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36 350: Statistical Computing
9.00 Credits
Carnegie Mellon University
Statistical Computing: An introduction to computing targeted at statistics majors with minimal programming knowledge. The main topics are core ideas of programming (functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction), illustrated through key statistical topics (exploratory data analysis, basic optimization, linear models, graphics, and simulation). The class will be taught in the R language. No previous programming experience required. Pre-requisites: (36-202 or 36-208), plus ("computing at Carnegie Mellon" or consent of instructor).
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36 350 - Statistical Computing
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36 401: Modern Regression
9.00 Credits
Carnegie Mellon University
This course is an introduction to the real world of statistics and data analysis. We will explore real data sets, examine various models for the data, assess the validity of their assumptions, and determine which conclusions we can make (if any). Data analysis is a bit of an art; there may be several valid approaches. We will strongly emphasize the importance of critical thinking about the data and the question of interest. Our overall goal is to use a basic set of modeling tools to explore and analyze data and to present the results in a scientific report. A minimum grade of C in any one of the pre-requisites is required. A grade of C is required to move on to 36-402 or any 36-46x course.
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36 401 - Modern Regression
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36 402: Advanced Data Analysis
9.00 Credits
Carnegie Mellon University
The material in this course concentrates on methods for the analysis of data, building on the theory and application of the linear model from 36-401. Topics include repeated measures ANOVA, linear mixed models, analysis of categorical data, power calculation, and if time permits, other topics such as non-linear regression, classification, survival analysis and multivariate methods. Real-world examples will be drawn from a variety of fields. Students will analyze data and write reports.
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36 402 - Advanced Data Analysis
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36 410: Introduction to Probability Modeling
9.00 Credits
Carnegie Mellon University
An introductory-level course in stochastic processes. Topics typically include Poisson processes, Markov chains, birth and death processes, random walks, recurrent events, and renewal theory. Examples are drawn from reliability theory, queuing theory, inventory theory, and various applications in the social and physical sciences.
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36 410 - Introduction to Probability Modeling
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36 461: Topics in Statistics: Statistical Methods in Epidemiology
9.00 Credits
Carnegie Mellon University
Epidemiology is concerned with understanding factors that cause, prevent, and reduce diseases by studying associations between disease outcomes and their suspected determinants in human populations. Epidemiologic research requires an understanding of statistical methods and design. Epidemiologic data is typically discrete, i.e., data that arise whenever counts are made instead of measurements. In this course, methods for the analysis of categorical data are discussed with the purpose of learning how to apply them to data. The central statistical themes are building models, assessing fit and interpreting results. There is a special emphasis on generating and evaluating evidence from observational studies. Case studies and examples will be primarily from the public health sciences.
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36 461 - Topics in Statistics: Statistical Methods in Epidemiology
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36 464: Topics in Statistics: Applied Multivariate Methods
9.00 Credits
Carnegie Mellon University
This course is an introduction to applied multivariate methods. Topics include a discussion of the multivariate normal distribution, the multivariate linear model, repeated measures designs and analysis, principle component and factor analysis. Emphasis is on the application and interpretation of these methods in practice. Students will use at least one statistical package.
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36 464 - Topics in Statistics: Applied Multivariate Methods
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36 490: Undergraduate Research
9.00 Credits
Carnegie Mellon University
This course is designed to give undergraduate students experience using statistics in real research problems. Small groups of students will be matched with clients and do supervised research for a semester. Students will gain skills in approaching a research problem, critical thinking, statistical analysis, scientific writing, and conveying and defending their results to an audience. Eligible students will receive information about the application processes for this course early in the fall.
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36 490 - Undergraduate Research
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36 495: Independent Study
0.00 - 36.00 Credits
Carnegie Mellon University
No course description available.
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36 495 - Independent Study
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36 497: Practicum in Statistics
1.00 - 36.00 Credits
Carnegie Mellon University
No course description available.
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36 497 - Practicum in Statistics
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36 595: Advanced Undergraduate Research
0.00 - 36.00 Credits
Carnegie Mellon University
No course description available.
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36 595 - Advanced Undergraduate Research
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