Statistics: Biostatistics

Statistics: Biostatistics
BS
Hours54 - 55 Credit Hours
MAPMajor Academic Plan

Program Requirements

No more than 3 hours of credit below C- is allowed in major courses.
Requirement 1 Complete 2 Courses
Preparation core courses:
Requirement 2 Complete 1 Course
Note: Students who have passed the AP statistics exam or an introductory statistics course should not take Stat 121.
Requirement 6 Complete 2 Courses
Requirement 7 Complete 1 Course
It is strongly recommended that students interested in graduate study in biostatistics include Math 341 and 342 in their elective lists.
Program Outcomes

Probability Problem Solving

Solve probability problems in finite sample spaces, with discrete and continuous univariate random variables, and apply the Central Limit Theorem.

Frequentist and Bayesian Inference

Demonstrate the derivation of frequentist and Bayesian inference for one-sample proportions and means

Effective Comunication and Collaboration

Write technical reports and make technical presentations containing statistical results, and work in teams to demonstrate the consulting skills of a professional statistician

Programming and Statistical Software

Write a computer program and use professional statistical software for regression and design of experiments

Technical Competence

Demonstrate competence in database concepts and terminology through preparation for the SAS Certified BASE and Advanced Programmer exams

Experiment Design and Analysis

Demonstrate the design and analysis of randomized factorial experiments and blocking at the level of a professional statistician

Multiple Regression Models

Demonstrate multiple regression modeling at the level of a professional statistician

Fitting Statistical Models

Demonstrate competence in fitting logistic regression and fitting ARIMA time series models

Calculus and Statistics

Apply the results of differential, integral, and multivariate calculus to problems and mathematical statistics

Matrix Computation and Application

Demonstrate competence with matrix computation and apply results from linear algebra to the linear model

Probability Problem Solving

Solve probability problems in finite sample spaces, with discrete and continuous univariate random variables, and apply the Central Limit Theorem.

Frequentist and Bayesian Inference

Demonstrate the derivation of frequentist and Bayesian inference for one-sample proportions and means

Effective Comunication and Collaboration

Write technical reports and make technical presentations containing statistical results, and work in teams to demonstrate the consulting skills of a professional statistician

Programming and Statistical Software

Write a computer program and use professional statistical software for regression and design of experiments

Technical Competence

Demonstrate competence in database concepts and terminology through preparation for the SAS Certified BASE and Advanced Programmer exams

Experiment Design and Analysis

Demonstrate the design and analysis of randomized factorial experiments and blocking at the level of a professional statistician

Multiple Regression Models

Demonstrate multiple regression modeling at the level of a professional statistician

Fitting Statistical Models

Demonstrate competence in fitting logistic regression and fitting ARIMA time series models

Calculus and Statistics

Apply the results of differential, integral, and multivariate calculus to problems and mathematical statistics

Matrix Computation and Application

Demonstrate competence with matrix computation and apply results from linear algebra to the linear model