## Undergraduate Course List

Course Number |
Course Name |
Description |

STAT 1000Q | Introduction to Statistics I | A standard approach to statistical analysis primarily for students of business and economics; elementary probability, sampling distributions, normal theory estimation and hypothesis testing, regression and correlation, exploratory data analysis. Learning to do statistical analysis on a personal computer is an integral part of the course. |

STAT 1100Q | Elementary Concepts of Statistics | Standard and nonparametric approaches to statistical analysis; exploratory data analysis, elementary probability, sampling distributions, estimation and hypothesis testing, one- and two-sample procedures, regression and correlation. Learning to do statistical analysis on a personal computer is an integral part of the course. |

STAT 2215Q | Introduction to Statistics II | Analysis of variance, multiple regression, chi-square tests, and non-parametric procedures. |

STAT 2255 | Statistical Programming | Introduction to statistical programming via Python including data types, control flow, object-oriented programming, and graphical user interface-driven applications such as Jupiter notebooks. Emphasis on algorithmic thinking, efficient implementation of different data structures, control and data abstraction, file processing, and data analysis and visualization.
Prerequisites: MATH 1131Q and MATH1132Q, or instructor consent. |

STAT 3005 | Biostatistics for Health Professions | Introduction to biostatistical techniques, concepts, and reasoning using a broad range of biomedical and public health related scenarios. Specific topics include description of data, statistical hypothesis testing and its application to group comparisons, and tools for modeling different type of data, including categorical, and time-event, data. Emphasis on the distinction of these methods, their implementation using statistical software, and the interpretation of results applied to health sciences research questions and variables. |

STAT 3025Q | Statistical Methods (Calculus Level I) | Basic probability distributions, point and interval estimation, tests of hypotheses, correlation and regression, analysis of variance, experimental design, non-parametric procedures. |

STAT 3115Q | Analysis of Experiments | Straight-line regression, multiple regression, regression diagnostics, transformations, dummy variables, one-way and two-way analysis of variance, analysis of covariance, stepwise regression. |

STAT 3255 | Introduction to Data Science | Introduction to data science for effectively storing, processing, analyzing and making inferences from data. Topics include project management, data preparation, data visualization, statistical models, machine learning, distributed computing, and ethics.
Prerequisites: STAT 2255 and STAT 3115Q, or instructor consent. |

STAT 3345Q | Probability Models for Engineers | Probability set functions, random variables, expectations, moment generating functions, discrete and continuous random variables, joint and conditional distributions, multinomial distribution, bivariate normal distribution, functions of random variables, central limit theorems, computer simulation of probability models. |

STAT 3375Q | Introduction to Mathematical Statistics | The mathematical theory underlying statistical methods. Probability spaces, distributions in one and several dimensions, generating functions, limit theorems, sampling, parameter estimation. Neyman-Pearson theory of hypothesis testing, correlation, regression, analysis of variance. |

STAT 3445 | Introduction to Mathematical Statistics | The mathematical theory underlying statistical methods. Probability spaces, distributions in one and several dimensions, generating functions, limit theorems, sampling, parameter estimation. Neyman-Pearson theory of hypothesis testing, correlation, regression, analysis of variance. |

STAT 3494W | Undergraduate Seminar II | The student will attend 6-8 seminars per semester, and choose one statistical topic to investigate in detail. The student will write a well revised comprehensive paper on this topic, including a literature review, description of technical details, and a summary and discussion. |

STAT 3515Q | Design of Experiments | Methods of designing experiments utilizing regression analysis and the analysis of variance. |

STAT 3675Q | Statistical Computing | Introduction to computing for statistical problems; obtaining features of distributions, fitting models and implementing inference (obtaining confidence intervals and running hypothesis tests); simulation-based approaches and basic numerical methods. One hour per week devoted to computing and programming skills. |

STAT 3965 | Elementary Stochastic Processes | Conditional distributions, discrete and continuous time Markov chains, limit theorems for Markov chains, random walks, Poisson processes, compound and marked Poisson processes, and Brownian motion. Selected applications from actuarial science, biology, engineering, or finance. |

STAT 4185 | Special Topics in Data Science | The purpose of this course is to gain understanding on the basic and critical importance of data science with applications to clinical drug development. Successful completion of an introductory course in statistics is a prerequisite for this course. |

STAT 4188 | Generalized Linear Models | Statistical models for the analysis of quantitative and qualitative data, of the types usually encountered in social science, public health, biological and life sciences research. |

STAT 4190 | Field Study Internship | Supervised field work relevant to some area of Statistics with a regional industry, government agency, or non-profit organization. Evaluated by the field supervisor and by the instructor (based on a detailed written report submitted by the student). |

STAT 4255 | Introduction to Statistical Learning | Modern statistical learning methods arising frequently in data science and machine learning with real-world applications: linear and logistic regression, generalized additive models, decision trees, boosting, support vector machines, and neural networks (deep learning). Prerequisites: STAT 3115Q or instructor consent |

STAT 4299 | Independent Study | Credits and hours by arrangement. Prerequisite: Open only with consent of instructor. May be repeated for credit. |

STAT 4389 | Undergraduate Research | Supervised research in probability or statistics. A final written report and oral presentation are required. |

STAT 4475 | Statistical Quality Control and Reliability | Development of control charts, acceptance sampling and process capability indices, reliability modeling, regression models for reliability data, and proportional hazards models for survival data. |

STAT 4525 | Sampling Theory | Sampling and nonsampling error, bias, sampling design, simple random sampling, sampling with unequal probabilities, stratified sampling, optimum allocation, proportional allocation, ratio estimators, regression estimators, super population approaches, inferences in finite populations. |

STAT 4535 | Introduction to Operations Research | Introduction to the use of mathematical and statistical techniques to solve a wide variety of organizational problems. Topics include linear programming, network analysis, queueing theory, decision analysis. |

STAT 4625 | Introduction to Biostatistics | Rates and proportions, sensitivity, specificity, two-way tables, odd ratios, relative risk, ordered and non-ordered classifications, trends, case-control studies, elements of regression including logistic and Poisson, additivity and interaction, combination of studies and meta-analysis. |

STAT 4675 | Probability and Statistics Problems | Designed to help students prepare for the second actuarial examination. |

STAT 4825 | Applied Time Series | Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate, autoregressive, moving average models. |

STAT 4875 | Nonparametric Methods | Basic ideas, the empirical distribution function and its applications, uses of order statistics, one- two- and c-sample problems, rank correlation, efficiency. |