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Your search for courses for 22/SP and with code: MATHAPPLIED found 4 courses.

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MATH 241.00 Ordinary Differential Equations 6 credits

Closed: Size: 30, Registered: 30, Waitlist: 0

CMC 210

MTWTHF
12:30pm1:40pm12:30pm1:40pm1:10pm2:10pm
Synonym: 61643

Rob C Thompson

An introduction to ordinary differential equations, including techniques for finding solutions, conditions under which solutions exist, and some qualitative analysis.

Prerequisite: Mathematics 232 or instructor permission

MATH 341.00 Partial Differential Equations 6 credits

Closed: Size: 25, Registered: 26, Waitlist: 0

CMC 210

MTWTHF
1:50pm3:00pm1:50pm3:00pm2:20pm3:20pm
Synonym: 61646

Rob C Thompson

An introduction to partial differential equations with emphasis on the heat equation, wave equation, and Laplace's equation. Topics include the method of characteristics, separation of variables, Fourier series, Fourier transforms and existence/uniqueness of solutions.

Prerequisite: Mathematics 241

STAT 250.00 Introduction to Statistical Inference 6 credits

Open: Size: 28, Registered: 21, Waitlist: 0

CMC 206

MTWTHF
12:30pm1:40pm12:30pm1:40pm1:10pm2:20pm
Synonym: 61553

Laura M Chihara

(Formerly Mathematics 275) Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.

Prerequisite: Mathematics 240 Probability (formerly Mathematics 265)

Formerly Mathematics 275

STAT 320.00 Time Series Analysis 6 credits

Closed: Size: 20, Registered: 22, Waitlist: 0

CMC 319

MTWTHF
1:50pm3:00pm1:50pm3:00pm2:20pm3:20pm
Synonym: 61554

Andy N Poppick

(Formerly MATH 315) Models and methods for characterizing dependence in data that are ordered in time. Emphasis on univariate, quantitative data observed over evenly spaced intervals. Topics include perspectives from both the time domain (e.g., autoregressive and moving average models, and their extensions) and the frequency domain (e.g., periodogram smoothing and parametric models for the spectral density).

Prerequisite: Statistics 230 and 250 (formerly Mathematics 245 and 275). Exposure to matrix algebra may be helpful but is not required

Formerly Mathematics 315

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Requirements
You must take 6 credits of each of these.
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You must take 6 credits of each of these,
except Quantitative Reasoning, which requires 3 courses.
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