审计统计软件使用中应注意的事项

审计统计工作在反映审计工作成果，促进审计工作管理和服务领导宏观决策等方面发挥了积极有效的作用。现行审计统计软件的正确、熟练应用大大提高了统计数据报送的及时性、准确性和真实性。为了把审计统计工作做得更好，切实提高审计统计质量，     

雅安市加强审计信息化建设见成效

近年来，雅安市审计局在信息化建设中，认真规划、注重建设、强化人员培训、加大运用，并已取得成效。该局已人均拥有1台电脑，组建了单位局域网，接通党政网，实现了宽带上网，加大外资平台、金融审计软件、乡镇负债审计调整审计软件、企业普查统计软件、审计统计台账与报表软件等审计软件的应用，大大提高了全体审计人员的工作效率。     

统计软件在《概率论与数理统计》教学中的应用

分析了统计软件应用到《概率论与数理统计》教学中的优点，举例介绍了SPSS软件在《概率论与数理统计》教学中的应用，说明了统计软件应用于《概率论与数理统计》教学的可行性。     

上海24家房地产上市公司绩效评价引发思考

利用多元统计中的因子分析和聚类分析方法，应用SPSS统计分析软件，以沪市房地产行业24家上市公司为样本，选取2005年度财务报表中能够反映上市公司绩效水平的13项指标，对房地产行业上市公司的绩效水平进行分析评价。     

优化《统计学》课程建设对策研究

《统计学》是一门具有成熟理论和完善方法体系．并广泛应用于社会和自然等领域的应用型学科。掌握统计学数量分析方法．熟练应用统计软件．对客观现象进行定量分析是高素质人才必备的品质之一．因此．国内各高等院校都比较重视统计学课程教学与改革。为此，选用优秀的教材、精选的参考书。建立完善的统计题库。编制优秀的教学课件．应用成熟的统计...     

顾客满意度统计分析的软件应用

用Excel的计算和函数功能可以方便地对顾客满意度调查函的各项指标打分情况进行快速正确的统计计算，从而避免了人工统计的繁琐和失误．同时应用质量、过程统计分析软件Minitab对统计结果进行分析，可减少大量的数据计算，并且有方便准确的特点，同时可绘制出专业美观的图形。     

QC小组活动的统计方法应用

近几年，中国质量协会加强了对于QC小组注重数据分析，正确并恰当地运用统计工具方向的引导，QC小组在活动中应用统计方法的意识明显增强，分析问题和解决问题的能力不断提高，取得了一定成效。但从全国QC成果发表赛的整体表现看，能正确应用统计工具的小组普及性还不够，一些小组仍不掌握基本统计方法的应用，     

Excel在统计学实践教学中的应用

笔者结合实际教学过程,将统计学与计算机技术有机地结合起来,运用Excel软件的相关功能对统计学的教学内容、教学方法以及考试方法进行了必要的教学改革,详细阐述了统计软件Excel在高校统计学实践教学中的应用。     

钢结构绘图软件GJGSJ

VBA是一个万能开发工具。用VBA开发的《GJGSJ1．0》钢结构绘图软件，使型钢截面绘图，钢构件统计汇总非常方便。文中介绍了《GJGSJ1．0》软件在钢结构施工图绘制中的应用。     

Excell在控制图中的应用

前言控制图是企业生产过程中常用的统计质量控制方法．Excell电子表格软件提供了丰富的数理统计函数和强大的图表功能,本文介绍了如何应用 Excell内嵌函数和图表功能来作质量控制图。     

Using R to teach econometrics

R, an open‐source programming environment for data analysis and graphics, has in only a decade grown to become a de‐facto standard for statistical analysis against which many popular commercial programs may be measured. The use of R for the teaching of econometric methods is appealing. It provides cutting‐edge statistical methods which are, by R's open‐source nature, available immediately. The software is stable, available at no cost, and exists for a number of platforms, including various flavours of Unix and Linux, Windows (9x/NT/2000), and the MacOS. Manuals are also available for download at no cost, and there is extensive on‐line information for the novice user. This review focuses on using R for teaching econometrics. Since R is an extremely powerful environment, this review should also be of interest to researchers. Copyright © 2002 John Wiley & Sons, Ltd.     

Use of Statistics in the Monetary Policy of the Czech National Bank: The Case of a Country in Transition

In the presented text the authors judge the importance of statistics in the monetary policy of the Czech National Bank (CNB) over the course of the economic transformation process, with particular consideration of changing statistical needs and the possibilities and limits of statistical data exploitation in the monetary analyses. The importance of statistics lies on the level of collection and processing of statistical information and on the level of use of statistical methods to analyse data. Since the start of the 1990s the requirements for statistics were significantly influenced by monetary policy. In the period 1990–1997, monetary targeting was the primary influential factor. Since 1998, the monetary policy is influenced by inflation targeting. Statistical priorities switched from monetary data to economy and financial market data. Much progress has been made in the use of statistical methods for analysing data. Statistics available at present cover the CNB's standard monetary-policy requirements and are on par with those in developed countries. Its further development will reflect the standard changes taking place in the more advanced countries.     

Narrowing the scope of failure prediction using targeted fault load injection

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend the focus of traditional fault injection techniques to predict failure in the Microsoft enterprise authentication service and Apache web server. These new fault loads were successful in creating failure conditions that were identifiable using statistical learning methods, with fewer irrelevant faults being created.     

Polytomous logistic regression

In this paper a review will be given of some methods available for modelling relationships between categorical response variables and explanatory variables. These methods are all classed under the name polytomous logistic regression (PLR). Models for PLR will be presented and compared; model parameters will be tested and estimated by weighted least squares and by likelihood. Usually, software is needed for computation, and available statistical software is reported.
An industrial problem is solved to some extent as an example to illustrate the use of PLR. The paper is concluded by a discussion on the various PLR-methods and some topics that need a further study are mentioned.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

Excel在岩土勘察指标统计中的应用

为了克服在利用专业勘察软件进行岩土物理力学性质指标进行分层统计时,要让有的数据不参加统计计算时操作不便、统计结果不能立即显示,统计结果如不合适又得重头再来一遍的缺点,将专业勘察软件生成的分层数据导入Excel中,采用Excel方法进行各种指标分层统计计算,形成Excel模板后可多次重复使用,经实际使用,计算过程透明、结果同步显示、数据准确,可在工作中应用。     

Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients

The use of joint modelling approaches is becoming increasingly popular when an association exists between survival and longitudinal processes. Widely recognized for their gain in efficiency, joint models also offer a reduction in bias compared with naïve methods. With the increasing popularity comes a constantly expanding literature on joint modelling approaches. The aim of this paper is to give an overview of recent literature relating to joint models, in particular those that focus on the time‐to‐event survival process. A discussion is provided on the range of survival submodels that have been implemented in a joint modelling framework. A particular focus is given to the recent advancements in software used to build these models. Illustrated through the use of two different real‐life data examples that focus on the survival of end‐stage renal disease patients, the use of the JM and joineR packages within R are demonstrated. The possible future direction for this field of research is also discussed.     

OR vice: Forecasting and classification techniques for narcotics interdiction

Counternarcotics interdiction efforts have traditionally relied on historically determined sorting criteria or “best guess” to find and classify suspected smuggling traffic. We present a more quantitative approach which incorporates customized database applications, graphics software and statistical modeling techniques to develop forecasting and classification models. Preliminary results show that statistical methodology can improve interdiction rates and reduce forecast error. The idea of predictive modeling is thus gaining support in the counterdrug community. The problem is divided into sea, air and land forecasting, only part of which will be addressed here. The maritime problem is solved using multiple regression in lieu of multivariate time series. This model predicts illegal boat counts by behavior and geographic region. We developed support software to present the forecasts and to automate the process of performing periodic model updates. During the period, the model was in use at. Coast Guard Headquarters. Because of deterrence provided by improved intervention, the vessel seizure rate declined from 1 every 36 hours to 1 every 6 months. Due in part to the success of the sea model, the maritime movement of marijuana has ceased to be a major threat. The air problem is more complex, and required us to locally design data collection and display software. Intelligence analysts are using a customized relational database application with a map overlay to perform visual pattern recognition of smuggling routes. We are solving the modeling portion of the air problem using multiple regression for regional forecasts of traffic density, and discriminant analysis to develop tactical models that classify “good guys” and “bad guys”. The air models are still under development, but we discuss some modeling considerations and preliminary results. The land problem is even more difficult, and data collection is still in progress.     

Semiparametric Regression Analysis of Panel Count Data: A Practical Review

Panel count data arise in many applications when the event history of a recurrent event process is only examined at a sequence of discrete time points. In spite of the recent methodological developments, the availability of their software implementations has been rather limited. Focusing on a practical setting where the effects of some time‐independent covariates on the recurrent events are of primary interest, we review semiparametric regression modelling approaches for panel count data that have been implemented in R package spef . The methods are grouped into two categories depending on whether the examination times are associated with the recurrent event process after conditioning on covariates. The reviewed methods are illustrated with a subset of the data from a skin cancer clinical trial.