Degrees and Programs offered through the Mathematics Department

Statistics  
List of all courses and their descriptions
List of all courses, their descriptions and offerings in the schedule book

STAT1130 - Statistical Reasoning
Fall 2016

This is an online statistics course for ACE students. It provides an introduction to the application of non-theoretical statistical methodology in which concepts are explained intuitively and supported by examples. The first part of the course deals with the efficient collection, organization, and description of data, commonly referred to as descriptive statistics. The second part provides a brief introduction to probability concepts, discrete and continuous probability distributions will be discussed. The third part of the course, statistical interference, is the process of drawing conclusions about unknown characteristics of a population. Interval estimation and hypothesis testing for one and two populations will be presented. The last part of the course is predictive statistics; correlation and regression analysis will be used to develop predictions of future values based on historical data. 3 credits
3 Credits

STAT2110 - Statistics
Fall 2016

This course is designed to give the student a general understanding of statistical tools as they are used in making decisions. The examples and exercises emphasize interpretation and are drawn from a wide range of actual situations. Excel computer software is used to simplify complex computations. The first section covers analysis decision-making through the use of summarized descriptive data, both ungrouped and grouped. This includes the study of frequency distributions, measures of central tendency and measures of dispersion. This section then deals with decision-making based upon classical hypothesis testing. It includes the study of curve fitting, large and small sample theory, the z distribution and the student's distribution. This coverage provides the student with a working understanding of statistical inference as a guide for judgment and action. The second section deals with more complex situations in decision-making with presentation of analysis of variance and chi-square testing with one and two variables and multiple outcomes of each variable. Excel is employed to handle the more complex calculations. The third section introduces the concepts and theories of correlation and linear regression and how these are used in statistical inference.
Prerequisite: MATH1010, MATH1020, MATH1050, or MATH2040
3 Credits

STAT2125 - Honors Statistics
Fall 2016

This course is designed to give the student a general understanding of statistical tools as they are used in making decisions. The examples and exercises emphasize interpretation and are drawn from a wide range of actual situations. Excel computer software is used to simplify complex computations. The first section covers analysis decision-making through the use of summarized descriptive data, both ungrouped and grouped. This includes the study of frequency distributions, measures of central tendency and measures of dispersion. This section then deals with decision-making based upon classical hypothesis testing. It includes the study of curve fitting, large and small sample theory, the z distribution and the student's distribution. This coverage provides the student with a working understanding of statistical inference as a guide for judgment and action. The second section deals with more complex situations in decision-making with presentation of analysis of variance and chi-square testing with one and two variables and multiple outcomes of each variable. Excel is employed to handle the more complex calculations. The third section introduces the concepts and theories of correlation and linear regression and how these are used in statistical inference.
Prerequisite: MATH1010, MATH1020, MATH1050, or MATH2040
3 Credits

STAT3120 - Statistics II
Fall 2016

This course continues the study of small sample statistical inference and hypothesis testing. Linear regression is introduced, together with Chi-square and an examination of variance techniques necessary to analyze experiments involving several independent variables. Additional topics include Bayesian decision-making theory, time series analysis, seasonal and cyclical trends, and index numbers. Examples and problems are taken from topics pertaining to business experience. Actuarial Science majors may not take QS312 Statistics II for credit toward a B.S. or B.A. degree.
Prerequisite: STAT2110 (QS211)
3 Credits

STAT3140 - Probability/Math Statistics I
Fall 2016

This course is directed to the mathematics major. The course includes basic probability analysis, marginal probability, conditional probability, random variables (both discrete and continuous), expectation, and consideration of special distribution. Both theory and application of methods are emphasized. Software proficiency in word processing is required.
Prerequisite: MATH2070 or permission of the instructor
3 Credits

STAT3145 - Probability/Math Stats I &Ii
Fall 2016

STAT3145 is an accelerated version of the STAT3140-STAT3150 courses, designed to prepare well-qualified students to sit for the SOA/CAS Professional Actuarial Exam P/1 at the conclusion of the fall semester. The class will cover the entire actuarial exam syllabus, consisting of: General Rules of Probability, Discrete Random Variables, Calculus and Continuous Random Variables, Special Discrete and Continuous Probability Distributions and Multivariate Distributions. The course emphasizes applications to insurance and risk management, and students will be continually drilled on typical actuarial exam questions in order to master the material in the one-semester time frame.
Prerequisites: MATH2170 and Permission of the Department Head
3 Credits

STAT3150 - Probability/Math Statistics II
Fall 2016

This is directed to the mathematics major. The course includes the theory of estimation, sampling distribution theory, the testing of hypotheses, nonparametric methods of statistical inference, linear statistical models of regression, and analysis of variance. Software proficiency in word processing is required.
Prerequisites: STAT3140 and MATH2170
3 Credits

STAT4230 - Time Series Analysis
Fall 2016

This course introduces the mathematical and statistical foundations necessary for the study of time dependent sets, more commonly referred to as time series. The estimation of model parameters and the application of the models to forecasting constitute the main themes of the course. Specific topics include smoothing techniques, the tudy of autocorrelation, and the standard auto regressive integrated moving average (ARIMA) models.
Prerequiisite: ASCI4200 or permission of the instructor
3 Credits

STAT4250 - Regression Analysis
Fall 2016

This course aims to provide students with a solid background in regression analysis at an advanced undergraduate level. The course covers the basic techniques of both the linear model and the generalized linear model. It prepares students for Statistics for Risk Modeling (SRM) Exam offered by the Society of Actuaries.
Prerequisites: ASCI4200 and MATH3400
3 Credits

STAT4300 - Hlth Care Predictive Analytics
Fall 2016

This course introduces students to contemporary topics at an advanced level in health care statistical modeling and predictive analytics. depending on employer's interests and student's needs, topics will be selected from such areas as models for predicting health costs, clinical identification algorithms, grouper models, and development and construction of diagnosis related groups (DRGs), diagonal cost group (DCGs), and episode treatment groups (ETGs).
Prerequisites: ASCI4230 and ASCI4250
3 Credits

STAT4350 - Predictive Analytics
Fall 2016

This course is designed to give students exposure to developing actuarial, statistical, and predictive models, and analyzing the types of data that are relevant to various areas of actuarial science, including life insurance, property/casualty insurance (general insurance), and pensions. Students will do this through a variety of projects using a combination of statistical modeling and predictive analytics methods with a focus on the communication of results. This course builds on the types of analysis learned in more basic courses; it assumes a working knowledge of spreadsheet and statistical software packages, as well as an understanding of the basic regression and time series models.
Prerequisites: ASCI2010, ASCI4140, ASCI4230, and ASCI4250
3 Credits