Courses

Penn State's 12-credit online Graduate Certificate in Applied Statistics consists of required and elective courses that can deepen your knowledge of statistical analysis and provide you with:

training on SAS and Minitab software packages
a blend of practical and theoretical data-analysis skills
sophisticated tools and knowledge to handle and analyze data

The graduate certificate allows you to simultaneously gain graduate credit and a highly valued skill set. The applied statistics program is designed as a "stand-alone" certificate or can serve as a "step-up" program into a master's degree — including the Master of Applied Statistics degree.

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.

To earn the Graduate Certificate in Applied Statistics, you will take 6 credits of required courses and choose 6 credits of electives based on your professional goals. At least 6 of the 12 credits must come from courses at the 500 level or above. Please note only 3 credits of statistical programming will count towards your certificate. A minimum 3.0 GPA is required to obtain the certificate.

Required Courses (6 credits)

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

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

Prerequisite
6 credits of statistics or STAT 500; matrix algebra

Elective Courses (select 6 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
  
  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.
- or all of:
  - 1
    
    credit
    
    Selection and evaluation of statistical computer packages.
    
    Prerequisite
    3 credits in statistics
  - and:
    1
    
    credit
    
    Intermediate SAS for data management.
    
    Prerequisite
    STAT 480
  - and:
    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
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

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
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; STAT 502
3

credits

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

Prerequisite
STAT 500, STAT 501, and STAT 502; matrix algebra
3

credits

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

Prerequisite
STAT 501 and STAT 502; matrix algebra
3

credits

Theory and application of sampling from finite populations.

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

credits

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.

Prerequisite
STAT 500
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

Course Availability

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