Charts and a pen

Master of
Applied Statistics

Program summary

Gain hands-on experience with the latest innovative software programs and study curriculum designed by experts in the field. This online program can help you develop data analysis skills and explore applied statistics without delving too deeply into the foundations of mathematical statistics.

Application deadline

Apply by April 1 to start May 13

Credits and costs

30 Credits $1,017 per credit

A Master of Applied Statistics Degree Can Help Advance Your Career

The demand for trained statisticians continues to increase as the world becomes more dependent on predictive data and numerical reasoning. With a Master of Applied Statistics degree you can advance your career in almost any field, including education, science, technology, health care, government, or business.

To help you gain the necessary credentials to progress in this flourishing field, Penn State World Campus has partnered with Penn State's Eberly College of Science to offer an online professional Master of Applied Statistics degree. 

Why a Master of Applied Statistics Online at Penn State

If you handle data as a professional and want to conveniently study a wide range of statistical application areas, our online Master of Applied Statistics program could be for you. The online degree is based on the highly regarded resident program and taught by many of the same faculty. The requirements for both the online and resident Master of Applied Statistics programs are identical.

The expertly designed curriculum enables you to use industry-standard software such as Minitab, R, Python, and SAS to improve your data analysis proficiency. In two to five years you can complete the degree, selecting from courses covering a variety of statistical applications areas, including:

  • data mining
  • predictive analytics
  • biostatistics techniques
  • statistical consulting

Students who successfully complete the master's program have the option to prepare for the SAS Base Programming Certification Exam, or to seek PStat® accreditation through the American Statistical Association as an Accredited Professional StatisticianTM.

Choose the Online Applied Statistics Graduate Program That Fulfills Your Goals

Penn State offers both a master's degree and a graduate certificate online in applied statistics.

Master of Applied Statistics
The master's program is designed to help you develop your data-analytic skills and explores the core areas of applied statistics (DOE, ANOVA, Analysis of Discrete Data, MANOVA, and many more) — without delving too deeply into the foundations of mathematical statistics.

Graduate Certificate in Applied Statistics
The certificate program consists of 12 credits designed to help professionals from various backgrounds improve their data-analytic skills.

Application criteria for the Master of Applied Statistics degree are more rigorous than for the graduate certificate. Based on your application portfolio, if you are at the borderline of admission into the master's program, preference will be given to applicants who have demonstrated excellent performance in the completion of the certificate program. If you are accepted into the master's program, the credits you earn in the certificate program will be applied to the master's degree.  

Who Should Apply?

If you want to hone your ability to make data-driven decisions, understand predictive analytics, and apply data science to achieve results in your field — whether in business, education, health care, science, government, or technology — the World Campus Master of Applied Statistics could be right for you.

Courses

This 30-credit master's program can be completed in two to five years, depending on whether you take one or two courses each semester. The goal is to provide graduates with broad knowledge in a wide range of statistical application areas — and the employable skills in statistics that are now in high demand.

Most courses within the Applied Statistics program are also available as individual courses for those looking to fulfill continuing professional development requirements. Read the instructions for how to register for Penn State World Campus courses to learn how you can enroll in any of the upcoming classes on an individual basis.

Build Your Professional Network

Your fellow students will have bachelor's degrees in agricultural, biological, business, computer, engineering, mathematical, physical or social sciences, and other related fields. The online courses are highly interactive and collaborative, allowing you to build strong ties with others and gain perspectives from other disciplines and industries.

Of the 30 credits required to graduate, 24 must be courses from the statistics department, and 21 must be at the 500 level. A minimum grade-point average of 3.0 is also required for graduation.

Required Courses (15 credits)

  • 3
    credits

    Probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems.

    • Prerequisite

      Statistics — STAT 500 and STAT 501 strongly recommended. Math — A standard three-course calculus sequence (for example, MATH 140, MATH 141, and MATH 230) and knowledge of matrix algebra and linear algebra (similar to MATH 220). If taken more than a few years ago, students are strongly encouraged to review their calculus knowledge.

  • 3
    credits

    A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests.

    • Prerequisite

      STAT 414

  • 3
    credits

    Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.

  • 3
    credits

    Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.

    • Prerequisite

      STAT 462 or STAT 501

  • 2
    credits

    General principles of statistical consulting and statistical consulting experience. Preparation of reports, presentations, and communication aspects of consulting are discussed.

    • Prerequisite

      STAT 502 and (STAT 505 or STAT 508 or STAT 557) and (STAT 503 or STAT 504 or STAT 506 or STAT 510)

  • 1
    credit

    Statistical consulting experience including client meetings, development of recommendation reports, and discussion of consulting solutions.

    • Prerequisite

      STAT 580

Elective Courses (15 credits)

  • 3
    credits

    Tests based on nominal and ordinal data for both related and independent samples. Chi-square tests, correlation.

    • Prerequisite

      STAT 200, STAT 220, STAT 240, STAT 250, STAT 301, or STAT 401

  • 1
    credit

    Selection and evaluation of statistical computer packages.

    • Prerequisite

      3 credits in statistics

  • 1
    credit

    Intermediate SAS for data management.

    • Prerequisite

      STAT 480

  • 1
    credit

    This course covers advanced statistical procedures in SAS, including ANOVA, GIM, CORR, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, MIXED, GRAPH, EXPORT, and SQL.

    • Prerequisite

      STAT 480 and STAT 481

  • 3
    credits

    Introduction, intermediate, and advanced topics in SAS.

    • Prerequisite

      3 credits in statistics

    • Note

      Credit cannot be received for both STAT 483 and STAT 480/481/482.

  • 1
    credit

    Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics.

  • 1
    credit

    Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics. R is a popular tool for statistical analysis and research used by a growing number of data analysts inside corporations and academia.

    • Enforced Concurrent at Enrollment

      STAT 484

  • 2
    credits

    Due to the pervasiveness of Python as a statistical analysis tool, there is a demand for statisticians to learn Python to perform descriptive and inferential data analysis. The course will take a case study approach to introduce students to Python. Students will learn to work with complex data using Python and will get hands-on experience on how to use Python to conduct statistical analyses.

    • Enforced Prerequisite at Enrollment

      STAT 300 or STAT 460 or STAT 461 or STAT 462 or STAT 500

  • 3
    credits

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

    • Prerequisite

      one undergraduate course in statistics

  • 3
    credits

    Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.

    • Prerequisite

      STAT 501 or STAT 502

  • 3
    credits

    Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher- way tables; latent and logistic models.

  • 3
    credits

    Analysis of multivariate data; T2-tests; particle correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations.

    • Prerequisite

      STAT 501, STAT 502

  • 3
    credits

    Theory and application of sampling from finite populations.

    • Prerequisite

      calculus, 3 credits in statistics (STAT 500 is recommended)

  • 3
    credits

    Research and quantitative methods for analysis of epidemiologic observational studies. Non-randomized, intervention studies for human health, and disease treatment.

    • Prerequisite

      3 credits in statistics, STAT 250 or equivalent

  • 3
    credits

    Data mining tools are exploring data with regression, PCA, discriminate analysis, cluster analysis, and classification and regression trees (CART).

    • Prerequisite

      STAT 501 or a similar course that covers analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression

  • 3
    credits

    The objective of the course is to introduce students to the various design and statistical analysis issues in biomedical research. This is intended as a survey course covering a wide variety of topics in clinical trials, bioequivalence trials, toxicological experiments, and epidemiological studies.

    • Prerequisite

      STAT 500

  • 3
    credits

    Identification of models for empirical data collected over time. Use of models in forecasting.

    • Prerequisite

      STAT 462 or STAT 501 or STAT 511

  • 3
    credits

    Statistical Analysis of High Throughput Biology Experiments.

Course Availability

If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.

Costs and Financial Aid

Graduate Tuition

Graduate tuition is calculated based on the number of credits for which you register. Tuition is due shortly after each semester begins and rates are assessed every semester of enrollment.

2023–24 Academic Year Rates

Tuition rates for the fall 2023, spring 2024, and summer 2024 semesters.

How many credits do you plan to take per semester? Cost
11 or fewer $1,017 per credit
12 or more $12,203 per semester

2024–25 Academic Year Rates

Tuition rates for the fall 2024, spring 2025, and summer 2025 semesters.

How many credits do you plan to take per semester? Cost
11 or fewer $1,027 per credit
12 or more $12,325 per semester

Financial Aid and Military Benefits

Some students may qualify for financial aid. Take the time to research financial aid, scholarships, and payment options as you prepare to apply. Military service members, veterans, and their spouses or dependents should explore these potential military education benefits and financial aid opportunities, as well.

How to Apply

Deadlines and Important Dates

Your degree application, including receipt of all transcripts, should be received by the following deadlines to be considered complete.

  • Summer DeadlineApply by April 1 to start May 13
  • Fall DeadlineApply by July 1 to start August 26
  • Spring DeadlineApply by November 1, 2024, to start January 13, 2025

Admissions Help

If you have questions about the admissions process, contact our admissions counselors.

Admission Requirements 

For admission to the Graduate School, an applicant must hold either (1) a baccalaureate degree from a regionally accredited U.S. institution or (2) a tertiary (postsecondary) degree that is deemed comparable to a four-year bachelor's degree from a regionally accredited U.S. institution. This degree must be from an officially recognized degree-granting institution in the country in which it operates.

The math prerequisite for this program is a standard three-course calculus sequence (for example, MATH 140, MATH 141, and MATH 230) and knowledge of matrix and linear algebra.

Transfer Credit

You may transfer up to 10 graduate credits from another accredited program into the degree program, with approval.

What You Need

Applications are submitted electronically and include a nonrefundable application fee. You will need to upload the following items as part of your application:

Official transcripts from each institution attended, regardless of the number of credits or semesters completed. Transcripts not in English must be accompanied by a certified translation. Penn State alumni do not need to request transcripts for credits earned at Penn State, but must list Penn State as part of your academic history. If you are admitted, you will be asked to send an additional official transcript. You will receive instructions at that time.

GPA and Test Scores — A minimum undergraduate GPA of 3.0 overall (on a 4.0 scale) is required.

GRE/GMAT scores are not required.

English Proficiency — The language of instruction at Penn State is English. With some exceptions, international applicants must take and submit scores for the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS). Minimum test scores and exceptions are found in the English Proficiency section on the Graduate School's "Requirements for Graduate Admission" page. Visit the TOEFL website for testing information. Penn State's institutional code is 2660.

References (3) — You will need to initiate the process through the online application by entering names, email addresses, and mailing addresses of three references. Upon submission of your application, an email will be sent to each recommender requesting they complete a brief online recommendation regarding your commitment for success in an online program. Please inform all recommenders they must submit the form in order for your application to be complete. 

Program-Specific Questions/Materials

Personal Statement — Describe your motivation for undertaking graduate studies in statistics.

Start Your Application

You can begin your online application at any time. Your progress within the online application system will be saved as you go, allowing you to return at any point as you gather additional information and required materials. When all materials have been received, we will notify you about your status and provide guidance about the next steps in becoming a Penn State student.

Begin the graduate school application

  • Choose Enrollment Type: "Degree Admission"
  • Choose "WORLD CAMPUS" as the campus

Checking Your Status

You can check the status of your application by using the same login information established for the online application form.

Technical Requirements 

Review the technical requirements for this degree program.

For courses where submitted work or examples involve Minitab or SAS software, students are strongly recommended to have access to a computer running a Windows operating system (as opposed to Mac OS).

Ready to Learn More?

Get the resources you need to make informed decisions about your education. Request information on this program and other programs of interest by completing this form.

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Ready to take the next step toward your Penn State master's degree?

Apply by April 1 to start May 13. How to Apply

Start or Advance Your Career

Two business professionals reviewing numbers

As a statistician with a master's degree and the support of Penn State career resources, you can promote your skill set in data science, business, industry, government, health care, and educational and research organizations.


Job Titles Related to This Degree

The following roles are often held by people with this type of degree:

  • Data Analyst
  • Statistical Analyst
  • Statistical Reporting Analyst
  • Statistician

Employment Outlook for Occupational Fields Related to This Degree

Estimates of employment growth and total employment are provided by the U.S. Bureau of Labor Statistics and are subject to change. While these occupations are often pursued by graduates with this degree, individual outcomes may vary depending on a variety of factors. Penn State World Campus cannot guarantee employment in a given occupation.

Data Scientists

35.8%
employment growth (10 years)
159,630
total employment

Statisticians

32.7%
employment growth (10 years)
30,780
total employment

Career Services to Set You Up for Success

Student having a virtual meeting on a laptop with a career counselor

From the day you're accepted as a student, you can access resources and tools provided by Penn State World Campus Career Services to further your career. These resources are beneficial whether you're searching for a job or advancing in an established career.

  • Opportunities to connect with employers
  • Career counselor/coach support
  • Occupation and salary information
  • Internships
  • Graduate school resources 

Earn a Valuable Credential along the Way

A figure walking on a path that includes a certificate part of the way through their progress

Show mastery of specific subjects before your degree is complete. Thanks to shared courses across programs, students can often earn a certificate along with their degree in less time than if they earned them separately.

Certificate Program Related to This Degree

The following certificate can be earned while completing this degree program:

Strengthen your data analysis and statistics skills while training on the latest industry-standard software programs. This online certificate program blends practical and theoretical data analysis and can give you the tools and knowledge you need to handle and analyze data for your organization.

Learn more about the Graduate Certificate in Applied Statistics  

Frequently Asked Questions

You can learn more about the application process, prerequisites, and what our courses are like.

What criteria are used for selecting new MAS students?

The MAS admissions committee considers grades — particularly in quantitative courses — along with curriculum vitae, letters of recommendation, and a statement of purpose. The committee will also take your prior working experience into consideration.

International students should refer to the the English Proficiency section on the Graduate School website for language requirements and criteria.

Can I take the online MAS program totally online?

Yes, you can take all courses in the MAS program online. There is no residency requirement for this degree.

What will the online MAS diploma actually say?

Your diploma will state that The Pennsylvania State University grants you the degree of Master of Applied Statistics. Neither the diploma nor the transcript will differentiate the mode (online or otherwise) in which you completed the courses or degree. 

If I have taken some courses at Penn State as a nondegree student, can they be transferred to the MAS program?

A maximum of 15 eligible, nondegree credits can be transferred to a degree program at Penn State. For example, credits taken to fulfill Graduate Certificate in Applied Statistics requirements at Penn State may be transferred to the MAS program if you are granted admission.

Can credits from another institution be transferred to the MAS program?

A maximum of 10 credits from an accredited external institution may be considered for transfer to the MAS program. These credits must have been received within the past five years and should not have been used to fulfill any other degree requirement. The syllabus and grades for these courses will be reviewed by the MAS admissions committee to determine whether credit is eligible to be transferred.

What is the math prerequisite for the MAS program?

The math prerequisite for this program is a standard three-course calculus sequence (for example, MATH 140, MATH 141, MATH 230) and sufficient knowledge of matrix algebra. 

Is a thesis required for the MAS degree?

There is no thesis requirement for this professional degree. You will need to complete the capstone MAS project, which is a course requirement for the core course STAT 581.

What is the application process if I have been admitted to another graduate degree program at Penn State?

If you have been admitted to a graduate program at Penn State (for example, you have obtained a master’s degree from Penn State), then instead of filing the application form to the Graduate School at Penn State again, you will need to submit the Resume Study/Change of Graduate Degree or Major form to the Graduate School. In addition, you will need to ask your graduate program to send a copy of your previous application material to the MAS admissions committee. You will also need to submit your statement of purpose, any additional transcripts, and three letters of recommendation.

What is the format of the online courses in this program?

Please visit the Online Learning page on the Department of Statistics website for more information.

Contact Us

To learn more about the Master of Applied Statistics, offered in partnership with the Penn State Eberly College of Science, please visit the departmental website or contact:

World Campus Admissions Counselors
Phone: 814-863-5386
Email: [email protected]

Mosuk Chow, Ph.D.
Research Professor
Director of MAS Program
Department of Statistics, Penn State University
315 Thomas Building
University Park, PA 16802
Phone: 814-863-8128
Email: [email protected]  

Faculty

  • Indrani  Basak

    • Degree
      Ph.D., Statistics, University of Pittsburgh 
    • Degree
      M.S., Statistics, Indian Statistical Institute, Calcutta 
    • Degree
      M.A., Mathematics, University of Pittsburgh 
    • Degree
      B.S., Statistics, Indian Statistical Institute, Calcutta 

    Dr. Indrani Basak teaches undergraduate and graduate statistics classes.  Her research interests include robust statistical methods, censoring methods, analytic hierarchy process, and multivariate analysis.

  • Priyangi Bulathsinhala

    • Degree
      Ph.D., Statistics, Southern Methodist University
    • Degree
      M.S., Statistics, University of Texas at El Paso 

    Dr. Priyangi Bulathsinhala is an assistant teaching professor in the statistics department. She teaches both online and resident classes. She joined the Penn State statistics department in August 2016. Her research interests include applications in spatial statistics.

  • Mosuk  Chow 

    • Degree
      Ph.D.,  Statistics, Cornell University 
    • Degree
      M.S.,  Statistics, Cornell University 
    • Degree
      B.S., Mathematics, Chinese University of Hong Kong 

    Dr. Mosuk Chow is the MAS program director, and her areas of research interest include biostatistics, statistical decision theory, Bayesian inference, and sampling methods. An important question in statistical decision theory is to characterize the set of all optimal procedures. An admissible procedure is optimal in the weak sense that it cannot be outperformed by another procedure completely in all circumstances. It is thus desirable to find necessary conditions for admissible procedures. Her work in decision theory involves finding such necessary conditions, investigating the admissibility properties of various estimators for problems arising from biology, genetics, and fishery.  

  • David Hunter

    • Degree
      Ph.D., Statistics, University of Michigan 
    • Degree
      A.B., Mathematics, Princeton University 

    Dr. David Hunter is a professor in the Department of Statistics, where he also serves as co-director of online programs. He was a high school mathematics teacher prior to earning his doctorate in statistics and joining the Penn State faculty in 1999. At Penn State, he has taught statistics at all levels from introductory to graduate levels, and as department head from 2012 to 2018, he oversaw Penn State's rise to national prominence as a center for expertise in statistics education. His research interests include statistical computing, models for social networks, and statistical clustering.

  • Prabhani Kuruppumullage Don 

    • Degree
      Ph.D.,  Statistics, Penn State
    • Degree
      M.S.,  Statistics, Penn State
    • Degree
      B.Sc., Statistics (First Class Honors), University of Colombo, Sri Lanka 

    Dr. Prabhani Kuruppumullage Don is an assistant research professor in the Department of Statistics. As the assistant director of online programs, she also oversees all operations of the online programs for the department. Prior to joining the department in 2018, she served as an assistant professor of statistics at the University of Rhode Island and completed her post-doctoral training at the Dana-Farber Cancer Institute/Harvard T.H. Chan School of Public Health. Her research interests include statistical computing, statistical genetics, and latent class models.

  • Eugene J. Lengerich

    • Degree
      V.M.D., Veterinary Medicine, University of Pennsylvania
    • Degree
      M.S., Agricultural Economics and Operations Research, Penn State

    Dr. Eugene Lengerich is a professor of public health sciences and faculty director of the public health preparedness option. He teaches courses on epidemiology, community preparedness and resilience, and the SARS-CoV-2 vaccine. He also mentors students in independent research. He has led health assessments for medical and public health students in domestic and international settings. Prior to joining Penn State, he conducted outbreak investigations as an Epidemic Intelligence Service officer and preventive medicine resident at the Centers for Disease Control and Prevention. Following his experience at the federal level, he led health investigations for the state of North Carolina. His research interests are in outbreak detection and investigation, community and public preparedness, and preparedness education.

  • Bruce Lord

    • Degree
      Ph.D., Forest Resources, Penn State
    • Degree
      M.S., Forest Resources and Operations Research, Penn State
    • Degree
      B.S., Forest Science, Penn State

    Dr. Bruce Lord has more than 30 years of experience as a resource economist specializing in the impacts of natural resources upon rural economies. He has made extensive use of survey research to study the economic impacts of the wood products industry and natural resource–based travel and tourism. His research interests include survey design and analysis, natural resource measurements, and economic forecasting.

  • Eric Nord

    • Degree
      Ph.D., Ecology, Penn State
    • Degree
      M.S., Ecology, Penn State

    Dr. Eric Nord is a plant physiological ecologist and quantitative ecologist with an interest in agricultural ecosystems.  

  • Iain Pardoe

    • Degree
      Ph.D., Statistics, University of Minnesota
    • Degree
      M.Sc., Statistics, University of Minnesota
    • Degree
      BSc., Economics and Statistics, University of Birmingham, UK

    Dr. Iain Pardoe teaches and writes online university statistics and math courses from Nelson, British Columbia, Canada. He teaches at Thompson Rivers University and Statistics.com, as well as at Penn State. His main teaching interest is applied statistics, particularly regression, and he is the author of Applied Regression Modeling (second edition, Wiley, 2012). Dr. Pardoe has broad experience in regression modeling and graphics, Bayesian analysis, and statistical computing. He has also been involved with statistical consulting projects in criminal justice, manufacturing demand, scheduling, and eco-labeling marketing.  

  • Megan  Romer

    • Degree
      Ph.D., Statistics,  Penn State
    • Degree
      M.S., Statistics,  Penn State
    • Degree
      B.S., Mathematics and Applied Mathematical Economics,  SUNY Oswego

    Dr. Megan Romer has been teaching online since 2009 when she earned her doctorate from Penn State's Department of Statistics. She enjoys discussing statistical concepts and problems with students. Before returning to school to finish her doctorate, Dr. Romer worked in clinical trials as a senior research support associate. Her primary area of research is in incomplete data.  

  • Scott Roths

    • Degree
      Ph.D., Statistics,  Penn State
    • Degree
      B.S., Mathematics,  Kansas State

    Dr. Scott Roths' primary interest is in teaching statistics, including probability and multivariate methods. He teaches both online and at the University Park campus.

  • Eduardo Santiago

    Degree
    Ph.D., Industrial Engineering and Operations Research, Penn State

    Dr. Eduardo Santiago's research interest is in design of experiments (DOE), specifically the algorithmic creation of optimal designs. He has more than 10 years of industry experience working as a consultant in automotive, food and beverage, pharmaceutical, medical device, insurance, logistics, and chemical industries. Dr. Santiago has written several papers in different areas, including DOE and statistical process control. He has designed and co-developed a control chart available in Minitab to monitor adverse events, such as nosocomial infections and urinary tract infections.  

  • Aleksandra  Slavković

    • Degree
      Ph.D., M.S., Statistics, Carnegie Mellon University 
    • Degree
      Master of Human-Computer Interaction, Carnegie Mellon University 
    • Degree
      B.A., Psychology, Duquesne University 

    Dr. Aleksandra Slavković is co-chair of the Applied Statistics program, and her past and current research interests include usability evaluation methods, human performance in virtual environments, statistical data mining, application of statistics to social sciences, algebraic statistics, and statistical approaches to confidentiality and data disclosure. Her dissertation work focuses on statistical methodologies for disclosure limitation and data confidentiality, and presents new theoretical links between disclosure limitation, statistical theory, and computational algebraic geometry.  

  • Andrew Wiesner

    • Degree
      Ph.D., Psychology in Education, University of Pittsburgh  
    • Degree
      M.A., Applied Statistics, University of Pittsburgh 

     Dr. Andrew Wiesner's primary research interests are in sports and educational statistics. He serves on the executive committee for the Penn State Center for the Study of Sports in Society and is a board member for the Penn State All-Sports Museum.  Dr. Wiesner has also presented several faculty workshops on interpreting item statistics to improve exams and the fundamentals of test item–writing.     

  • Manel  Wijesinha

    • Degree
      Ph.D., Statistics, University of Florida
    • Degree
      M.S., Statistics, University of Florida 
    • Degree
      B.S., Mathematics, University of Sri Lanka, Colombo 

    Dr. Manel Wijesinha focuses her research primarily on optimal designs in multi-response regression models. She has expanded her research areas to include dose response experiments and microarray data analysis. In these areas of biostatistics, she finds many opportunities to apply her optimal design expertise.  

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Ready to take the next step toward your Penn State master's degree?

Apply by April 1 to start May 13. How to Apply