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 Welcome to the OSU Department of Statistics

The Statistics Department at Oklahoma State University is the only statistics department in the state of Oklahoma.  The department offers courses in both undergraduate and graduate degree programs.  Our graduates are in demand in the areas of:  education, actuarial science, health care, agriculture research, marketing research, money and banking and many more disciplines.

OSU Department of Statistics
301 MSCS (Math Statistics and Computer Sciences)
Stillwater, OK  74078-1056
Office:  405-744-5684
FAX:  405-744-3533

Position Announcement:  Lecturer Position - Beginning August 2018 

Interested in a Statistics degree from OSU?

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Seminar:  Friday, April 27, 2018


MSCS 310

Identify risk factors associated with HCV mono-infection, HIV mono-infection and HIVHCV co-infection in the southwest of China

Biting Zhou

Acquired Immune Deficiency Syndrome (AIDS) and Hepatitis C are global health problems of unprecedented scale. Moreover, many reports indicate that Hepatitis C virus (HCV) has a higher infection rate among people who lives with Human immunodeficiency virus (HIV). Thus there raises a need to better understanding of risk factors associated with each infection and their co-infection. The aim of this report is to identify risk factors for HCV mono-infection, HIV mono-infection and HIVHCV co-infection among population in the southwest of China, where it is an AIDS epidemic area and also one of the largest areas inhabited by the Yi people. A data set of 3576 enrollees in the Methadone Maintenance Treatment Clinic (MMT) between 2004 and 2012 was utilized. We used univariate associate test and multiple logistic regression model to identify the risk factors for each type of infection. The results indicate that in the studied area the HCV mono-infection rate is associated with gender, age, methods of drug abusing for 6 months, drug injection, frequency of drug usage per day, occupation, and HIV infection status; HIV mono-infection rate is associated with gender, age of initial drug usage, duration of drug usage, drug injection, education, occupation, and HCV infection status; The HIVHCV co-infection is highly associated with drug injection.  Knowing the potential risks in turn helps design more effective risk mitigation strategies to reduce the risk of infection in the area under the study.


A Comparison:  The Two-Sample Kolmogorov- Smirnov, Cramer-Von Mises, and Anderson-Darling Tests

Jana Alford

The objective of this paper is to compare the two-sample Kolmogorov-Smirnov, Cramer-Von Mises, and Anderson Darling test statistics. We wish to make conclusions on the performance of each test statistic under varying conditions, so that researchers can choose which test to use based on these specific conditions. Random samples following three distributions were examined with different shape, location, and scale parameters. We first determined the minimum sample size needed to achieve a predetermined Type I error rate for each test statistic. We then compared the power of each test statistic at a common sample size to determine which will have the greatest power.


A Simulation Study on Coherence Estimation of Time Series 

Youngjun Chae 

This report investigates the estimation of spectral coherence between two time series via simulation studies. The coherence estimation depends on both the univariate and bivariate spectral density estimation. The purpose of the study is to compare different kernel smoothing methods for correlated autoregressive time series. The coherence estimation is important for studies in climate change, when the temporal relationship between climate variables is of interest. An example using Oklahoma observatory data is illustrated.



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