APPLICATIONS OF SPECTRAL ANALYSIS

The basic concepts and application of spectral analysis are explained. Stationary time series and autocorrelation are first defined. Autocorrelation is related to the familiar concepts of variance and covariance. The use of autocorrelation analysis is explained in estimating the interdependent relationship of a time series over discrete time lags. In order to measure the behavior of the time series using autocorrelation, it would be necessary to examine a very large number of autocorrelation lags. Alternatively, the technique of Fourier analysis can be used to transform the autocorrelation function of the time series into a continuous function, termed a spectrum. The spectrum has a one to one correspondence to the autocorrelation for the time series and has the advantage of representing all possible autocorrelations over the discrete time lags. The spectrum can then be examined as a measure of the behavior of the time series. Spectral analysis indicates the reliability of the analysis of autocorrelated variables when familiar statistical techniques such as sample means and variances are used. The application of spectral analysis to management science problems in three general areas is illustrated: (1) inventory demand, (2) transportation simulation, and (3) stock market price behavior. Spectral analysis was used to detect cycles and trends in the data. Analyses were focused on the spectrum which provides a measure of the relative contribution of cycles in a band of frequencies to the total variance of the data.     

为中国人民服务

我来自加拿大安大略省的多伦多市,相信大家对这个地方并不陌生,它是大山的家乡,白求恩医生也在此出生并接受医疗培训。我所在的约克大学就有一个本科生学院是以白求恩医生的名字命名的。白求恩医生的事迹感召着我们．我们每一位获奖外国专家都将永远为我们有机会“为人民服务”,为中国人民服务而心存感激,这是我们无上的荣耀。     

USING DUMMY VARIABLES TO CHECK FOR ROUNDING ERROR IN COMPUTERIZED REGRESSION PROGRAMS*

After a brief review of the role of dummy variables in regression analysis and the current state-of-the art in rounding/truncation error detection in computerized least squares programs, this paper presents a theorem that can be used to detect this type of error whenever an analyst is running a regression program that has one (or more) dummy variables as independent variables.     

REGRESSION METHODOLOGY WITH GROSS OBSERVATION ERRORS IN THE EXPLANATORY VARIABLES***

The robustness of linear programming regression estimators is examined where the disturbance terms are normally distributed and there are observation errors in the explanatory variables. These errors are occasional gross biases between one set of observations and another. The simulation of short series data offers preliminary evidence that when these biases have a non-zero mean, MSAE estimation is more robust than least squares.     

THE EFFECTS OF INTERDISCIPLINARY EDUCATIONAL FORMATS ON CONCEPT RETENTION AND PROBLEM–SOLVING ABILITY*

The purpose of this study was to examine the effects of different interdisciplinary problem-oriented formats on a student's knowledge of and retention of disciplinary concepts and principles, his application of these concepts and principles to disciplinary problems, and his use of a discipline in his analysis of a complex problem. Performances of students enrolled in the experimental sections were compared to those of students enrolled in standard lecture-discussion sections. Regression analysis was used to analyze students' performance in order to control the effects of differences in student backgrounds. It was found that altering the pedagogical format and reward system within the experimental sections had no significant effect upon student performances in the experimental, problem-oriented program. In general, this study indicates that students taught in the standard lecture-discussion format in which grades are determined by examination over course material retain concepts and principles, apply these concepts and principles to disciplinary problems, and integrate disciplinary concepts into their analysis of complex problems as well as, if not better than, students taught in an interdisciplinary, problem-solving format.     

EXPLORING MARKET PRICING STRATEGIES VIA DYNAMIC PROGRAMMING

Price determinants as well as strategies can be studies by use of simulation, particularly if cost and price relationships can be related to market activity [1] [9] [11]. But, through the use of dynamic programming, given the market conditions, one can extend the analysis to include an optimal strategy. This paper describes a dynamic programming approach to studying price strategy. A model is developed to show that in a market characterized by cost/volume and price/volume relationships, profitability can be extended beyond that resulting from a dominant market strategy to an optimal maximizing strategy. Extension of the model is suggested for studying (a) sensitivity of a strategy (solution) to price level and cost changes, (b) optimal timing of withdrawal, and (c) present value analysis.     

DETERMINING OPTIMAL SHIFT SCHEDULES FOR TELEPHONE TRAFFIC EXCHANGE OPERATORS

A branch and bound algorithm is presented for determining the minimum number of telephone operators, and their shift schedules, required to meet demand that varies over a 24 hour operating period. An integer linear programming formulation is used, and the algorithm is described in terms of its separation, relaxation, fathoming, and branching procedures. Computational results are provided, using actual operating data. The results indicate that practical sized problems can be solved by the algorithm, involving as many as 100 different shift type variables and demand profiles typical of those encountered in many telephone traffic exchanges.     

You Wouldn't Like Me When I'm Angry: List Experiment Misreporting*

Objective. List experiment respondents may misreport the number of list items that they associate with in order to associate themselves with a socially desirable test item or to disassociate themselves from a socially undesirable test item. Tests for such misreporting were conducted. Methods. List experiments from the 1991 National Race and Politics Survey, the 2006 Cooperative Congressional Election Study, and the 2008 Cooperative Campaign Analysis Project were analyzed or reviewed. Results. Evidence suggested that some respondents deflated their report more than necessary to avoid association with a socially undesirable test item. Conclusions. List experiments may provide inaccurate estimates of the percentage of the population to which the test item applies, but the direction of bias is predictable.