• Henri Berestycki wins the 2026 Schauder Medal

    Congratulations Henri Berestycki for winning the 2026 Schauder Medal.  This medal is awarded to Henri for his exceptional achievements in nonlinear analysis and for his numerous applications in many other branches of science.  Henri has been spending one semester per year in our department since 2023.  Among many other awards, Henri is an Read More
  • Doron Levy is elected SIAM Class of 2026 Fellow

    Doron Levy was elected Fellow of the Society for Industrial and Applied Mathematics (SIAM), class of 2026:  https://www.siam.org/publications/siam-news/articles/siam-announces-2026-class-of-fellows.   Dr. Levy is recognized for his amazingly-stellar and sustained distinguished contributions to research and training in mathematical oncology and mathematical biology.  This exceedingly well-deserved award is fantastic for our department and university. Read More
  • Artem Chernikov awarded the Bessel Research Award by the Humboldt Foundation

    This award is given annually to internationally renowned academics from outside of Germany in recognition of their research accomplishments.  This award is named after Bessel and funded by the German ministry of education and research. Congratulations Atrem Chernikov.  https://www.humboldt-foundation.de/en/apply/sponsorship-programmes/friedrich-wilhelm-bessel-research-award  Read More
  • Mapping the Mind

    Junior computer science and mathematics double major Brooke Guo analyzes neural connections to understand the causes of complex brain conditions like schizophrenia.  When Brooke Guo arrived at the University of Maryland as a freshman in 2022, she knew she wanted to help people and work in a health-related field someday. Read More
  • Four Science Terps Awarded 2025 Goldwater Scholarships

    Four undergraduates in the University of Maryland’s College of Computer, Mathematical, and Natural Sciences (CMNS) have been awarded 2025 scholarships by the Barry Goldwater Scholarship and Excellence in Education Foundation, which encourages students to pursue advanced study and research careers in the sciences, engineering and mathematics.  Over the last 16 years, UMD’s nominations Read More
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Description

STAT 430 will introduce modern techniques of computational statistics for practical analysis of data. The course will utilize the SAS software system, which is widely used both in statistical applications and in corporate data management applications. Data analysis and interpretation will be emphasized, rather than statistical theory. At every point in the course real world data sets will be used to illustrate statistical principles.

For undergraduates in mathematics or computer science seeking statistical employment after graduation, SAS is a valuable job credential. For graduate students in fields which use statistics heavily, such as agriculture, life science, education, engineering or social science, SAS is a powerful aid to research.  Graduate students who have successfully taken an applied statistics course in their own department should have no difficulty with STAT 430.

Prerequisites

1 course with a minimum grade of C- from (STAT400, STAT410).

co-requisite starting Spring 2023 1 course with a minimum grade of C- from (STAT401, STAT420).


Level of Rigor

Standard


Sample Textbooks

Applied Statistics and the SAS Programming Language, by R.P. Cody and J.K. Smith

The Little SAS Book: A Primer, by L. Delwiche & S. Slaughter


Applications



If you like this course, you might also consider the following courses



Additional Notes

Students interested in grad school in STAT should consider this course 

Topics

Brief review of statistical concepts

Descriptive statistics

Points estimators

Confidence intervals

Hypothesis testing

Introduction to the SAS software system

Data screening

Data summaries and graphics

Creating and manipulating data sets

Merging, sorting and splitting data sets

Least squares and fitting models

Straight line fits

Graphical tools for model criticism

Multiple predictors

Categorical predictors and analysis of variance

Categorical data

Frequency tables

Logistic regression

Models for count data

Simulation

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