# Statistics: Statistical Science

Statistics: Statistical Science
BS
 Hours 48 Credit Hours MAP Major 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 3 Complete 9 Courses
Statistics core courses:
Requirement 4 Complete 2 Courses
Requirement 5 Complete 12.0 hours from the following Courses
It is strongly recommended that students interested in graduate study in statistics choose electives to prepare for the BYU BS/MS statistics integrated program by meeting with the statistics graduate coordinator.
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 Communication 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 exam

#### 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

Deomstrate 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 in probability and mathematics

#### 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 Communication 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 exam

#### 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

Deomstrate 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 in probability and mathematics

#### Matrix Computation and Application

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