Program Requirements
Statistics Course Requirements
The Statistics major consists of at least 41 credit hours, as follows:
| Required courses | (32–35) | |
| MATH 120C | P4 Calculus I (4) | |
| MATH 122C | P4 Calculus II (4) | |
| MATH 232 | Linear Algebra (3) | |
| MATH 301 | SQ Mathematical Statistics I (3) | |
| MATH 302 | Mathematical Statistics II (3) | |
| MATH 410 | Probability Models (3) | |
| STAT 205 | SQ Design and Analysis of Experiments (3) | |
| STAT 210 | Regression Analysis (3) | |
| STAT 390 | Special Topics in Statistics (3) | |
| STAT 490 | Field Experience (May be taken more than once for a total of 6 credit hours) (3–6) |
|
| Elective courses* | (9) | |
| Choose three: | CSCI 161 P4 Foundations of Computer Science I (3) | |
| ECON 222 Nonparametric Statistics (3) | ||
| ECON 314 Introduction to Econometrics (3) | ||
| MATH 221C Calculus III (4) | ||
| MATH 260 Applied Mathematical Statistics (3) | ||
| PSYC 386 Survey Design and Analysis (3) | ||
| PSYC 388 Testing and Measurements (3) | ||
| STAT 260 Introduction to Meta-Analysis (3) | ||
| STAT 496 Independent Study (3) | ||
| Total | (41–44) | |
*Students may substitute a course not listed for a required Statistics elective with the approval of the major advisor.
For students majoring in Statistics, all courses that are required or may be used as electives for the major are included in the determination of the grade point average in the major.
At least 21 of the required 41 credits (one half of the major) must be completed in residence at St. John Fisher College.
Since the Statistics major is completed as part of a Bachelor of Arts degree, a minor or second major is required. A Statistics major may NOT minor in Mathematics.
Program Goals and Student Learning Outcomes
Goal #1 Statistical: Be trained and experienced in statistical reasoning, in designing studies (including practical aspects), in exploratory analysis of data by graphical and other means, and in a variety of formal inference procedures.
- Knowledge of statistical theory (e.g., distributions of random variables, point and interval estimation, hypothesis testing, Bayesian methods)
- Knowledge of graphical data analysis methods
- Competency in the design of studies (e.g., random assignments, replication, blocking, analysis of variance, fixed and random effects, diagnostics in experiments; random sampling, stratification in sample surveys; data exploration in observational studies)
- Be able to do statistical modeling (e.g., simple, multiple, and logistic regression; categorical data; diagnostics; data mining)
Goal #2 Computational: Be familiar with standard statistical software, data management and algorithmic problem solving.
- Have an understanding of programming concepts and their applications in statistics
- Knowledge of the professional statistical software appropriate for a variety of tasks
Goal #3 Mathematical: Have knowledge of probability, statistical theory, and any prerequisite mathematics (especially calculus and linear algebra).
- Knowledge of Calculus (integration and differentiation) through multivariable calculus
- Knowledge of linear algebra (emphasis on matrix manipulations, linear transformations, projections in Euclidean space, eigenvalue/eigenvector decomposition and singular value decomposition)
- Probability: emphasis on connections between concepts and their applications in statistics
Goal #4 Communicating & Consulting: Be able to write clearly, speak fluently, and have developed skills in collaboration and teamwork and organizing and managing projects.
- Be able to demonstrate effective technical writing and presentations skills
- Demonstrate teamwork and collaborative skills
- Demonstrate effective planning for data collection
- Competency in data management