis and quantitative analysis of SAS multiple stepwise regression,the changing trends of cost-benefit of mutton sheep breeding in Northwest agricultural and pastoral areas and influencing factors of production costs and production efficiency were investigated,aiming to provide reference for saving mutton sheep feeding material resources,reducing mutton sheep breeding costs,and improving mutton sheep breeding benefits.

A Comparison of Variable Selection by Tabu Search and Stepwise Regression with Multicollinearity Problem

This paper has compared variable selection method for multiple linear regression models that have both relative and non-relative variables in full model when predictor variables are highly correlated 0.999 . In this study two objective functions used in the Tabu Search are mean square error （MSE） and the mean absolute error （MAE）. The results of Tabu Search are compared with the results obtained by stepwise regression method based on the hit percentage criterion. The simulations cover the both cases, without and with multicollinearity problems. For each situation, 1,000 iterations are examined by applying a different sample size n = 25 and 100 at 0.05 level of significance. Without multicollinearity problem, the hit percentages of the stepwise regression method and Tabu Search using the objective function of MSE are almost the same but slightly higher than the Tabu Search using the objective function of MAE. However with multicollinearity problem the hit percentages of the Tabu Search using both objective functions are higher than the hit percentage of the stepwise regression method.

A Study on Influence of Predictor Multicollinearity on Performance of the Stepwise Regression Prediction Equation

The prediction accuracy of the traditional stepwise regression prediction equation（SRPE）is affected by the multicollinearity among its predictors.This paper introduces the condition number analysis into the prediction modeling to minimize the multicollinearity in the SRPE.In the condition number prediction modeling,the condition number is used to select the combination of predictors with the lowest multicollinearity from the possible combinations of a number of candidate predictors（variables）,and the selected combination is then used to construct the condition number regression prediction equation（CNRPE）.This novel prediction modeling is performed in typhoon track prediction,which is a difficult task among meteorological disaster predictions.Six pairs of typhoon track latitude/longitude SRPEs and CNRPEs for July,August,and September are built by employing the traditional and the novel prediction modeling approaches,respectively,and by using a large number of identical modeling samples.The comparative analysis indicates that under the condition of the same candidate predictors（variables）and predictands（dependent variables）,although the fitting accuracy of the novel prediction models used for the historical samples of South China Sea（SCS）typhoon tracks is slightly lower than that of the traditional prediction models,the prediction accuracy for the independent samples is obviously improved,with the averaged prediction error of the novel models for July,August,and September being 153.9 kin,which is 75.3 km smaller than that of the traditional models（a reduction of 33%）.This is because the novel prediction modeling effectively minimizes the multicollinearity by computation and analysis of the condition number.It is shown further that when F=1.0,2.0,and 3.0,the average prediction errors of the traditional SRPEs are obviously larger than those of the CNRPEs.Moreover,extremely large and unreasonable prediction errors occur at some individual points of the typhoon track predicted by the SRPEs due to the multicollinearity existing in the combination of predictors.

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.

Study on QSAR of Taxol and its Derivatives Based on Stepwise Multivariate Linear Regression Analysis

Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.

Stepwise multiple regressions application in liposome orthogonal experiments

Aim New statistical method was applied in data analysis of orthogonal experiments to optimize the preparation of liposome. Method Particle size, zeta potential, encapsulation efficiency and physical stability of liposomes were selected by orthogonal design as evaluating indicators. Through three statistical methods （direct observation, variance analysis and stepwise multiple regression）, the optimized preparing conditions were acquired and validated by experiment. Results All of the four indicators were different by these analyses. The validation experiments indicated that the optimized conditions by stepwise multiple regressions were better than that by traditional analysis. Conclusion Experiment results suggested that multiple regressions could avoid the weakness of direct observation and variance analysis, but more work should be done in preparing liposomes.

Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and Random Forest

Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.

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.

Variation characteristics of stable isotopes in atmospheric precipitation in Adelaide,Australia

Background,aim,and scope Stable isotope in water could respond sensitively to the variation of environment and be reserved in different geological archives,although they are scarce in the environment.And the methods derived from the stable isotope composition of water have been widely applied in researches on hydrometeorology,weather diagnosis,and paleoclimate reconstruction,which help well for understanding the water-cycle processes in one region.Here,it is aimed to explore the temporal changes of stable isotopes in precipitation from Adelaide,Australia and determine the influencing factors at different timescales.Materials and methods Based on the isotopic data of daily precipitation over four years collected in Adelaide,Australia,the variation characteristics of dailyδD,δ^(18)O,and dexcess in precipitation and its relationship with meteorological elements were analyzed.Results The results demonstrated the local meteoric water line(LMWL)in Adelaide,wasδD=6.38×δ^(18)O+6.68,with a gradient less than 8.There is a significant negative correlation between dailyδ^(18)O and precipitation amount or relative humidity at daily timescales in both the whole year and wither/summerhalf year(p<0.001),but a significant positive correlation between dailyδ^(18)O and temperature in the whole year and the winter half-year(p<0.001).Discussion The correlation coefficients betweenδ^(18)O and daily mean temperature didn’t show a significant positive correlation,which may be attributed to that the precipitation in Adelaide area in January was mainly influenced by strong convective weather,and the stable isotope values in precipitation were significantly negative.Furthermore,this propose was also evidenced by the results from dexcess of precipitation with larger value in the winter half-year than that in the summer half-year,which may be resulted from the precipitation events in winter are mostly influenced by oceanic water vapor,while the sources of water vapor in summer precipitation events are more complicated and influenced by strong convective weather.On the other hand,the slope and intercept of theδ^(18)O—P regression lines in the summer months(-0.41 and 0.50‰)are larger and smaller than those in the winter months(-0.22 and-2.15‰),respectively,indicating that the precipitation stable isotopes have a relatively stronger rainout effect in the summer months than in the winter months.Besides,the measured values ofδ^(18)O in daily precipitation have a good linear relationship with our simulated values ofδ^(18)O,demonstrating the established regression model could provide a reliable simulation for theδ^(18)O values in daily precipitation in Adelaide area.It’s worth noting that the precipitation events with low precipitation amount,low relative humidity and high temperature,usually had relatively small slope and intercept of MWL,implying that raindrops may be strongly affected by sub-cloud secondary evaporation in the falling process.Conclusions The variation ofδ^(18)O in daily precipitation from Adelaide region was controlled by different factors at different timescales.And the water vapor sources and the meteorological conditions of precipitation events(such as the degree of sub-cloud secondary evaporation)also played an important role on the variation ofδ^(18)O.Recommendations and perspectives Stable isotope in daily precipitation can provide more accurate information about water-cycle and atmosphere circulation,it is therefore necessary to continue to collect and analyze daily-scale precipitation data over a longer time span.The results of this study will provide the basis for the fields of hydrometeorology,meteorological diagnosis and paleoclimate reconstruction in Adelaide,Australia.

Genomic Selection for Frogeye Leaf Spot Resistance in Soybean

Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.

Analysis and Evaluation Indicator Selection of Chilling Tolerance of Different Cotton Genotypes

[Objectivc] This study aimed to investigate the chilling tolerance of seedlings of different cotton genotypes and screen appropriate indicators for assess- ing chilling tolerance, to establish reliable mathematical evaluation model for chilling tolerance of cotton, thus providing theoretical basis for breeding and promoting new chilling-tolerant cotton germplasms and large-scale evaluation of chilling tolerance of cotton varieties. [Method] Fifteen cotton varieties （lines） were used as experimental materials. The photosynthetic gas exchange parameters, chlorophyll fluorescence ki- netic parameters, chlorophyll content, relative soluble sugar content, malonaldehyde content, relative proiine content, relative conductivity and other 12 physiological indi- cators of seedling leaves under low temperature treatment （5 ℃, 12 h） and recovery treatment （25 ℃. 24 h） were determined; based on the chilling tolerance coefficient （CTC） of various individual indicators, the comprehensive evaluation of chilling toler- ance was conducled by using principal component analysis, hierarchical cluster anal- ysis and stepwise regression analysis. [Result] The results showed that the 12 indi- vidual physiological indicators could be classified into 7 independent comprehensive components by principal component analysis; 15 cotton varieties （lines） were clus- tered into three categories by using membership function method and hierarchical cluster analysis; the mathematical model for evaluating chilling tolerance of cotton seedlings was established： D =0.275 -0.244Fo1 ＋0.206Fv/Fm1＋0.326g,%-0.056SS ＋ 0.225MDA＋O.O38REC （FF=0.995）, and the evaluation accuracy of the equation was higher than 94.25%,0. Six identification indicators closely related to chilling tolerance were screened, including Fo,, Fv/Fm1, Seedling leaves of cotton varieties （lines） gs2, SS, MDA, and REC. [Conclusion] with high chilling tolerance are less dam- aged under low temperature stress, and are able to maintain relatively high photo- synthetic electron transport capacity and high stomatal conductance after recovery treatment, which is contributed to gas exchange and recovery of photosynthetic ca- pacity. Determination of the six indicators under the same stress condition can be adopted for rapid identification and prediction of the chilling tolerance of other cotton varieties, which provides basis for the breeding, promotion, identification and screen- ing of chilling tolerant germplasms.

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.

Impacts of COVID-19 pandemic on urban park visitation:a global analysis

The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally,as of 1 October 2020.During the lockdown and restrictions placed on public activities and gatherings,green spaces have become one of the only sources of resilience amidst the coronavirus pandemic,in part because of their positive effects on psychological,physical and social cohesion and spiritual wellness.This study analyzes the impacts of COVID-19 and government response policies to the pandemic on park visitation at global,regional and national levels and assesses the importance of parks during this global pandemic.The data we collected primarily from Google’s Community Mobility Reports and the Oxford Coronavirus Government Response Tracker.The results for most countries included in the analysis show that park visitation has increased since February 16th,2020 compared to visitor numbers prior to the COVID-19 pandemic.Restrictions on social gathering,movement,and the closure of workplace and indoor recreational places,are correlated with more visits to parks.Stay-at-home restrictions and government stringency index are negatively associated with park visits at a global scale.Demand from residents for parks and outdoor green spaces has increased since the outbreak began,and highlights the important role and benefits provided by parks,especially urban and community parks,under the COVID-19 pandemic.We provide recommendations for park managers and other decision-makers in terms of park management and planning during health crises,as well as for park design and development.In particular,parks could be utilized during pandemics to increase the physical and mental health and social well-being of individuals.

Responses of Antioxidant Enzymes to Chilling Stress in Tobacco Seedlings

Chilling is one of the major abiotic stresses limiting yield and quality of many important crops. For better understanding of chilling stress responses in tobacco （Nicotiana tabacum）, growth rate and antioxidant enzymes of seedlings in 2 tobacco cultivars, viz., MSk326 （chilling sensitive variety） and Honghuadajinyuan （HHDJY, chilling tolerant variety） at chilling temperature （5℃） were studied. The results showed that the relative growth rate in chilling period to that in recovery period was significantly higher in roots than that in shoots for both cultivars, suggesting that shoots growth was more easily affected by chilling stress. Chilling stress increased peroxidase （POD） activity and reduced superoxide dismutase （SOD） activity in shoots of HHDJY, and catalase （CAT） activity was little affected. In the roots of HHDJY, chilling stress increased SOD and CAT activities, and had little effect on POD activity. For MSk326, chilling treatment increased SOD activity in shoots and declined CAT activity in roots. MDA concentration in both varieties was increased under the chilling stress, while it was decreased after seedlings were recovered growth for 4 d at normal temperature （25℃）. It showed that tobacco seedlings might have the capacity of recovering from chilling injury for a short term, The relationship between the growth rate and antioxidant enzyme activity was analyzed by stepwise regression. It was found that there was a close relationship between relative growth rate of tobacco seedlings and CAT activity under chilling stress condition and regression equations containing CAT could be used in predicting seedling growth rate of tobacco under chilling stress condition.

Optimization of tensile strength for new type acetone-urea-formaldehyde furan resin using uniform design

In this study,the 24 h tensile strength of new type acetone-urea-formaldehyde furan resin (nitrogen content 3%) was investigated by uniform design optimization.Four independent variables such as acetone:formaldehyde molar ratio (mol/mol),solution pH value,reaction temperature (℃) and reaction time (min) were considered in the experiments.U13(134) uniform design was employed and the equation of 24 h tensile strength model was obtained after 13 experimentations.The 24 h tensile strength was optimized by applying single factor experiments and stepwise non-linear regression analysis.Minitab (Minitab 15 trial version) and MATLAB (R2010a trial version) were used for data analysis.The t-value and p-value indicate that the major impact factors include the interaction effect of solution pH value and reaction temperature (X2X3),the linear terms of acetone:formaldehyde molar ratio (X1),reaction time (X4) followed by the square effects of acetone/formaldehyde molar ratio (X1X1).The optimized results were achieved with the acetone:formaldehyde molar ratio (mol/mol) at 3:1,solution pH value at 6.0,reaction temperature at 70℃,and reaction time at 140 min,respectively.This method can not only significantly reduce the number and cost of the tests,but also provide a good experimental design strategy for the development of furan resin.The investigation shows that the predicted results of 24 h tensile strength are consistent well with the experimental ones.

Comparison of Long-Term Forecasting of June-August Rainfall over Changjiang-Huaihe Valley

In terms of an Artificial Neural Network (ANN) established is a long-term prediction model for June-August flood/drought in the Changjiang-Huaihe Basins and a regression forecasting expression is formulated with the aid of the same factors and sample size for comparison. Results show that the ANN is superior in predictions and fittings due to its higher self-adaptive learning recognition and nonlinear mapping especially in the years of severe flood and drought. This shows great promise in using ANN in the research of flood/drought prediction on a long-range basis.