Why Study Statistics?
Statistics is the science which studies data – its collection, description, analysis, and interpretation. Almost all modern professions, from economists to engineers and from social scientists to medical scientists, rely on statistics. Statistical methods are used for studying relationships, predicting results, testing hypothesis, and a variety of other purposes.
The Bachelor of Science degree in statistics is designed to provide students with a fundamental understanding of probability and mathematical statistics, a complementary knowledge of basic methods for data collection and inference, and practical computing skills to carry out statistical analyses of problems in many different areas of application.
One option within the major allows students with a strong interest in statistical
or biostatistical aspects of a particular science to apply courses in that science
to their major. This option should provide the interested student with a good background
for employment or graduate work in statistics, biostatistics, or in that science.
Emphases in actuarial science and data science enable students to pursue further specializations
aligned with professional opportunities in these areas.
Statistics is the discipline at the heart of the scientific method of discovery. Statistical
principles are used in designing experiments and surveys to collect information, and
statistical procedures are applied to summarize information, draw conclusions, and
make decisions.
Because of the broad applicability of their training in statistical reasoning and data analysis, undergraduate majors are prepared for careers in diverse fields – such as biotechnology, environmental science, insurance, industrial manufacturing, and market research – in which the need for professionally trained statisticians is great.
Graduates who seek to acquire additional skills in applied or theoretical statistics may also consider programs of advanced study at the master’s or doctoral level. Statisticians with advanced degrees are sought for senior positions in industry and government, as well as teaching positions in secondary schools, community colleges, and universities.
Bachelor's Degree Options in Statistics
The Department offers three options for a Bachelor of Science degree in Statistics and a Minor.
- Bachelor of Science in Statistics
- Bachelor of Science in Statistics, emphasis in Actuarial Science
- Bachelor of Science in Statistics, emphasis in Data Science
- Minor in Statistics
Degree Learning Outcomes for Statistics Majors
The different degree programs vary in the emphasis placed on each learning outcome.
- Demonstrate knowledge of basic statistical vocabulary and concepts. Students will
be able to understand statistics concepts and vocabulary. Statistical terms such as
population, sample, central tendency, dispersion, likelihood, parameter and interval
estimation, hypothesis testing, and decision theory will be introduced.
Introduced: Stat 119, 250, 350A
Practiced: Stat 350B, 520, 560
Demonstrated: Stat 550, 551AB, 575
- Use calculus and algebra to study statistical inferences and modeling. When asked
to identify and compare basic statistical concepts, students will be able to define
terms, use definitions, form arguments using appropriate vocabulary, develop and manipulate
mathematical formulations, and utilize the elementary methods of proof.
Introduced: Stat 350A
Practiced: Stat 520, 560
Demonstrated: Stat 550, 551AB, 575
- Interpret statistical inferences. Given data, students will be able to construct and
interpret interval estimates for population parameters. Given data and a model, construct
and interpret interval estimates for model parameters. Formulate and test statistical
hypotheses; interpret results. Explain problems with the way traditional Fisherian
inference has been applied in the scientific community.
Introduced: Stat 250
Practiced: Stat 350 AB
Demonstrated: Stat 410, 551 AB, 560, 575, 580
- Evaluate and fit probability models. Students will be able to compare and contrast
probability models for characterizing populations. Given data, students will be able
to fit these probability models by a suite of methods. Students will be able to use
probability theory and foundational statistical principles to assess model fits and
draw statistical inferences, both asymptotically and in finite samples. Students will
know how to utilize statistical software such as SAS and R to perform model fitting
and assessment, and develop the building blocks to extend these ideas to new models
and scientific problems.
Introduced: Stat 200, 250
Practiced: Stat 350A, 350B, 551A, 551B
Demonstrated: Stat 410, 520, 575, 580
- Use statistical software appropriately. Given a real life data set, students will
be able to apply statistical software to conduct data analyses, visualize the data,
check model assumptions, and interpret the output from statistical software.
Introduced: Stat 250, 325, 410
Practiced: Stat 520, 550
Demonstrated: Stat 580 - Communicate and Report Statistical Findings. Students will be able to communicate
statistical inferences to both the lay person and to scientific audiences. Oral communication
skills will include conference-style lecture presentations, business meeting presentations,
and small group tutorials. Written communication skills will include statistical
analysis reports, tutorials, and conceptual short text pieces. Students will be able
to use statistical software such as SAS and R to visualize data and predictive analytics
results to communicate statistical solutions to scientific problems.
Introduced: Stat 200, 250, 350A
Practiced: Stat 325, 350B, 410
Demonstrated: Stat 520, 560, 575, 580 - Apply statistical models to data. Students will be able to determine which statistical
methods are appropriate for a given dataset based on whether required mathematical
assumptions are met, and whether the methods provide evidence useful in answering
relevant questions. For appropriately chosen methods, students will utilize statistical
software such as SAS and R to implement the analysis and summarize findings. Students
will perform diagnostic tests to evaluate the performance of statistical methods (e.g.,
evaluating model fit, checking for expected behavior in numerical algorithms).
Introduced: Stat 200, 250, 350A
Practiced: Stat 325, 350B, 410
Demonstrated: Stat 520, 551B, 560, 575, 580