STAT

101 (General Education (GA) *†

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

(Inter-Domain) 15 16

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 12 Courses offered in foreign countries by individual or group instruction.

Subject

STAT

Credits (min)

1

Credits (max)

12

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

its or STAT 240 Introduction to Biometry or STAT 250 Introduction to Biostatistics Supporting Courses and Related Areas 3 Select 12 credits of ABSM courses from the following: 12 3 ABSM 310 Power Transmission in Agriculture

Subject

STAT

Credits (min)

3

Credits (max)

4

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

or STAT 301 Select 3-4 credits of the following: 3-4

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

STAT 401 Select 3-4 cre MATH 21 MATH 22 MATH 26 MATH 33 MATH 34 MATH 35 MATH 36 MATH 37 MATH 38 MATH 41 MATH 81 MATH 82 MATH 83 MATH 110 MATH 140 MATH 140G MATH 140H MATH 141 MATH 141G MATH 141H Select 3 credi EMSC 299 te, EMSC 470W GEOG 199 GEOG 294 GEOG 296 ts GEOG 299 GEOG 399 GEOG 493 GEOG 494 3 GEOG 494H GEOG 495 3 GEOG 495B GEOG 496 3 GEOG 499 Select 3 credi 1 GEOG 310 3 GEOG 314 GEOG 320 GEOG 324 3-4 GEOG 326 GEOG 328 GEOG 330N Select 12 cred

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 18 Creative projects, including research and design, that are supervised on an individual basis and that fall outside the scope of formal courses.

Subject

STAT

Credits (min)

1

Credits (max)

18

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 9 Formal courses given infrequently to explore, in depth, a comparatively narrow subject that may be topical or of special interest.

Subject

STAT

Credits (min)

1

Credits (max)

9

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

This course is designed to serve as a bridge between introductory statistics (including AP statistics) and more advanced applied statistics courses. The course will emphasize applied statistical modeling for real data using computer software (e.g. R, Minitab). Broad statistical topics include simple linear regression, multiple linear regression, analysis of variance (ANOVA) and factorial designs, logistic regression, multiple linear regression. Enforced Prerequisite at Enrollment: STAT 100 or STAT 200 or STAT 240 or STAT 250 or SCM 200 or PSYCH 200 or DS 200 or IE 323

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Combinatorial analysis, axioms of probability, conditional probability and independence, discrete and continuous random variables, expectation, limit theorems, additional topics. Students who have passed either MATH(STAT) 414 or 418 may not schedule this course for credit. Enforced Prerequisite at Enrollment: MATH 141 Cross-listed with: MATH 318 Bachelor of Arts: Quantification

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Statistical inference: principles and methods, estimation and testing hypotheses, regression and correlation analysis, analysis of variance, computer analysis. Students who have passed STAT (MATH) 415 may not schedule this course for credit. Enforced Prerequisite at Enrollment: MATH 318 or STAT 318 or MATH 414 or STAT 414 or STAT 418 or MATH 418 Cross-listed with: MATH 319 Bachelor of Arts: Quantification

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

General Education Course 3 General Education Course 3 (GS) (Inter-Domain) 16 15

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 12 Courses offered in foreign countries by individual or group instruction.

Subject

STAT

Credits (min)

1

Credits (max)

12

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

This course is intended to build directly upon STAT 300 (Applied Statistical Modeling I) for students pursuing a major in statistics or a closely related program. Topics include likelihood-based inference, generalized linear models, random and mixed effects modeling, multilevel modeling. In particular, the applied nature of the course seeks to examine the advantages and disadvantages of various modeling tools presented, identify when they may be useful, use R software to implement them for analysis of real data, evaluate assumptions, interpret results, etc. Enforced Prerequisite at Enrollment: STAT 184 and MATH 220 and (STAT 300 or STAT 462)

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Prescribed Courses: Require a grade of C or better

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Select two of the following: 6

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

This course covers probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems. In contrast to the non-honors version of 414, 414H has a stronger focus on a multivariate presentation of core concepts, asymptotic results, and proofs of essential theorems. This emphasis is complemented through more advanced in-class examples, homework problems, and exam questions. Students will also pursue a topic of choice in further depth through a course project. Students may take only one course from STAT(MATH) 414, 414H, and 418. Enforced Prerequisite at Enrollment: MATH 230 or MATH 230H or MATH 232 Cross-listed with: MATH 414H

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Select 10-12 credits from 400-level STAT courses 10-12 Not including: • STAT 401 • STAT 414 • STAT 415 • STAT 418 Some course may require other coursework as some courses have prerequisites.

Subject

STAT

Credits (min)

1

Credits (max)

1

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Review of distribution models, probability generating functions, transforms, convolutions, Markov chains, equilibrium distributions, Poisson process, birth and death processes, estimation. Enforced Prerequisite at Enrollment: (STAT 318 or MATH 318 or STAT 414 or MATH 414) and (MATH 230 or MATH 232) Cross-listed with: MATH 416

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

3 STAT 462 Applied Regression Analysis 3

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Review of hypothesis testing, goodness-of-fit tests, regression, correlation analysis, completely randomized designs, randomized complete block designs, latin squares. Enforced Prerequisite at Enrollment: STAT 200 or STAT 240 or STAT 250 or STAT 401

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Analysis of variance for single and multifactor designs; response surface methodology. Enforced Prerequisite at Enrollment: STAT 200 or STAT 240 or STAT 250 or STAT 401

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

or STAT 464 Applied Nonparametric Statistics Select one of the following: 6-8

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Supporting Courses and Related Areas Select 5 credits of unrestricted electives at 100-400 level Students must earn a 2.5 or higher grade point average in the follow courses: • For the General Option: CMPSC 221, CMPSC 312, CMPSC 360, CMPSC 430, CMPSC 460, CMPSC 462, CMPSC 463, CMPSC 469, CMPSC 470, CMPSC 472, CMPSC 487W, and CMPSC 488 • For the Data Science Option: DS 220, CMPSC 312, CMPSC 360, CMPSC 430, CMPSC 445, CMPSC 446, CMPSC 460, CMPSC 462, CMPSC 463, CMPSC 469, CMPSC 472, CMPSC 487W, and CMPSC 488 General Option (35 credits) Available at the following campuses: Abington, Harrisburg

Subject

STAT

Credits (min)

1

Credits (max)

1

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Legal Studies Option

Subject

STAT

Credits (min)

21

Credits (max)

21

Credit unit

credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Supporting Courses and Related Areas Select 26 credits from department list, including a minor in a supporting field other than Mathematics Neither the mathematics major nor the six sigma minor, nor the risk management major with the actuarial science option may be used to satisfy the minor/concurrent major requirement. If a student wants to work in a supporting field that does not have a minor, he or she can propose a list of six appropriate courses and petition the Statistics Department for approval. It is the student's responsibility to justify appropriateness of the proposed list. Students must receive a grade of C or better in each of these six courses. Biostatistics Option (47-50 credits) Code Title Cred

Subject

STAT

Credits (min)

1

Credits (max)

1

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Communication in Applied Statistics This is a capstone course intended primarily for undergraduate statistics majors in their last semester prior to graduation. The course is designed to reinforce problem solving and communication skills through development of writing ability, interaction with peers and oral presentations. Course objectives are tailored to the needs of each cohort and may include the application of statistical reasoning to real- world problems and case studies, recognition or recommendation of appropriate experimental designs, proficient use of ANOVA & GLMs with understanding of associated modeling assumptions, ability to identify concerns about the use or interpretation of statistical models in context, and both written and verbal communication of statistical findings.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

400-level Advanced Stat 3 Supporting Course 3 General Education Course 3 General Education Course 3 (GS) (Inter-Domain) General Education Course 3 (GHW) 17 15

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Intermediate SAS for data management. STAT 481 Intermediate SAS for Data Management (1) STAT 481 builds on the skills and tools learned in STAT 480 to provide intermediate level ability to use the Statistical Analysis System (SAS). It covers additional capability and major uses of the program, such as error checking, report generation, date and time processing, random number generation, and production of presentation quality output for graphs and tables. Other possible topics include advanced merging, PROC SQL, importing and exporting data sets, SAS GRAPH, and the Output Delivery System. Enforced Concurrent at Enrollment: STAT 480

Subject

STAT

Credits (min)

1

Credits (max)

1

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Advanced statistical procedures in SAS, including ANOVA, GLM, CORR, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, MIXED, GRAPH, EXPORT, and SQL. STAT 482 Advanced Topics in SAS (1) STAT 482 builds on the skills and tools learned in STAT 480 and STAT 481 to provide advanced programming ability to use the Statistical Analysis System (SAS). It provides a survey of the major statistical analysis procedures, such as the TTEST, GLM, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, and MIXED procedures. Other topics include using the TABULATE procedure to create reports, generating random numbers, exporting data from SAS data sets, using the SAS/Graph module to produce presentation quality graphs, using the SQL procedure to query and combine data tables, and using macros to write more efficient SAS programs. Credit can not be received for both STAT 482 and STAT 480/481/483. Enforced Concurrent at Enrollment: STAT 480 and STAT 481

Subject

STAT

Credits (min)

1

Credits (max)

1

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Introduction, intermediate, and advanced topics in SAS. Credit can not be received for both STAT 483 and STAT 480/481/482. STAT 483 Statistical Analysis System Programming (3) The three-credit STAT 483 course is a combination of the three one-credit courses STAT 480, STAT 481, and STAT 482. In STAT 480, students are introduced to the SAS windowing system, basic SAS programming statements, and Undergraduate - The Pennsylvania State University 2026-2027 4893 descriptive reporting procedures, such as the FORMAT, PRINT, REPORT, MEANS, and FREQ procedures. In STAT 481, the focus is primarily on extending the programming skills of the students, as they learn how to read messy data into SAS data sets, how to combine SAS data sets in various ways, how to use SAS character functions, how to read and process date and time variables, how to use arrays and do loops to write more efficient programs, and how to use the Output Delivery System to create SAS output in a variety of formats. STAT 482 provides a survey of the major statistical analysis procedures, such as the TTEST, GLM, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, and MIXED procedures. Other STAT 482 topics include using the TABULATE procedure to create reports, generating random numbers, exporting data from SAS data sets, using the SAS/Graph module to produce presentation quality graphs, using the SQL procedure to query and combine data tables, and using macros to write more efficient SAS programs. Credit can not be received for both STAT 483 and STAT 480/481/482. Enforced Prerequisite at Enrollment: 3 credits in Statistics

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 1 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. The flexibility and extensibility of R are key attributes that have driven its adoption in a wide variety of fields. This course begins with an overview of the R language and the basics of R programming. Building upon these basic understandings and procedures, this course then provides students with hands on experience in implementing statistical analysis of data in univariate, bivariate and multivariate contexts using the R software. In addition, the course works through accessing, importing and manipulating data. Documentation of work and report writing are also important aspects of the course content, and R Markdown is utilized to illustrate best practices. Enforced Prerequisite at Enrollment: 3 credits in Statistics

Subject

STAT

Credits (min)

1

Credits (max)

1

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

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. The flexibility and extensibility of R are keys attributes that have driven its adoption in a wide variety of fields. This course begins extends the application of statistical analyses by providing students with hands on experience implementing R in various regression and ANOVA contexts. In addition, data visualization options are considered for producing customized graphics and simple programming is learned. Documentation of work and report writing is also an important aspect of the course content. Enforced Concurrent at Enrollment: STAT 484

Subject

STAT

Credits (min)

1

Credits (max)

1

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

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

Subject

STAT

Credits (min)

2

Credits (max)

2

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 12 Supervised student activities on research projects identified on an individual or small group basis. Enforced Prerequisite at Enrollment: 6 credits in Statistics

Subject

STAT

Credits (min)

1

Credits (max)

12

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 12 Supervised student activities on research projects identified on an individual or small group basis. Enforced Prerequisite at Enrollment: 6 credits in Statistics

Subject

STAT

Credits (min)

1

Credits (max)

12

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 18 Supervised off-campus, nongroup instruction including field experiences, practica, or internships. Enforced Prerequisite at Enrollment: 6 credits in Statistics

Subject

STAT

Credits (min)

1

Credits (max)

18

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 18 Creative projects, including research and design, which are supervised on an individual basis and which fall outside the scope of formal courses.

Subject

STAT

Credits (min)

1

Credits (max)

18

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

Subject

STAT

Credits (min)

1

Credits (max)

18

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 18 Creative projects, including research and design, which are supervised on an individual basis and which fall outside the scope of formal courses.

Subject

STAT

Credits (min)

1

Credits (max)

18

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 9 Formal courses given infrequently to explore, in depth, a comparatively narrow subject which may be topical or of special interest.

Subject

STAT

Credits (min)

1

Credits (max)

9

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

/Maximum of 12 Courses offered in foreign countries by individual or group instruction.

Subject

STAT

Credits (min)

1

Credits (max)

12

Credit unit

Credits

Type

course

Edition

undergraduate

Source

bulletins.psu.edu

3 credit course in immunology 3 3-credit course in molecular biology 3 Total Credits 21 At least 6 credits in thesis research (PHSIO 600 or PHSIO 610) must be taken in conjunction with the thesis. The thesis must be accepted by the advisers and/or committee members, the head of the graduate program, and the Graduate School, and the student must pass a thesis defense which includes a public presentation. Students in the non-thesis option must write a satisfactory scholarly paper, while enrolled in PHSIO 596. Doctor of Philosophy (Ph.D.) Requirements listed here are in addition to Graduate Council policies listed under GCAC-600 Research Degree Policies. (https:// gradschool.psu.edu/graduate-education-policies/) All candidates must complete rotations in physiology laboratories before choosing an area of specialization. Possible areas of specialization include cellular, molecular, animal or human aspects of the following: • cardiovascular and respiratory physiology • comparative physiology • environmental physiology • exercise physiology • muscle physiology • physiology of nutrition and metabolism • immunology • neurophysiology • reproductive physiology Students in the Ph.D. program must successfully pass the qualifying, comprehensive, and final oral examination (the dissertation defense) required by Graduate Council. 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. The Ph.D. committee shall be appropriately represented by members of the Integrative and Biomedical Physiology faculty and those of the area of specialization who shall have the responsibility and jurisdiction for determining the course program and research acceptable in satisfying degree requirements. Graduate - The Pennsylvania State University 2026-2027 463 The doctoral degree in Integrative and Biomedical Physiology requires a minimum of 30 credits, including: Code Title Credits

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu

or STAT 511 Regression Analysis and Modeling

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu

3-credit course in immunology 3 3-credit course in molecular biology 3 Other Seminars 4

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu

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

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 462 or STAT 501 ; STAT 502

Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher- way tables; latent and logistic models. Graduate - The Pennsylvania State University 2026-2027 1419

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 460 or STAT 502 or STAT 516 ; matrix algebra

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

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

MATH 441 , STAT 501 , STAT 502

Theory and application of sampling from finite populations.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

calculus; 3 credits in statistics

Research and quantitative methods for analysis of epidemiologic observational studies. Non-randomized, intervention studies for human health, and disease treatment. STAT 507 Epidemiologic Research Methods (3) This 3-credit course develops research and quantitative methods related to the design and analysis of epidemiological (mostly observational) studies. Such studies assess the health and disease status of one or more human populations or identify factors associated with health and disease status. To a lesser degree, the course also covers non-randomized, intervention (experimental) studies that may be designed and analyzed with epidemiological methods. This course is a second-level course and complements Biostat Methods, STAT 509, e, which is focused on clinical (experimental) trials. Together, these two courses provide students with a complete review of research methods for the design and analysis for common studies related to human health, disease, and treatment. Prerequisite are Intro Biostats (STAT 250 or equivalent).

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 250 or equivalent

With rapid advances in information technology, the field of Applied Statistics and Data Science has witnessed an explosive growth in the capabilities to generate and collect data. In the business world, very large databases on commercial transactions are generated by retailers. Huge amounts of scientific data are generated in various fields as well using a wide assortment of high throughput technologies. The internet provides another example of billions of web pages consisting of textual and multimedia information that is used by millions of people. Analyzing large complex bodies of data systematically and efficiently remains a challenging problem. This course addresses this problem by covering techniques and new software that automate the analysis and exploration of large complex data sets. Data Mining methods are introduced by using examples to demonstrate the power of the statistical methods for exploring structure in data sets, discovering patterns in data, making predictions, and reducing the dimensionality by Principal Component Analysis (PCA) and other tools for visualization of high dimensional data. Exploratory data analysis, classification methods, clustering methods, and other statistical and algorithmic tools are presented and applied to actual data. In particular, the course investigates classification methods (supervised learning), and clustering methods (unsupervised learning), and other statistical and algorithmic tools as they are applied to actual

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

An introduction to the design and statistical analysis of randomized and observational studies in biomedical research. STAT 509 Design and Analysis of Clinical Trials (3) 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, toxicologic experiments, and epidemiological studies. Many of these topics do not appear in other statistics courses, although a few topics are covered in greater depth in more advanced statistics courses. Computations are performed via the SAS statistical software package. Evaluation methods include four to five homework assignments, an in-class mid-semester examination and an in-class final examination.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 500

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

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 462 or STAT 501 or STAT 511

Multiple regression methodology using matrix notation; linear, polynomial, and nonlinear models; indicator variables; AOV models; piece- wise regression, autocorrelation; residual analyses.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 500 or equivalent; matrix algebra; calculus

AOV, unbalanced, nested factors; CRD, RCBD, Latin squares, split-plot, and repeatd measures; incomplete block, fractional factorial, response surface designs; confounding.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 511

its STAT 514 Theory of Statistics II 3

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu

Sufficiency, completeness, likelihood, estimation, testing, decision theory, Bayesian inference, sequential procedures, multivariate distributions and inference, nonparametric inference.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 513

3 Statistical Consulting 3 STAT 580 Statistical Consulting Practicum I 2

Subject

STAT

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu

Measure theoretic foundation of probability, distribution functions and laws, types of convergence, central limit problem, conditional probability, special topics. Cross-listed with: MATH 517

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

MATH 403

Measure theoretic foundation of probability, distribution functions and laws, types of convergence, central limit problem, conditional probability, special topics. Cross-listed with: MATH 518

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 517

Selected topics in stochastic processes, including Markov and Wiener processes; stochastic integrals, optimization, and control; optimal filtering. Cross-listed with: MATH 519

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 516 , STAT 517

Location estimation, 2- and K- sample problems, matched pairs, tests for association and covariance analysis when the data are censored.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 512 , STAT 514

Computational foundations of statistics; algorithms for linear and nonlinear models, discrete algorithms in statistics, graphics, missing data, Monte Carlo techniques.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 501 or STAT 511 ; STAT 415 ; matrix algebra

Two-way tables; generalized linear models; logistic and conditional logistic models; loglinear models; fitting strategies; model selection; residual analysis.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 512 , STAT 514

A coordinate-free treatment of the theory of univariate linear models, including multiple regression and analysis of variance models. or MATH 441

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

MATH 415 or STAT 415 or STAT 514 ; STAT 512 ; MATH 436

Treatment of other normal models, including generalized linear, repeated measures, random effects, mixed, correlation, and some multivariate models.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 551

A rigorous but non-measure-theoretic introduction to statistical large- sample theory for Ph.D. students. STAT 553 Asymptotic Tools (3) STAT 553 covers most standard statistical asymptotics theory but does not require any knowledge of measure theory (it does not define convergence with probability one, for example). It covers convergence Graduate - The Pennsylvania State University 2026-2027 1421 of random variables in both the univariate and multivariate settings, Slutsky's theorem(s) and the delta method, the Lindeberg-Feller central limit theorem, power and sample size, likelihood-based estimation and testing, and U-statistics. Although there is no measure theory in the course, it is a mathematically rigorous course and major results are proved. Many common applications of the theory in mathematical statistics are discussed, and most assignments require the use of a computer.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 513 and STAT 514

Statistical Analysis of High Throughput Biology Experiments. Cross-listed with: BIOL 555, MCIBS 555

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

This course introduces data mining and statistical/machine learning, and their applications in information retrieval, database management, and image analysis. STAT 557 Data Mining I With rapid advances in information technology, we have witnessed an explosive growth in our capabilities to generate and collect data in the last decade. In the business world, very large databases on commercial transactions have been generated by retailers. Huge amount of scientific data have been generated in various fields as well. For instance, the human genome database project has collected gigabytes of data on the human genetic code. The World Wide Web provides another example with billions of web pages consisting of textual and multimedia information that are used by millions of people. How to analyze huge bodies of data so that they can be understood and used efficiently remains a challenging problem. Data mining addresses this problem by providing techniques and software to automate the analysis and exploration of large complex data sets. Research on data mining have been pursued by researchers in a wide variety of fields, including statistics, machine learning, database management and data visualization. This course on data mining will cover methodology, major software tools and applications in this field. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. Considerable amount of effort will also be put on computational aspects of algorithm implementation. To make an algorithm efficient for handling very large scale data sets, issues such as algorithm scalability need to be carefully analyzed. Data mining and learning techniques developed in fields other than statistics, e.g., machine learning and signal processing, will also be introduced. Example topics include linear classification/regression, logistic regression, model regularization, dimension reduction, prototype methods, decision trees, mixture models, and hidden Markov models. Students will be required to work on projects to practice applying existing software and to a certain extent, developing their own algorithms. Classes will be provided in three forms: lecture, project discussion, and special topic survey/research applications. Project discussion will enable students to share and compare ideas with each other and to receive specific guidance from the instructors. Efforts will be made to help students formulate real-world problems into mathematical models so that suitable algorithms can be applied with consideration of computational constraints. By surveying special topics, students will be exposed to massive literature and become more aware of recent research. Students are strongly encouraged to survey or present their own applications of data mining and statistical learning

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Advanced data mining techniques: temporal pattern mining, network mining, boosting, discriminative models, generative models, data warehouse, and choosing mining algorithms. IST (STAT) 558 Data Mining II (3)This course is the second course in a two-course sequence on data mining. It emphasizes advanced concepts and techniques for data mining and their application to large-scale data warehouse. Building on the statistical foundations and underpinnings of data mining introduced in Data Mining I , this course covers advanced topics on data mining; mining association rules from large-scale data warehouse, hierarchical clustering, mining patterns from temporal data, semi-supervised learning, active learning and boosting. In addition, to computational aspects of algorithm implementation, the course will also cover architecture and implementation of data warehouse, data preprocessing (including data cleansing), and the choice of mining algorithms for applications. In addition to discriminative models such as CRF and SVM models, the course will also introduce generative models such as Bayesian Net and LDA. A term project will be developed by each student to apply an advanced data mining algorithm to a multi-dimensional data set. Classes will include lectures, paper discussions, and project presentations. Paper discussions will allow students to discuss state-of-the-art literature related to data mining. Project presentations will enable students to share and compare project ideas with each other and to receive feedback from the instructor. Cross-listed with: IST 558

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 557 or IST 557

Classical optimal hypothesis test and confidence regions, Bayesian inference, Bayesian computation, large sample relationship between Bayesian and classical procedures.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 514; Concurrent: STAT 517

Basic limit theorems; asymptotically efficient estimators and tests; local asymptotic analysis; estimating equations and generalized linear models.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 561

Theoretical treatment of methods for analyzing multivariate data, including Hotelling's T2, MANOVA, discrimination, principal components, and canonical analysis.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 505 , STAT 551

General principles of statistical consulting and statistical consulting experience. Preparation of reports, presentations, and communication aspects of consulting are discussed. Students will be working on client provided short on-call and long term projects. STAT 504; STAT 506; STAT 510

Subject

STAT

Credits (min)

2

Credits (max)

2

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

STAT 502, STAT 505; STAT 508; STAT 557, STAT 503;

3 Electives 3 6 credits of electives 6

Subject

STAT

Credits (min)

1

Credits (max)

1

Type

course

Edition

graduate

Source

bulletins.psu.edu

Computational methods for modern machine learning models, including applications to big data and non-differentiable objective functions. Cross-listed with: CSE 584

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

/Maximum of 3 Continuing seminars which consist of a series of individual lectures by faculty, students, or outside speakers.

Subject

STAT

Credits (min)

1

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

This course is designed to help students become better teachers and communicators of statistics. INTAF 592 Teaching Statistics (1) This course is designed to help students become better teachers and communicators of statistics, and specifically to prepare students to supervise undergraduate statistics students in labs or small group settings, or even to lead their own undergraduate courses. Students learn about and discuss pedagogy in statistics, gain experience with practice teaching, and improve via individual feedback.

Subject

STAT

Credits (min)

1

Credits (max)

1

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

/Maximum of 9 Creative projects, including nonthesis research, which are supervised on an individual basis and which fall outside the scope of formal courses.

Subject

STAT

Credits (min)

1

Credits (max)

9

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

/Maximum of 9 Formal courses given on a topical or special interest subject which may be offered infrequently; several different topics may be taught in one year or term.

Subject

STAT

Credits (min)

1

Credits (max)

9

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

/Maximum of 999 No description.

Subject

STAT

Credits (min)

1

Credits (max)

15

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

0 Credits/Maximum of 999 No description.

Subject

STAT

Type

course

Edition

graduate

Source

bulletins.psu.edu

/Maximum of 999 No description.

Subject

STAT

Credits (min)

1

Credits (max)

15

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Investigates methods for assessing data collected from experimental and/or observational studies in various research setting. STAT 800 Applied Research Methods (3) This course provides students with a broad exploration of the tools and methods in Applied Statistics. In particular, it investigates basic probability distributions and methods for assessing data collected from experimental and/or observational studies in social science and other research settings. Students learn methods of point and interval estimation, including sample size determinations required to achieve a prescribed margin of error. Additionally, students examine hypothesis testing and the determination of sample sizes to achieve a prescribed power of a given test. The distinction between observational studies and randomized experiments is clarified and the limitations of the conclusions are emphasized. Research articles that are relevant to students' fields of study are used to determine how these statistical methods are being applied. Students then identify and critique appropriate research methods. Students work with various data sets to establish fundamental practices that properly analyze data and interpret results via either Minitab or SPSS statistical software as they formulate and communicate conclusions based on a given research context.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

This course is designed to build upon a student's undergraduate quantitative backgrounds by giving an overview of multivariate statistical techniques. Many applied fields often require the use of large, multivariate data sets and students need to be aware of the wide range of statistical tools available to them. Major objectives of this course are to gain a working knowledge of probability theory, univariate and Graduate - The Pennsylvania State University 2026-2027 1423 multivariate statistics, the use of copulas, Monte Carlo techniques, and multiple linear regression. Throughout the course, students will have the opportunity to apply these concepts to real world data sets using modern statistical software packages.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

This course is designed to build upon a student's background by giving an overview of the techniques of time series analysis often used in applied settings. Many areas of research and application often utilize long time series of data in an effort to model changes and volatility in data measured consistently over time. Major objectives in this course include an overview of linear time series; AR, MA, and ARIMA models; ARCH and GARCH models; nonlinear time series models; multivariate time series models; and models of high-frequency data. Throughout the course, students will have the opportunity to apply these concepts to real world data sets using modern statistical software packages.

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

( MFE 801, STAT 805; STAT 505 )

/Maximum of 9 Formal courses given on a topical or special interest subject which may be offered infrequently; several different topics may be taught in one year or term.

Subject

STAT

Credits (min)

1

Credits (max)

9

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Supply Chain and Information

Subject

STAT

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu