Statistics

Admission Requirements

Applicants apply for admission to the program via the Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies.

Scores from the Graduate Record Examinations (GRE), or from a comparable substitute examination accepted by the Statistics graduate program, are required for admission. [The requirement to submit GRE scores is temporarily suspended for Fall 2021.]

While applications from all students (including those who already have done graduate work) are reviewed, completion of a standard calculus sequence is regarded as a prerequisite. Students with a 3.00 or better junior/senior average (on a 4.00 scale) and with appropriate course backgrounds will be considered for admission. The best-qualified applicants will be accepted up to the number of spaces that are available for new students. Exceptions to the minimum 3.00 grade-point average may be made for students with special backgrounds, abilities, and interests. Students hoping to earn a Ph.D. in statistics may apply directly to the Ph.D. program without need for a master's degree.

Degree Requirements

Master of Applied Statistics (M.A.S.)

Requirements listed here are in addition to Graduate Council policies listed under GCAC-700 Professional Degree Policies.

For the M.A.S. degree, a minimum of 30 credits and a minimum grade-point average of 3.0 are required for graduation. Of the 30 credits, 24 must be courses from the Statistics department and 21 must be at the 500 level. The student must complete:

Required Courses
Applied Statistics
STAT 501Regression Methods3
STAT 502Analysis of Variance and Design of Experiments3
Mathematical Statistics
STAT 414Introduction to Probability Theory3
STAT 415Introduction to Mathematical Statistics3
Statistical Consulting
STAT 580Statistical Consulting Practicum I2
Electives
To complete the remaining credit requirements, a student can select 9-15 credits from the following applied statistics courses:9-15
Applied Nonparametric Statistics
Introduction to SAS
Applied Statistics
Design of Experiments
Analysis of Discrete Data
Applied Multivariate Statistical Analysis
Sampling Theory and Methods
Epidemiologic Research Methods
Design and Analysis of Clinical Trials
Applied Time Series Analysis
In addition, students with suitable backgrounds may choose up to 6 credits from a departmental list of additional courses with approval from their adviser.0-6
Culminating Experience
STAT 581Statistical Consulting Practicum II (Capstone Project) 1 1
Total Credits30

Master of Science (M.S.)

Requirements listed here are in addition to Graduate Council policies listed under GCAC-600 Research Degree Policies.

For the M.S. degrees, a student must complete at least 30 credits at the 400, 500, 600, or 800 level, including at least 27 at the 500 or 600 level and a minimum of 6 credits of thesis research (600 or 610); 21 of the 27 500-level credits must be formal course work from the Department of Statistics. A student must complete:

Required Courses
Applied Statistics
STAT 511Regression Analysis and Modeling3
STAT 512Design and Analysis of Experiments3
Mathematical Statistics
STAT 513Theory of Statistics I3
STAT 514Theory of Statistics II3
Stochastic Processes
STAT 515Stochastic Processes and Monte Carlo Methods3
Statistical Consulting
STAT 580Statistical Consulting Practicum I2
STAT 581Statistical Consulting Practicum II1
Electives
6 credits of electives6
Culminating Experience
STAT 600Thesis Research6
or STAT 610 Thesis Research Off Campus
Total Credits30

The student must also pass a written master's qualifying examination taken at the end of the first year. The thesis must be accepted by the advisers, a second reader, the head of the graduate program, and the Graduate School.

Doctor of Philosophy (Ph.D.)

Requirements listed here are in addition to Graduate Council policies listed under GCAC-600 Research Degree Policies.

The Department of Statistics requires a minimum total of 48 postbaccalaureate credits for the Ph.D. At least 42 credits, exclusive of the dissertation, must be in Statistics. Course work accepted for the M.S. in Statistics at Penn State will count toward the department’s 48-credit requirement. In the case of students who have earned credits in an advanced degree program at another university or in another department at Penn State, a maximum of 24 credits may count toward the 48-credit departmental requirement, subject to departmental approval.

For the Ph.D. degree, a student in Statistics must complete at least 48 credits, of which at least 42 must be STAT and at most three credits can be at the 400 level.

Required Courses
Core Course Requirements
STAT 511Regression Analysis and Modeling3
STAT 512Design and Analysis of Experiments3
STAT 513Theory of Statistics I3
STAT 514Theory of Statistics II3
STAT 515Stochastic Processes and Monte Carlo Methods3
STAT 553Asymptotic Tools3
Additional Courses
STAT 517Probability Theory3
STAT 561Statistical Inference I3
STAT 580Statistical Consulting Practicum I2
STAT 581Statistical Consulting Practicum II1
STAT 590Colloquium2
STAT 592Teaching Statistics1
Electives
Select 18 credits of the following:18
Probability Theory
Categorical Data Analysis I
Linear Models II
Statistical Inference II
Multivariate Analysis
Other courses approved by the Graduate Studies Committee
Total Credits48

Doctoral students must pass a qualifying examination, a comprehensive written and oral examination, and a final oral examination (the dissertation defense). To earn the Ph.D. degree, doctoral students must also write a dissertation that is accepted by the Ph.D. committee, the head of the graduate program, and the Graduate School.

Minor Requirements

Requirements listed here are in addition to requirements for minors in Graduate Council policies listed under GCAC-600 Research Degree Policies and GCAC-700 Professional Degree Policies.

Doctoral Minor in Statistics

The Department of Statistics has three possible paths for a Doctoral Minor in Statistics:

  • Path 1: STAT 414/MATH 414 and STAT 415/MATH 415 and at least three 500-level courses from the department.
  • Path 2: Five or more courses totaling 15 credits at the 500-level from the department. STAT 464 may also count toward the 15 credits.
  • Path 3: Four 500-level courses totaling 12 credits from the department and one additional course of 3 credits approved by the department head or graduate studies chair.

Please note: STAT 500 will not be counted toward the Doctoral Minor in Statistics under any path.

For all paths, a 3.5 GPA is required in the courses to be counted toward the minor. Completion of one of the paths listed above, with the specified grade-point average, and the signature on the Graduate Minor Program form constitutes approval of the Minor in Statistics. Official requests to add a minor to a doctoral student’s academic record must be submitted to Graduate Enrollment Services prior to establishment of the Ph.D. committee and prior to scheduling the comprehensive examination. At least one Graduate Faculty member from the minor field must be on the student’s Ph.D. committee.

Dual-Titles

Dual-title Ph.D. in Statistics and Climate Science

Requirements listed here are in addition to requirements listed in GCAC-208 Dual-Title Graduate Degree Programs.

Climate Science is a field devoted to the study of Earth’s climate in the past, present, and future. A particular focus is understanding the effects of human activities (anthropogenic impacts) and natural forcing on climate.  The Climate Science dual-title degree program is administered by the Department of Meteorology and Atmospheric Science for the participating graduate programs. The dual-title degree program is offered through participating programs in the College of Earth and Mineral Sciences and, where appropriate, other graduate programs in the University. The program enables students from several graduate programs to gain the perspectives, techniques, and methodologies of Climate Science, while maintaining a close association with major program areas of application.

Admission Requirements

Students must apply and be admitted to the graduate program in Statistics and The Graduate School before they can apply for admission to the dual-title degree program. After admission to their primary program, students must apply for admission to and meet the admissions requirements of the Climate Science dual-title program. Refer to the Admission Requirements section of the Climate Science Bulletin page. Doctoral students must be admitted into the dual-title degree program in Climate Science no later than the end of the fourth semester (not counting summer semesters) of entry into the graduate major program.

Degree Requirements

To qualify for the dual-title degree, students must satisfy the degree requirements for the Ph.D. in Statistics. In addition, students must complete the degree requirements for the dual-title in Climate Science, listed on the Climate Science Bulletin page. The qualifying examination in the Statistics satisfies the qualifying exam requirement for the dual-title degree program in Climate Science.

Ph.D. Committee Composition

In addition to the general Graduate Council requirements for Ph.D. committees, the Ph.D. committee of a Climate Science dual-title doctoral degree student must include at least one member of the Climate Science Graduate Faculty. Faculty members who hold appointments in both programs’ Graduate Faculty may serve in a combined role. If the chair of the Ph.D. committee is not also a member of the Graduate Faculty in Climate Science, the member of the committee representing Climate Science must be appointed as co-chair. The Climate Science representative on the student’s Ph.D. committee will develop questions for and participate in the evaluation of the comprehensive examination.

Dissertation and Dissertation Defense

Students in the dual-title program are required to write and orally defend a dissertation on a topic that is approved in advance by their Ph.D. committee and reflects their original research and education in both Statistics and Climate Science. Upon completion of the doctoral dissertation, the candidate must pass a final oral examination (the dissertation defense) to earn the Ph.D. degree. The dissertation must be accepted by the Ph.D. committee, the head of the Statistics graduate program, and the Graduate School.

Dual-Title M.S. and Ph.D. in Statistics and Operations Research

Requirements listed here are in addition to requirements listed in GCAC-208 Dual-Title Graduate Degree Programs.

The Operations Research dual-title degree program is administered by an Operations Research committee, which is responsible for management of the program. The program enables students from diverse graduate programs to attain and be identified with the tools, techniques, and methodology of operations research, while maintaining a close association with areas of application. Operations research is the analysis--usually involving mathematical treatment--of a process, problem, or operation to determine its purpose and effectiveness and to gain maximum efficiency.

Admission Requirements

Students must apply and be admitted to the graduate program in Statistics and the Graduate School before they can apply for admission to the dual-title degree program. Students are encouraged to submit their application forms as early as possible. Doctoral students must be admitted into the dual-title degree program in Operations Research no later than the end of the fourth semester (not counting summer semesters) of entry into the graduate major program. The “Request for Dual-Title Degree in Operations Research” form must be filled out in consultation with the Graduate Coordinator in the Statistics Department and submitted to the Chair of the Operations Research Program.

For the M.S. dual-title degree in Operations Research, in addition to those prescribed by the graduate major program, prerequisites for acceptance to the program without deficiency include the following or their equivalent:

MATH 140Calculus With Analytic Geometry I Keystone/General Education Course4
MATH 141Calculus with Analytic Geometry II Keystone/General Education Course4
MATH 220Matrices Keystone/General Education Course2-3
CMPSC 101Introduction to Programming Keystone/General Education Course3
3 credits of probability and statistics3

The “Request for Masters Dual-Title Degree in Operations Research” form must be filled out.

For the Ph.D. dual-title degree in Operations Research, in addition to those prescribed by the graduate major program, prerequisites for acceptance to the program without deficiency include the following or their equivalent:

MATH 401Introduction to Analysis I3
MATH 436Linear Algebra3
CMPSC 101Introduction to Programming Keystone/General Education Course3
3 credits of probability and statistics3

The "Request for PH.D. Dual-Title Degree in Operations Research” form must be filled out.

Degree Requirements

To qualify for the dual-title degree, students must satisfy the requirements for the degree they are enrolled in Statistics. In addition, they must satisfy the requirements described below, as established by the Operations Research committee.

For the M.S. dual-title degree in Operations Research, the minimum requirements are:

  • 6 credits in stochastic/statistical methods, including a minimum of 3 credits in each of the areas of statistical methods and stochastic processes;
  • 6 credits in optimization, including a minimum of 3 credits in linear programming;
  • 3 credits in computational methods; and
  • 3 credits in applications/specialization.

A minimum of 9 credits must be in the 500 series. Particular courses may satisfy both the graduate major program requirements and those in the Operations Research program. The supervisor of the master’s thesis must be a member of the Graduate Faculty recommended by the chair of the program granting the degree and approved by the Operations Research committee as qualified to supervise thesis work in operations research.

The minimum requirements for the Ph.D. dual-title degree in Operations Research are:

  • 9 credits in stochastic/statistical methods, including a minimum of 3 credits in each of the areas of statistical methods and stochastic processes;
  • 9 credits in optimization, including a minimum of 3 credits in linear programming;
  • 6 credits in computational methods, including a minimum of 3 credits in simulation; and
  • 12 credits in applications/specialization.

A minimum of 18 credits must be in the 500 series, and particular courses may satisfy both the graduate major program requirements and those in the Operations Research program.

Qualifying Exam

The qualifying examination committee for the dual-title Ph.D. degree will be composed of Graduate Faculty from Statistics and must include at least one Graduate Faculty member from the Operations Research program. Faculty members who hold appointments in both programs’ Graduate Faculty may serve in a combined role. There will be a single qualifying examination, containing elements of both Statistics and Operations Research. Dual-title graduate degree students may require an additional semester to fulfill requirements for both areas of study and, therefore, the qualifying examination may be delayed one semester beyond the normal period allowable.

Ph.D. Committee Composition

The Ph.D. committee must conform to all requirements of the primary program and the Graduate Council. In accordance with Graduate Council policy, the Ph.D. committee of a Statistics and Operations Research dual-title doctoral degree student must include at least one member of the Operations Research Graduate Faculty. Faculty members who hold appointments in both programs’ Graduate Faculty may serve in a combined role.

If the chair of the committee representing Statistics is not also a member of the Graduate Faculty in Operations Research, the member of the committee representing Operations Research must be appointed as co-chair.

Comprehensive Exam

After completing all course work, doctoral students in the dual-title doctoral degree program in Statistics and Operations Research must pass a comprehensive examination that includes written and oral components.

There are two ways for students to complete their comprehensive examination.

Typically, both written and oral components of the comprehensive examination involve the defense of a dissertation proposal, which must contain core Statistics content and substantial Operations Research content, and is evaluated by the Ph.D. committee. The Operations Research representative(s) on the student’s Ph.D. committee will participate in the evaluation of the comprehensive examination.

Alternatively, the student may have a written and oral comprehensive exam focusing on at least two key areas in Statistics with content from Operations Research (acting as a first minor field). The examination focuses on the dissertation prospects and the student’s preparation to undertake dissertation research, and is evaluated by the Ph.D. committee. The Operations Research representative(s) on the student’s Ph.D. committee will develop questions for and participate in the evaluation of the comprehensive examination. A written and oral defense of a dissertation proposal would then occur at a later stage as per committee’s recommendation.

Dissertation and Dissertation Defense

Upon completion of the doctoral dissertation, the candidate must pass a final oral examination (the dissertation defense) to earn the Ph.D. degree. Students enrolled in the dual-title program are required to write and orally defend a dissertation on a topic that is approved in advance by their Ph.D. committee and reflects their original research and education in Statistics and Operations Research. The dissertation must be accepted by the Ph.D. committee, the head of the graduate program, and the Graduate School.

Dual-Title Ph.D. in Statistics and Social Data Analytics

Requirements listed here are in addition to requirements listed in GCAC-208 Dual-Title Graduate Degree Programs.

Statistics doctoral students seeking to attain and be identified with an interdisciplinary array of tools, techniques, and methodologies for social data analytics, while maintaining a close association with statistics, may apply to pursue a dual-title Ph.D. in Statistics and Social Data Analytics. Social data analytics is the integration of social scientific, computational, informational, statistical, and visual analytic approaches to the analysis of large or complex data that arise from human interaction. The dual-title Ph.D. aims to enable scientists who expand the capability of social data analytics, and use those capabilities creatively to answer important social scientific questions and to address grand social challenges, in both academic and nonacademic settings.

Admission Requirements

Students must apply and be admitted to the graduate program in Statistics and the Graduate School before they can apply for admission to the dual-title degree program. Applicants interested in the dual-title degree program may make their interest in the program known clearly on their applications to Statistics and include remarks in their statement of purpose that address the ways in which their research and professional goals in statistics reflect an expanded interest in Social Data Analytics-related research.

To apply to the dual-title Ph.D. in Statistics and Social Data Analytics, a student must submit a letter of application and transcript, which will be reviewed by the Social Data Analytics Program. An applicant must have a minimum grade-point average of 3.0 (on a 4.0 point scale) to be considered for enrollment in the dual-title degree program. Doctoral students must be admitted into the dual-title degree program in Operations Research no later than the end of the fourth semester (not counting summer semesters) of entry into the graduate major program.

Degree Requirements

To qualify for the dual-title degree, students must satisfy the requirements of the Ph.D. in Statistics. In addition, they must satisfy the requirements described below, as established by the Social Data Analytics Committee. Within this framework, final course selection is determined by the student in consultation with academic advisers from their home department and Social Data Analytics.

Course Work

The minimum course work requirements for the dual-title Ph.D. in Statistics and Social Data Analytics are as follows:

  • Course work and other requirements for the Ph.D. in Statistics.
  • SODA 501 (3 credits)
  • SODA 502 (3 credits)
  • 12 or more elective credits in Social Data Analytics from a list of courses maintained by the Social Data Analytics Committee. Collectively the elective credits must satisfy the following requirements:
    • (A) Core analytics distribution. 3 or more credits in courses focused on statistical learning, machine learning, data mining, or visual analytics. Courses approved as meeting this requirement are designated (A) on the list of approved electives.
    • (Q) Quantification distribution. 6 or more credits in courses focused on statistical inference or quantitative social science methodology. Courses approved as meeting this requirement are designated (Q) on the list of approved electives. (A Statistics Ph.D. student would typically satisfy this distribution requirement as a function of completing the requirements of the Statistics Ph.D.)
    • (C) Computational / informational distribution. 6 or more credits in courses focused on computation, collection, management, processing, or interaction with electronic data, especially at scale. Courses approved as meeting this requirement are designated (C) on the list of approved electives.
    • (S) Social distribution. 6 or more credits in courses with substantial content on the nature of human interaction and/or the analysis of data derived from human interaction and/or the social context or ethics or social consequences of social data analytics. Courses approved as meeting this requirement are designated (S) on the list of approved electives. (A Statistics Ph.D. student would typically satisfy this distribution requirement as a function of completing the requirements of the Statistics Ph.D.)
    • Cross-departmental distribution.
      • 3 or more credits in approved courses with the prefix STAT or that of a primarily social science department. (A Statistics Ph.D. student would typically satisfy this distribution requirement as a function of completing the requirements of the Statistics Ph.D.)
      • 3 or more credits in approved courses with the prefix IST, GEOG, or that of a primarily computer science or engineering department.
      • 6 or more credits in approved courses outside Statistics.
      • 3 or fewer credits in approved courses at the 400-level.

Students are encouraged to take interdisciplinary courses that carry multiple (A), (Q), (C), (S) designations, as well as to select SODA electives that also meet STAT requirements. In particular, the 12 elective SODA credits can be met with as few as 6 credits of appropriately chosen course work. Conversely, 6 credits of SODA course work, including SODA 501 and SODA 502, can be used to meet the STAT elective requirement. Within this framework, final course selection is determined by the student in consultation with academic advisers from Statistics and Social Data Analytics. (There are no formal maxima for the number of double-counted credits. For those meeting the SODA elective requirement with the minimum of 12 credits, the outside-program minimum effectively limits the number of primary degree STAT credits that count toward SODA at 6. For those meeting STAT elective requirements with the minimum of 18 credits, the 12 credit STAT minimum effectively limits the number of SODA credits that count toward STAT at 6.)

Qualifying Exam

The qualifying examination committee for the dual-title Ph.D. degree will be composed of Graduate Faculty from Statistics and must include at least one Graduate Faculty member from the Social Data Analytics program. Faculty members who hold appointments in both programs’ Graduate Faculty may serve in a combined role. There will be a single qualifying examination, containing elements of both Statistics and Social Data Analytics. Dual-title graduate degree students may require an additional semester to fulfill requirements for both areas of study and, therefore, the qualifying examination may be delayed one semester beyond the normal period allowable.

The Social Data Analytics Program maintains a list of recommended background and skills that it recommends students have in place by the time they begin the interdisciplinary course work required to complete the Social Data Analytics degree. The qualifying exam is the appropriate setting for assessing the student’s preparation for the interdisciplinary work of the dual-title Ph.D. in Statistics and Social Data Analytics.

Ph.D. Committee Composition

The Ph.D. committee must conform to all requirements of the primary program and the Graduate Council. In accordance with Graduate Council policy, the Ph.D. committee of a Statistics and Social Data Analytics dual-title doctoral degree student must include at least one member of the Social Data Analytics Graduate Faculty. Faculty members who hold appointments in both programs’ Graduate Faculty may serve in a combined role. If the chair of the committee representing Statistics is not also a member of the Graduate Faculty in Social Data Analytics, the member of the committee representing Social Data Analytics must be appointed as co-chair.

Comprehensive Exam

After completing all course work, doctoral students in the dual-title doctoral degree program in Statistics and Social Data Analytics must pass a comprehensive examination that includes written and oral components.

There are two ways for students to complete their comprehensive examination.

Typically, both written and oral components of the comprehensive examination involve the defense of a dissertation proposal, which must contain core Statistics content and substantial Social Data Analytics content, and is evaluated by the Ph.D. committee. The Social Data Analytics representative(s) on the student’s Ph.D. committee will participate in the evaluation of the comprehensive examination.

Alternatively, the student may have a written and oral comprehensive exam focusing on at least two key areas in Statistics with content from Social Data Analytics (acting as a first minor field). The examination focuses on the dissertation prospects and the student’s preparation to undertake dissertation research, and is evaluated by the Ph.D. committee. The Social Data Analytics representative(s) on the student’s Ph.D. committee will develop questions for and participate in the evaluation of the comprehensive examination. A written and oral defense of a dissertation proposal would then occur at a later stage as per committee’s recommendation.

Dissertation and Dissertation Defense

Upon completion of the doctoral dissertation, the candidate must pass a final oral examination (the dissertation defense) to earn the Ph.D. degree. Students enrolled in the dual-title program are required to write and orally defend a dissertation on a topic that is approved in advance by their Ph.D. committee and reflects their original research and education in Statistics and Social Data Analytics. The dissertation must be accepted by the Ph.D. committee, the head of the graduate program, and the Graduate School.

Integrated Undergrad-Grad Programs  

Integrated B.A. or B.S. in Mathematics and M.A.S. in Applied Statistics

Requirements listed here are in addition to requirements listed in GCAC-210 Integrated Undergraduate-Graduate (IUG) Degree Programs.

The Integrated Undergraduate-Graduate (IUG) degree with B.A./B.S. in Mathematics and Master of Applied Statistics (M.A.S.) is designed to be completed in five years. This integrated degree will enable a select number of highly qualified and career-oriented students to obtain training in statistics focused on developing data analysis skills, and exploration of core areas of applied statistics at the graduate levels in addition to an undergraduate degree in Mathematics. The M.A.S. degree is a professional master's degree that emphasizes applications. The degree prepares students with interests in mathematics, computation, and the quantitative aspects of science for careers in industry and government as statistical analysts. Research divisions in the pharmaceutical industry, quality control, and quality engineering divisions in manufacturing companies, clinical research units, corporate planning and research units, and other data intensive positions require persons with training in mathematics, computation, database management, and statistical analysis, which this program will provide.

Admission Requirements

Applicants apply for admission to the program via the Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies.

The number of openings in the integrated B.A./B.S. and M.A.S. program is limited. Students must apply to the program via the Graduate School application for admission, and must meet all the admission requirements of the Graduate School and the Applied Statistics graduate program for the Master of Applied Statistics degree, listed on the Admission Requirements tab. Admission will be based on specific criteria and the recommendation of faculty. Before applying to the Graduate School, students must have completed entrance to their undergraduate major, have completed no less than 60 credits, and be admitted no later than the end of the second week of the semester preceding the semester of expected conferral of the undergraduate degree. Transfer students must have completed at least 15 credits at Penn State to enroll in an IUG. Applicants to the integrated program:

  • Must be enrolled in the Mathematics B.A./B.S. program.
  • Must have completed at least 60 credits of the undergraduate degree program including the two courses: STAT 414 and STAT 415.
  • Must submit a transcript and a statement of purpose.
  • Must present a departmental approved plan of study in the application process in consultation with the M.A.S. program director. The plan should cover the entire time period of the integrated program, and it should be reviewed periodically with an adviser as the student advances through the program.
  • Must be recommended by the chair of Mathematics Department's undergraduate program committee. Two additional recommendation letters must be sent to the M.A.S. admissions committee.
  • Must be accepted to the M.A.S. program in Statistics.

Degree Requirements

Students must fulfill all degree requirements for each degree in order to be awarded that degree, subject to the double-counting of credits as outlined below. Degree requirements for the Bachelor of Arts and Bachelor of Science in Mathematics are listed in the Undergraduate Bulletin. Degree requirements for the Master of Applied Statistics in Applied Statistics degree are listed on the Degree Requirements tab.

Up to 12 credits may be double-counted towards the degree requirements for both the graduate and undergraduate degrees; a minimum of 50% of the double-counted courses must be at the 500 or 800 level. Independent study courses and credits associated with the culminating experience for the graduate degree cannot be double-counted. The courses that will double count for both degrees are:

Courses Eligible to Double Count for Both Degrees
STAT 414Introduction to Probability Theory3
STAT 415Introduction to Mathematical Statistics3
STAT 501Regression Methods3
STAT 502Analysis of Variance and Design of Experiments3
Total Credits12

Students must sequence their courses so all undergraduate degree requirements are fulfilled before taking courses to count solely towards the graduate degree. Students must complete the undergraduate degree requirements within the typical time to degree for the undergraduate major. In the semester in which the undergraduate degree requirements will be completed, IUG students must apply to graduate, and the undergraduate degree should be conferred at the next appropriate Commencement. If students accepted into the IUG program are unable to complete the M.A.S. degree, they are still eligible to receive their undergraduate degree if all the undergraduate degree requirements have been satisfied.

Integrated B.S. in Statistics and M.A.S. in Applied Statistics

Requirements listed here are in addition to requirements listed in GCAC-210 Integrated Undergraduate-Graduate (IUG) Degree Programs.

The Integrated Undergraduate-Graduate (IUG) degree with B.S. in Statistics and Master of Applied Statistics (M.A.S.) is designed to be completed in five years. This integrated degree will enable a select number of highly qualified and career-oriented students to obtain training in statistics focused on developing data analysis skills and exploration of core areas of applied statistics at the undergraduate and graduate levels. The M.A.S. degree is a professional master's degree that emphasizes applications and does not provide as much training in the mathematical and statistical theory. The degree prepares students with interests in mathematics, computation, and the quantitative aspects of science for careers in industry and government as statistical analysts. Research divisions in the pharmaceutical industry, quality control and quality engineering divisions in manufacturing companies, clinical research units, corporate planning and research units, and other data-intensive positions require persons with training in mathematics, computation, database management, and statistical analysis, which this program will provide.

Admission Requirements

Applicants apply for admission to the program via the Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies.

The number of openings in the integrated B.S./M.A.S. program is limited. Students must apply to the program via the Graduate School application for admission, and must meet all the admission requirements of the Graduate School and the Applied Statistics graduate program for the Master of Applied Statistics degree, listed on the Admission Requirements tab. Admission will be based on specific criteria and the recommendation of faculty. Before applying to the Graduate School, students must have completed entrance to their undergraduate major, have completed no less than 60 credits, and be admitted no later than the end of the second week of the semester preceding the semester of expected conferral of the undergraduate degree. Transfer students must have completed at least 15 credits at Penn State to enroll in an IUG.  Applicants to the integrated program:

  • Must be enrolled in the Statistics B.S. program.
  • Must have completed at least 60 credits of the undergraduate degree program, including the two courses: STAT 414 and STAT 415.
  • Must submit a transcript and a statement of purpose.
  • Must present a departmental approved plan of study in the application process in consultation with the M.A.S. program director. The plan should cover the entire time period of the integrated program, and it should be reviewed periodically with an adviser as the student advances through the program.
  • Must be recommended by the chair of the department's undergraduate program committee.
  • Must be accepted into the M.A.S. program in Statistics.

Degree Requirements

Students must fulfill all degree requirements for each degree in order to be awarded that degree, subject to the double-counting of credits as outlined below. Degree requirements for the Bachelor of Science in Statistics are listed in the Undergraduate Bulletin. Degree requirements for the Master of Applied Statistics in Applied Statistics degree are listed on the Degree Requirements tab.

Up to 12 credits may be double-counted towards the degree requirements for both the graduate and undergraduate degrees; a minimum of 50% of the double-counted courses must be at the 500 or 800 level. Independent study courses and credits associated with the culminating experience for the graduate degree cannot be double-counted. The courses that will double count for both degrees are:

Courses Eligible to Double Count for Both Degrees
STAT 414Introduction to Probability Theory3
STAT 415Introduction to Mathematical Statistics3
STAT 501Regression Methods3
STAT 502Analysis of Variance and Design of Experiments3
Total Credits12

Students must sequence their courses so all undergraduate degree requirements are fulfilled before taking courses to count solely towards the graduate degree. Students must complete the undergraduate degree requirements within the typical time to degree for the undergraduate major. In the semester in which the undergraduate degree requirements will be completed, IUG students must apply to graduate, and the undergraduate degree should be conferred at the next appropriate Commencement. If students accepted into the IUG program are unable to complete the M.A.S. degree, they are still eligible to receive their undergraduate degree if all the undergraduate degree requirements have been satisfied.

Student Aid

Graduate assistantships available to students in this program and other forms of student aid are described in the Tuition & Funding section of The Graduate School’s website. Students on graduate assistantships must adhere to the course load limits set by The Graduate School.

GRE scores are required for consideration for assistantships.

World Campus students in graduate degree programs may be eligible for financial aid. Refer to the Tuition and Financial Aid section of the World Campus website for more information.

Courses

Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.

Statistics (STAT) Course List

Learning Outcomes

Master of Applied Statistics (M.A.S.)

  1. Graduates shall demonstrate conceptual and practical knowledge of the broad aspects of Statistical analysis techniques. The core areas of Applied Statistics (Regression Analysis, Design of Experiment, Analysis of Variance, Analysis of Discrete Data, MANOVA, and many more) will be explored. 
  2. Graduates will be able to apply the statistical analysis techniques they learn to real problems. They will demonstrate proficiency in the working with others as a data analyst in a team setting, as well as in broad areas of data processing, data visualization, statistical analysis and interpretation of the statistical results.  Students will also demonstrate adequate professional preparation for drawing sound conclusions and creating reports to aid in making decisions as data analysts and applied statisticians.
  3. Graduates will demonstrate skills in communicating statistical findings and reports in a group setting and through oral presentations. They will be trained on development of recommendation reports, and discussion of consulting solutions.
  4. Graduates will be able to demonstrate critical thinking skills when reviewing scientific papers, literature and numerical reports. They will be trained to have a firm grasp of statistical thinking and sound understanding of statistical conclusions. 
  5. Graduates will demonstrate knowledge of interpersonal working dynamics, ethical professional conduct and the ability to perform in a team environment. They will participate in professional networking, and engagement in professional activities and organizations serving the discipline and the industry.

Master of Science (M.S.) and Doctor of Philosophy (Ph.D.)

  1. Graduates shall demonstrate in-depth and advanced knowledge and understanding in statistics core areas of  probability, statistical inference, modeling and computing. The core demonstration will include the application of these principles to problems in various contexts such as genetics, medicine, biology, environmental studies, and social and behavioral sciences, that are crucial for the practice of modern statistics.
  2. Graduates shall demonstrate, at a level appropriate to a departmental colloquium, (i) knowledge of several outstanding problems or questions in diverse sub-fields of statistics, (ii) the experimental and theoretical origins of these problems, and (iii) the principle efforts proposed or underway to address them, including demonstrating critical thinking skills when reviewing scientific papers, literature and numerical reports.
  3. Graduates shall demonstrate the ability to communicate professionally, in written and oral form, research work and conclusions of statistical findings to statistical experts and non-expert audiences.
  4. Graduates shall demonstrate (i) knowledge and understanding of professional standards of ethics and conduct, (ii) the ability to analyze situations to identify the standards that should apply including performing in a team environment, and (iii) describe how they may be appropriately acted upon. They will participate in professional networking, and engagement in professional activities and organizations serving the discipline and the broader scientific community and the industry.
  5. Graduates shall have a specialty area within the broad domain of statistics, within which they shall demonstrate (i) advanced knowledge and understanding of the primary literature, (ii) the ability to analyze and judge new contributions to the primary literature, (iii) the ability to pose complex research problem(s) and identify the knowledge and methodologies required to address them, and (iv) the ability to apply that knowledge and those methodologies to create new knowledge and/or develop new theories and methods that advance (or show the potential to advance) knowledge and understanding within the specialty area, and to another discipline where their findings are applicable.

Contact

Campus University Park
Graduate Program Head Murali Haran
Director of Graduate Studies (DGS) or Professor-in-Charge (PIC) Ephraim Mont Hanks
Program Contact

Stephanie Mary Valente
326 Thomas Bldg.
University Park PA 16802
suv9@psu.edu
(814) 863-3886

Program Website View