|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
Statistical models in nonparametric settings. Theory and practice using techniques requiring less restrictive assumptions about the distribution of the data. Nonparametric analogues of t- and F-tests in one and two sample settings, ANOVA, regression and correlation will be discussed.
-
3.00 Credits
Statistical computing considers how data are processed and analyzed, and how statistical models are simulated, in a computational setting. The current landscape of the statistical computing community will be explored, including common statistical software, proprietary versus open-source statistical languages, and how statistical software packages are tailored for specific uses. Computationally intense statistical techniques will be discussed and programmed. At least one proprietary and one open-source statistical computing environment will be learned. Students will learn how to combine the functionality of different statistical packages to create and present a data analysis optimally. Prior experience with computer programming highly recommended.
-
1.00 - 3.00 Credits
A unique and specifically focused course within the general purview of a department which intends to offer it on a "one time only" basis and not as a permanent part of the department's curriculum.
-
1.00 - 6.00 Credits
A workshop is a program which is usually of short duration, narrow in scope, often non-traditional in content and format, and on a timely topic.
-
1.00 - 3.00 Credits
A Selected Topics course is a normal, departmental offering which is directly related to the discipline, but because of its specialized nature, may not be able to be offered on a yearly basis by the department.
-
3.00 Credits
There are five basic sources of data that can be used for a statistical study: observational data, experimental data, data from a survey or census, simulation data, and found data. In this course, we learn the strengths and weaknesses associated with each study type, exploring the concepts of randomization, representation, causality, weighting, estimation, and variance. Ethical issues associated with statistical studies will also be discussed. A working knowledge of spreadsheet software such as Microsoft Excel is assumed.
-
3.00 Credits
Statistics is used in computer algorithms (machine learning) to enhance computer discision-making and prediction capabilities. this course will cover a wide variety of topics in statistical learning methods. Major statistical methods used in machine learning such as linear regression, survival analysis and others will be discussed. Additional topics include unsupervised learning and supervised techniques such as principal component analysis, nearest neighbor, random forest, support vector machines and neural networks. simulation methods, such as the EM algorithm, Metropolis-Hasting algorithm and the Markov Chain Monte Carlo method will also be discussed.
-
3.00 Credits
A calculus-based introduction to probability and statistical applications. Discrete and continuous probability and expected value. Confidence intervals and hypothesis testing for single populations. This course is not open to students who have credit for MATH 352. This course does not count as an upper division elective mathematics course for mathematics majors.
-
3.00 Credits
An introduction to the mathematical foundations of probability theoryincluding discrete and continuous probability distributions, randomvariables, mathematical expectation, momentuo, and moment generatingfunctions.
-
3.00 Credits
Functions of random variables, sampling distributions, introduction tomathematical theory of statistical inference, including methods ofmoments, estimators, maximum likelihood estimators, sufficientstatistics, interval estimates, and hypothesis testing.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|