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
3 Credits

Statistics is directed toward majors in the Schools of Nursing and Health Sciences, Communications and Information Systems, Education and Social Sciences and some majors in Mathematics and Science. This course is designed to give students the statistical tools needed to make decisions and interpret results based on sample data. Grouped and ungrouped data will be analyzed through descriptive statistics, including: frequency distributions, measures of central tendency, and measures of dispersion. Techniques for the presentation and interpretation of results will include bar graphs, histograms, frequency and relative frequency tables. Basic concepts of probability theory are briefly examined. Various distributions of sample data including: binomial, normal and uniform distributions are explored. Statistical inference will include detailed discussion of confidence interval estimation and hypothesis testing including problems involving proportions and means of both one and two populations, and large and small samples. The last part of the course will cover correlation and regression. Interpretation of results will be emphasized. Technologies used may include Excel and the TI 83/84 calculator.
Prerequisite: MATH1010, MATH1020, MATH1050, or MATH2040 3 Credits
3 Credits

Course description unavailable, please contact Academic Services.

3 Credits

This course introduces the students to the application of statistical methodology in business, education and science. The first part of the course deals with the efficient collection, organization, and description of data, commonly referred to as descriptive statistics. Several techniques for describing the data graphically and numerically will be covered. Basic concepts of probability theory are covered next. Then, the second part of the course focuses on statistical inference, the process of drawing conclusions about unknown characteristics of a population from which the data were taken. Interval estimation and hypothesis testing will be discussed in detail. The course also includes predictive statistics with its purpose to develop predictions of future values based on historical data using correlation and regression analysis. Finally, the course covers goodness-of-fit and contingency table analysis. In this course, students will learn to use Excel and R to solve statistical problems. Students will complete a project as part of their coursework. Pre-req: MATH 1010 or MATH 1020 or MATH 1050 or MATH 2040 and admission to the University Honors Program
3 Credits

This course is the first of a two-course sequence in calculus-based probability. Topics include basic combinatorics methods: basic set theory; laws of probability; conditional probability and independence; Bayes Theorem; probability mass functions; cumulative distribution functions; probability-generating functions; commonly-used discrete variables (Bernoulli, binomial, Poisson, geometric, negative binomial, hyper geometric); expectation; variance; discrete conditional distributions.
Prerequisite: MATH2070 or permission of the instructor 3 Credits
3 Credits

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
3 Credits

This course is the second of a two-course sequence in calculus-based probability. Topics include density functions; expectation; variance; covariance; correlation; mixed distributions with applications to insurance; moment-generating functions; commonly-used continuous distributions (uniform, exponential, normal, lognormal, gamma, beta, Pareto and Weibull); hazard rate functions; independence; joint distributions; multivariate distributions; continuous conditional distributions; bivariate normal distributions.
Prerequisites: STAT3140 and MATH2170 3 Credits
3 Credits

Course description unavailable, please contact Academic Services. Please try again at a later time 2 Credits
2 Credits

Course description unavailable, please contact Academic Services. Please try again at a later time 3 Credits
3 Credits

Course description unavailable, please contact Academic Services.

6 Credits

Course description unavailable, please contact Academic Services.

9 Credits