浅析企业的培训需求

培训,是在学校教育、社会教育之后的第三种教育方式,通常在工作中获得。培训就是使受训人员通过有计划的、连续的系统学习而获得知识、技能、态度,乃至行为的定向改进过程,以使其能够按照预期的标准或水平完成所承担或将要承担的工作任务。随着社会的飞速发展,作为培训活动首要环节的培训需求,在培训中的地位愈加凸显。

Modeling household car ownership using ordered logistic regression model

Considering both the discrete and ordered nature of the household car ownership an ordered logistic regression model to predict household car ownership is established by using the data of Nanjing Household Travel Survey in the year 2012. The model results show that some household characteristics such as the number of driver licenses household income and home location are significant.Yet the intersection density indicating the street patterns of home location and the dummy near the subway and the bus stop density indicating the transit accessibility of home location are insignificant.The model estimation obtains a good γ2 the goodness of fit of the model and the model validation also shows a good performance in prediction.The marginal effects of all the significant explanatory variables are calculated to quantify the odds change in the household car ownership following a one-unit change in the explanatory variables.

Analysis of passenger boarding time difference between adults and seniors based on smart card data

As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.

Three Critical Issues in Sustainable Development Studies and the Transition Development of China

The core issue for China transition development in the next 30 years is to shift from the quantity growth model to the quality improvement model. The paper introduces the research progress in three key areas of sustainable development studies since the 1990s. It is pointed out that there is a well-being thresh-old at which the margin utility of economic growth for human well-being will decline, that there is an ecological limit beyond which more economic growth in terms of physical scale will be impossible, and that the creation of human well-being is related not only to the amount but also to the structure and efficiency of public expenditure from government. After an in-depth discussion on facts, origins and policy implications of each issue, some theory and policy thinking with long-lasting significance are raised for the transition development of China.

An Extended Bailey-welch Rule with No-show and Walk-in For Outpatient Appointment Scheduling

In clinic＇s appointment scheduling system no-shows have been a significant and confirmed issue with a bad influence on patient accessibility and clinic efficiency. The problem of walk-in has often been seen as the opposite of no-show problem. In this work we revisit a walk-in admitting based approach to mitigate the bad influence of no-show without overbooking. First we establish a model which utilizes marginal benefit objective function to balance the interests of the clinic, the patient and the doctor, we prove that no-show and walk-in cancels out each other straightly has a bad property. Then we propose a new rule which is an extension of the well-known Bailey - Welch rule, the simulation results show that our rule has an improvement comparing with the common rule that cancels them out straightly.

The Impact of Environmental Regulation on FDI Spiilover Effects： A Cross-Provincial Study

Using correlated data from thirty Chinese provinces for the years between 2000-2009, this paper examines the impact of FDI spillover and environmental regulation on the progress of industrial technology in China as well as the impact of environmental regulation on the marginal effect of FDI. Empirical results show that while FDI spillover has a negative effect, enhanced environmental regulation has a positive effect. Environmental regulation also has a significant impact on the marginal effect of FDI spillover on industrial technology development. Separate studies on state-owned and private enterprises suggest that environmental regulation has a heterogeneous effect on industrial technology progress and the marginal effect of FDI spillover on industrial technology progress.

A selective overview of feature screening for ultrahigh-dimensional data

High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.

Non-Relativistic Phase Shifts for Scattering on Generalized Radial Yukawa Potentials

Non-relativistic phase shifts for a generalized Yukawa potential V(r) =-V_0( e^(-αr)/r)-V_1( e^(-2αr)/r^2) are studied by the amplitude-phase method and by a frequently used analytic method based on a Pekeris-type approximation of power-law potential terms.Small variations of V_1 seem to have marginal effects on the effective potential and on exact phase shifts.However,as pointed out in this study,a Pekeris-type approximation in scattering applications often implies serious distortions of both effective potentials and phase shifts.The Pekeris-type based analytic approximation in this study seems to give low-quality scattering results for this model potential at low energies.

glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models

Generalized linear mixed models(GLMMs)have been widely used in contemporary ecology studies.However,determination of the relative importance of collinear predictors(i.e.fixed effects)to response variables is one of the challenges in GLMMs.Here,we developed a novel R package,glmm.hp,to decompose marginal R2^(2)explained by fixed effects in GLMMs.The algorithm of glmm.hp is based on the recently proposed approach‘average shared variance’i.e.used for multivariate analysis.We explained the principle and demonstrated the use of this package by simulated dataset.The output of glmm.hp shows individual marginal R2^(2)s that can be used to evaluate the relative importance of predictors,which sums up to the overall marginal R2^(2).Overall,we believe the glmm.hp package will be helpful in the interpretation of GLMM outcomes.