Applied Statistics Courses Previously Offered at UCLA
Below is a list of applied statistics courses that have previously been
offered at UCLA. Not all classes are offered every year, so you can look
through this list to see when a class might be offered again.
Fall 2006
- Biostatistics
- Biostat 100A: Introduction to Biostatistics, Professor
Gjertson.
Introduction to methods and concepts of statistical analysis.
Sampling situations, with special attention to those occurring in biological
sciences. Topics include distributions, tests of hypotheses, estimation,
types of error, significance and confidence levels, sample size.
- Biostat 110A: Basic Biostatistics, Professor Crespi.
Basic concepts of statistical analysis applied to biological sciences. Topics include
random variables, sampling distributions, parameter estimates, statistical
inference.
- Biostat 200A: Biostatistics, Professor Boscardin.
Topics in
methodology of applied statistics, such as design, analysis of variance,
regression.
- Biostat M208: Introduction to Demographic Methods (Same as Community
Health Sciences M208, Economics M208, and Sociology M213A), Professor
Frankenberg .
Introduction to methods of demographic analysis. Topics
include demographic rates, standardization, decomposition of differences,
life tables, survival analysis, cohort analysis, birth interval analysis,
models of population growth, stable populations, population projection, and
demographic data sources.
- Biostat M215: Survival Analysis (Same as Biomathematics M281), Professor
Li.
Statistical methods for analysis of survival data.
- Biostat M235: Statistical Analysis of Incomplete Data (Same as
Biomathematics M232), Professor Belin.
Discussion of statistical analysis
of incomplete data sets, with material from sample survey, econometric,
biometric, psychometric, and general statistical literature. Topics include
treatment of missing data in statistical packages, missing data in ANOVA and
regression imputation, weighting, likelihood-based methods, and nonrandom
nonresponse models. Emphasis on application of methods to applied problems,
as well as on underlying theory.
- Biostat 403A: Computer Management of Health Data, Professor
Sayre.
Concepts of health data management, design and maintenance of
large databases on various media as well as across networks; computer
programming tools and techniques facilitating data entry, transmission, data
retrieval for statistical analyses, tabulation and report generation useful
to biostatisticians, health planners, and other health professionals.
- Community Health Science
- Com Hlth M208: Introduction to Demographic Methods (Same as
Biostatistics M208, Economics M208, and Sociology M213A), Professor
Frankenberg.
Introduction to methods of demographic analysis. Topics
include demographic rates, standardization, decomposition of differences,
life tables, survival analysis, cohort analysis, birth interval analysis,
models of population growth, stable populations, population projection, and
demographic data sources.
- Earth and Space Sciences
- E&S Sci M204: Time-Series Analysis (Same as Statistics M221),
Professor Paik Schoenberg.
Exploration of methods for analyzing numerical
time-series data. Basic topics in temporal and frequency analysis, followed
by more recent topics. Examples in various fields including economics,
signal processing, and atmospheric sciences.
- Economics
- Econ 41: Statistics for Economists, Professor Kyriazdou.
Introduction
to probability and statistics for economists, with emphasis on rigorous
arguments.
- Econ 203A: Probability and Statistics for Econometrics, Professor
Giacomini.
Provides statistical tools necessary to understand econometric
techniques. Random variables, distribution and density functions, sampling,
estimators, estimation techniques, hypothesis testing, and statistical
inference. Use of economic problems and examples.
- Econ M208: Introduction to Demographic Methods (Same as Biostatistics
M208, Community Health Sciences M208, and Sociology M213A), Professor
Frankenberg.
Introduction to methods of demographic analysis. Topics
include demographic rates, standardization, decomposition of differences,
life tables, survival analysis, cohort analysis, birth interval analysis,
models of population growth, stable populations, population projection, and
demographic data sources.
- Econ M231A: Econometrics: Single Equation Models, Professor
Kyriazidou.
Linear regression model, specification error, functional
form, autocorrelation, nonlinear estimation, distributed lags, nonnormality,
univariate time series, qualitative dependent variables, aggregation,
structural change, and errors-in-variables.
- Education
- Educ 230A: Introduction to Research Design and Statistics; Professor
Webb, Professor Schlackman and Professor Boscardin.
Key concepts and
issues in design and conduct of social sciences research. Introduction to
descriptive statistics and fundamentals of statistical inference.
- Educ 231A: Multivariate Analysis, The Staff.
Review of multiple
regression analysis, analysis of covariance. Introduction to matrix algebra.
Introduction to multivariate normal distribution. Multivariate analysis of
variance. Linear discriminant function. Analysis of repeated measurements.
Canonical correlation. Principal components.
- Educ 233A: Professional Writing in Education, Professor Rose.
Designed for first-and second-year doctoral
students and intended to assist in professional development as writers, with
focus on style and organization, scholarly genres, modes of discourse, and
broader issues of conceptualization and method.
- Geography
- Geog 168: Introduction to Geographic Information Systems, Professor
Shin.
Limited to juniors/seniors. Introduction to basic geographic
information systems (GIS) concepts and spatial analysis. Data structures,
topology, and attribute information. Laboratory exercises use database
query, manipulation, and spatial analysis to address "real world" problems.
- Human Complex Systems
- Hum CS M100: Modeling and Simulation (same as Anthropology M186 and Honors Collegium M150), Professor Nardi.
Exploration of different approaches to modeling
empirical phenomena of concern to social sciences. Topics include utility
models, learning models, decision models, group competition models, and
evolutionary models. Use of multiagent computer simulations and group
exercises to explore emergent behaviors among individuals interacting
according to models for behavior. Discussion of advantages and drawbacks of
more traditional mathematical modeling. Review of alternative forms of
formal representations of hypothesized processes and issues related to
verification of simulations.
- Management
- Mgmt 213B: Statistical Methods in Management, Professor
Stern.
Introduction to parameter and interval estimation, simple and
multiple linear regression and correlation, fixed, random, and mixed effects
analysis of variance models and nonparametric statistics, all as they apply
to management studies.
- Mgmt 213C: Introduction to Multivariate Analysis, Professor
Morrison.
Introduction to use of multivariate models in management
research to organize and represent information; interpretation of
coefficients from multivariate exploratory models (e.g., principal axes and
factor analysis models); survey of multivariate statistical procedures
(e.g., multiple discriminate analysis, multivariate analysis of variance,
canonical correlation, and confirmatory factor models).
- Mgmt 402: Data and Decisions, The Staff.
Topics include
probabilities, random variables (expectation, variance, covariance, normal
random variables), decision trees, estimation, hypothesis testing, and
multiple regression models. Emphasis on actual business problems and data.
- Political Science
- Pol Sci 200A: Statistical Methods I, Professor Denardo.
Introduction
to statistical analysis of political data. Methods of data analysis,
estimation, and inference.
- Psychology
- Psych 100A: Psychological Statistics, The Staff.
Basic statistical
procedures and their application to research and practice in various areas
of psychology.
- Psych 250A: Advanced Psychological Statistics, Professor Reise.
Basic
statistical techniques as applied to design and interpretation of
experimental and observational research.
- Public Policy
- Pub Plc 203: Statistical Methods of Policy Analysis I, Professor
Phillips.
Review of statistical principles useful to policy research and
analysis. Topics include descriptive statistics, expectations, univariate
distribution, probability, covariance and correlations, statistical
independence, random sampling, estimators, unbiasedness and efficiency,
statistical inference, confidence intervals, and hypothesis testing.
- Pub Plc M224A: Introduction to Geographic Information Systems (Same as
Urban Planning M206A), Professor Estrada.
Principles of Geographic
Information Systems (GIS) and applied techniques of using spatial data for
mapping and analysis. Topics include data quality, data manipulation,
spatial analysis, and information systems. Use of mapping and spatial
analysis to address a planning problem.
- Sociology
- Sociol 210A: Intermediate Statistical Methods I, Professor
Bonacich.
Intermediate statistical methods using computers: probability
theory, sampling distributions, hypothesis testing, interval estimation,
multiple regression and correlation, experimental design, analysis of
variance and covariance, contingency tables, sampling theory.
- Sociol 212A: Survey Data Analysis, Professor Treiman.
Analysis and
interpretation of primarily nonexperimental quantitative data, with focus on
sample survey and census data. Extensive practice at utilizing statistical
methods encountered in previous courses, culminating in term paper in style
of American Sociological Review or similar journal article. Topics include
simple tabular analysis, log-linear analysis, ordinary least squares
regression, robust regression, binomial and multinomial logistic regression,
and scale construction. Logic of analysis and problems of statistical
inference, including diagnostic procedures and methods for handling complex
sample survey designs.
- Sociol M213A: Introduction to Demographic Methods (Same as Biostatistics
M208, Community Health Sciences M208, and Economics M208), Professor
Frankenberg.
Topics include demographic rates, standardization,
decomposition of differences, life tables, survival analysis, cohort
analysis, birth interval analysis, models of population growth, stable
populations, population projection, and demographic data sources.
- Statistics
- Stats 110A: Applied Statistics, Professor Wu.
Probability,
distributions, expectation, estimation, central limit theorem, confidence
intervals, testing.
- Stats 110B: Applied Statistics, Professor Esfandiari.
One- and
two-sample problems, goodness of fit and contingency tables, correlation and
regression, analysis of variance, nonparametrics.
- Stats 130A: Statistical Analysis with Stata, Professor Lew.
How to
manage and analyze quantitative data using Stata statistical software.
Graphical analysis and programming and extensions to basic package.
- Stats 153: Statistical Analysis with Missing Data, Professor
Yuille.
Study of methods dealing with nonresponse and missing data,
including introduction to terminology, limitations of simple methods, and
modern methods for dealing with missing data, such as EM algorithm and
multiple imputation.
- Stats 201A: Research Design, Sampling, and Data Management, Professor
Xu.
Conditioning, Markov chains, Poisson process, Brownian motion,
stationary processes, applications.
- Stats 204: Nonparametric Function Estimation and Modeling, Professor
Hansen.
Introduction to many useful nonparametric techniques such as
nonparametric density estimation, nonparametric regression, and
high-dimensional statistical modeling. Some semiparametric techniques and
functional data analysis.
- Stats M221: Time-Series Analysis (Same as Earth and Space Sciences
M204), Professor Paik Schoenberg.
Exploration of methods for analyzing
numerical time-series data. Basic topics in temporal and frequency analysis,
followed by more recent topics. Examples in various fields including
economics, signal processing, and atmospheric sciences.
- Urban Planning
- Urbn Pl M206A: Introduction to Geographic Information Systems (Same as
Public Policy M224A), Professor Estrada.
Principles of Geographic
Information Systems (GIS) and applied techniques of using spatial data for
mapping and analysis. Topics include data quality, data manipulation,
spatial analysis, and information systems. Use of mapping and spatial
analysis to address a planning problem.
Spring 2006
- Biostatistics
- Biostat 100A: Introduction to Biostatistics, Professor Lee.
Introduction to methods and concepts of statistical analysis. Sampling situations,
with special attention to those occurring in biological sciences. Topics include distributions,
tests of hypotheses, estimation, types of error, significance and confidence levels, sample size.
- Biostat 200C: Biostatistics, Professor Wong.
Measures of association and analysis of categorical data, theory of generalized linear models.
- Biostat 233C: Statistical Methods in AIDS, Professor Cumberland.
Coverage of methods necessary to address statistical problems in AIDS research,
including projection methods for the size of AIDS epidemic and methods for estimating incubation distribution.
- Biostat M235: Causal Inference, Professor Belin (Same as Psychiatry
M232).
Selection bias, confounding, ecological paradox, contributions of Fisher and Neyman.
Rubin model for causal inference, propensity scores. Analysis of clinical trials with
noncompliance. Addressing confounding in longitudinal studies. Path analysis, structural
equation, and graphical models. Decision making when causality is disputed.
- Biostat M237: Applied Genetic Modeling, Professor Sinsheimer (Biomathematics M207B and Human Genetics M207B).
Introduction to practical issues in management and analysis of health data using SAS programming language.
Cross-sectional and longitudinal population-based data sets to be used throughout to illustrate principles of
data management and analysis for addressing biomedical and health-related hypotheses.
- Biostat 406: Applied Multivariate Biostatistics, Professor Afifi.
Use of multiple regression, principal components, factor analysis,
discriminant function analysis, logistic regression, and canonical correlation
in biomedical data analysis.
- Biostat 413: Introduction to Pharmaceutical Statistics, Professor Lee.
Exploration of various types of statistical techniques used in pharmaceutical and related
industries. Topics include bioassay and other assay techniques (e.g., ELISAs and FACs analysis),
quality control techniques, and pharmacokinetic and pharmacodynamic modeling.
- Economics
- Econ 203C: Introduction to Econometrics: Systems Models , Professor
Buchinsky.
Multivariate regression, simultaneous equation estimation, identification, and latent variables.
- Education
- Educ 230A: CoIntroduction to Research Design and Statistics, The Staff.
Key concepts and issues in design and conduct of social sciences research. Introduction
to descriptive statistics and fundamentals of statistical inference.
- Educ 230C: Linear Statistical Models in Social Science Research: Analysis of Designed Experiments, Professor Novak and Professor Ender.
Solid and comprehensive training in experimental design and analysis methods, especially use of analysis of variance methods.
- Educ 231D: Advanced Quantitative Models in Nonexperimental Research: Multilevel Analysis, Professor Seltzer.
Examination of conceptual, substantive, and methodological issues in analyzing multilevel data (i.e., on individuals in organizational settings
such as schools, corporations, hospitals, communities); consideration of alternative analytical models.
- Management
- Mgmt 212B: Decision Sciences Models II, Professor Herman.
Broad survey of nonlinear, time-staged, and probabilistic models for managerial decision making.
Application areas include finance, marketing, facilities design, production, and energy systems.
- Mgmt 269C: Quantitative Research in Marketing, Professor Bucklin.
Students are assumed to have good background in marketing principles and to be familiar with probability,
statistics, mathematical programming, and econometrics. Review of a range of quantitative models as
applied in marketing research.
- Political Science
- Pol Sci 200C: Statistical Methods III, Professor Denardo.
Statistical modeling of political processes. Topics include simultaneous equations models,
discrete choice models, time-series models.
- Pol Sci 200E: Advanced Regression Analysis, Professor Honaker.
- Pol Sci 209: Applications and Tests of Formal Models of Politics, Professor Groseclose.
Intersection of formal theory and statistical methods. By working through exemplar articles, students
gain experience in drawing testable empirical implications from formal models and how to design
statistical estimators that capture structural parameters of formal models.
- Psychiatry and Biobehavioral Sciences
- Psyctry M232: Causal Inference, Professor Belin (Same as Biostatistics M235).
Selection bias, confounding, ecological paradox, contributions of Fisher and Neyman.
Rubin model for causal inference, propensity scores. Analysis of clinical trials with
noncompliance. Addressing confounding in longitudinal studies. Path analysis, structural
equation, and graphical models. Decision making when causality is disputed.
- Psychology
- Psych 100A: Psychological Statistics, The Staff.
Basic statistical procedures and their application to research and practice in various areas of psychology.
- Psych 250B: Advanced Psychological Statis, Professor Nandy.
Advanced experimental design and planning of investigations.
- Public Policy
- Pub Plc M224B: Advanced Geographic Information Systems, Professor
Kwano (Same as Urban Planning M206B).
Principles and skills of geographic analysis and modeling; managing, processing, and
interpreting spatial data. Especially useful for students interested in environmental,
demographic, suitability, and transportation-related research. Scripts (Avenue),
modeling (Spatial Analyst), network analysis, and transportation modeling (TransCAD).
- Sociology
- Sociol 210C: Intermediate Statistical Methods III, Professor Mason.
Survey of advanced statistical methods used in social research, with focus on problems
for which classical linear regression model is inappropriate, including categorical
data, structural equations, longitudinal data, incomplete and erroneous data, and complex samples.
- Sociol 212B: Survey Data Analysis, Professor Treiman.
Analysis and interpretation of primarily nonexperimental quantitative data,
with focus on sample survey and census data. Extensive practice at utilizing statistical
methods encountered in previous courses, culminating in term paper in style of
"American Sociological Review" or similar journal article. Topics include simple
tabular analysis, log-linear analysis, ordinary least squares regression, robust
regression, binomial and multinomial logistic regression, and scale construction.
Logic of analysis and problems of statistical inference, including diagnostic
procedures and methods for handling complex sample survey designs.
- Statistics
- Stats 110A: Applied Statistics, Professor Li.
Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
- Stats 110B: Applied Statistics, Professor Yuan.
One- and two-sample problems, goodness of fit and contingency tables, correlation and regression,
analysis of variance, nonparametrics.
- Stats 120B: Introduction to Applied Regression Analysis, Professor
Gould.
Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and
generalized linear model (e.g., logistic regression). Special attention to modern extensions of
regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence.
- Stats 135: Introduction to Computational Statistics with R, Professor
De Leeuw.
- Stats M154: Measurement and Its Applications, Professor Bentler (Same as Psychology M144).
Selected theories for quantification of psychological, educational, social, and behavioral science data.
Classical test, factor analysis, generalizability, item response, optimal scaling, ordinal measurement,
computer-adaptive, and related theories. Construction of tests and measures and their reliability, validity, and bias.
- Stats 201B: Regression Analysis: Model Building, Fitting, and Criticism, Professor Berk.
Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear
model (e.g., logistic regression). Special attention to modern extensions of regression, including regression
diagnostics, graphical procedures, and bootstrapping for statistical inference.
- Stats C225: Experimental Design, Professor Xu (Concurrently scheduled with course C151).
Basic principles, analysis of variance, randomized block designs, Latin squares, balanced incomplete
block designs, factorial designs, fractional factorial designs, minimum aberration designs, robust parameter designs.
- Stats C236: Introduction to Bayesian Statistics, Professor Sabatti (Concurrently scheduled with course C180).
Introduction to statistical inference based on use of Bayes theorem, covering foundational aspects, current applications,
and computational issues. Topics include Stein paradox, nonparametric Bayes, and statistical learning. Examples of
applications include protein alignment algorithms and image denoising procedures.
- Stats M242: Multivariate Analysis with Latent Variables, Professor Bentler (Same as Political Science M208D and Psychology M257).
Introduction to models and methods for analysis of data hypothesized to be generated by unmeasured latent variables, including
latent variable analogues of traditional methods in multivariate analysis. Causal modeling: theory testing via analysis of moment
structures. Measurement models such as confirmatory, higher-order, and structured-means factory analytic models. Structural
equation models, including path and simultaneous equation models. Parameter estimation, hypothesis testing, and other statistical
issues. Computer implementation. Applications.
Winter 2005
- Biostatistics
- Biostat 201: Topics in Applied Regression, Professor Gornbein.
Further studies in multiple linear
regression, including applied multiple regression models, regression
diagnostics and model assessment, factorial and repeated measure
analysis of variance models, nonlinear regression, logistic regression,
propensity scores, matching versus stratification, Poisson regression,
and classification trees. Applications to biomedical and public health
scientific problems.
- Biostat M236: Analysis of Repeated Measures Designs, Professor Weiss (Same
as Biomath M282).
Presentation of classical and
modern theories for analysis of repeated measures designs, with focus on
computation and robustness.
- Biostat M403B: Computer Management and Analysis of Health Data Using SAS,
Professor Yu (Same as Epidemiology M403).
Introduction to practical issues
in management and analysis of health data using SAS programming language.
Cross-sectional and longitudinal population-based data sets to be used
throughout to illustrate principles of data management and analysis for
addressing biomedical and health-related hypotheses.
- Biostat 411: Analysis of Correlated Data, Professor Afifi.
Statistical techniques designed
for analysis of correlated data, including cluster samples, multilevel
models, and longitudinal studies. Computations done on SAS and Stata. Mixed
models and generalized estimation equations (GEE). Emphasis on application,
not theory.
- Economics
- Econ 203B: Introduction to Econometrics: Single Equation Models,
Professor Kyriazidou.
Estimation of basic linear
regression model, testing hypotheses, generalized least squares, serial
correlation, heteroskedasticity, multicollinearity, error-in-variables,
distributed lags, qualitative dependent variables, and forecasting.
- Econ 232B: System Models, Professor Kyriazidou.
Multivariate regression,
errors-in-variables, simultaneous equations, identification, proxy
variables, latent variables, factor analysis of panel data, asymptotic
distribution theory.
- Education
- Educ 230B: Linear Statistical Models in Social Science Research:
Multiple Regression Analysis, Professor Webb.
Solid and comprehensive training in regression-based methods for analyzing quantitative social science data.
- Educ 231B: Factor Analysis, Professor Thum.
Exploratory factor analysis,
rotations, confirmatory factor analysis, multiple-group analysis.
- Educ 231D: Advanced Quantitative Models in Nonexperimental Research:
Multilevel Analysis, Professor Thum.
Examination of conceptual,
substantive, and methodological issues in analyzing multilevel data (i.e.,
on individuals in organizational settings such as schools, corporations,
hospitals, communities); consideration of alternative analytical models.
- Political Science
- Pol Sci 200B: Statistical Methods II, Professor Lewis.
Applications of multiple
regression in political science.
- Psychology
- Psych M238: Survey Research Techniques in Psychocultural Studies, Professor Tucker
(same as Psychiatry M238).
Techniques for conceptualizing, conducting, and analyzing survey data; instruction in qualitative strategies
for enhancing survey research on psychocultural problems.
- Psych M257: Multivariate Analysis with Latent Variables, Professor
Bentler (same as Political Science M208D and Statistics M242). Introduction to models and
methods for analysis of data hypothesized to be generated by unmeasured
latent variables, including latent variable analogues of traditional methods
in multivariate analysis. Causal modeling: theory testing via analysis of
moment structures. Measurement models such as confirmatory, higher-order,
and structured-means factory analytic models. Structural equation models,
including path and simultaneous equation models. Parameter estimation,
hypothesis testing, and other statistical issues. Computer implementation.
Applications.
- Public Policy
- Pub Plc 208: Statistical Methods of Policy Analysis II, Professor Kane.
Quantitative studies of public policy, covering regression analysis and its application to public policy
questions.
- Sociology
- Sociol 210B: Intermediate Statistical Methods, Professor Bonacich.
Intermediate statistical methods using computers: probability theory, sampling distributions, hypothesis
testing, interval estimation, multiple regression and correlation,
experimental design, analysis of variance and covariance, contingency
tables, sampling theory.
- Sociol 212B: Survey Data Analysis, Professor Treiman.
Analysis and interpretation of primarily nonexperimental quantitative data, focusing on sample survey and
census data. Extensive practice at utilizing statistical methods encountered
in previous courses, culminating in term paper in style of "American
Sociological Review" or similar journal article. Topics include simple
tabular analysis, log-linear analysis, ordinary least squares regression,
robust regression, binomial and multinomial logistic regression, and scale
construction. Logic of analysis and problems of statistical inference,
including diagnostic procedures and methods for handling complex sample
survey designs.
- Sociology 285C, Applied Multilevel Analysis, Professor Mason
This course will cover hierarchical stochastic parameter models, fixed
effect models (in the econometric sense), marginal models, and design
issues relevant to technique selection, as well as cross-classified
models. The course is definitely "applied," and the goal is to get all
students able to estimate models using a range of software, including as
many as possible of Stata, Stata-Gllamm, HLM, MLwiN, aML, Mplus, and
R/S+. Students will not be expected to use all packages; that's too
much to ask. The web site will be updated soon and can be found at
http://www.sscnet.ucla.edu/05W/soc285c-1/
- Statistics
- Stats 110A: Applied Statistics, Professor Sanchez and Professor Beddo.
Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing.
- Stats 110B: Applied Statistics, Professor Beddo.
One- and two-sample problems, goodness of fit and contingency tables, correlation and regression, analysis
of variance, nonparametrics.
- Stats 120A: Introduction of Applied Regression Analysis, Professor
Gould.
Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and
generalized linear model (e.g., logistic regression). Special attention to
modern extensions of regression, including regression diagnostics, graphical
procedures, and bootstrapping for statistical influence.
- Stats 201B: Regression Analysis: Model Building, Fitting and Criticism,
Professor Berk.
Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and
generalized linear model (e.g., logistic regression). Special attention to
modern extensions of regression, including regression diagnostics, graphical
procedures, and bootstrapping for statistical influence.
- Stats C226: Resampling Methods.
Simple intuitive introduction to practical application of statistics for experiments and surveys in business
and biological, medical, physical, and social sciences. Resampling methods
-- bootstrap and permutation test -- are table-free and distribution-free,
require common sense (not calculus), yet have a broader range of
applications than classical parametric statistical procedures. Concurrently
scheduled with course C152.
Fall 2004
- Biostatistics
- Biostat M208: Introduction to Demographic Methods, Professor
Frankenberg (Same as Community Health Science M208 and Sociology M213A).
Introduction to methods of demographic analysis. Topics include demographic
rates, standardization, decomposition of differences, life tables, survival
analysis, cohort analysis, birth interval analysis, models of population
growth, stable populations, population projection, and demographic data
sources. - Biostat M234: Applied Bayesian Inferences, Professor Weiss (Same
as Biomathematics M234).
Bayesian approach to statistical inferences, with emphasis on biomedical applications and concepts rather than
mathematical theory. Topics include large sample Bayes inference from
likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian
approaches to linear and nonlinear regression, model selection, Bayesian
hypothesis testing, and numerical methods
- Education
- Education 231A: Multivariate Analysis, Professor Thum
Review of
multiple regression analysis, analysis of covariance. Introduction to matrix
algebra. Introduction to multivariate normal distribution. Multivariate
analysis of variance. Linear discriminant function. Analysis of repeated
measurements. Canonical correlation. Principal components. - Education 255A: Measurement (No other information given on this
course.)
- Policy Studies
- Poly St 203: Statistical Methods of Policy Analysis I, Professor
Kane
Review of statistical
principles useful to policy research and analysis. Topics include
descriptive statistics, expectations, univariate distribution, probability,
covariance and correlations, statistical independence, random sampling,
estimators, unbiasedness and efficiency, statistical inference, confidence
intervals, and hypothesis testing.
- Poly St M218:
Research Design and Methods for Social Policy, The Staff
How to
become more sophisticated consumers and producers of qualitative and
quantitative policy research. In first half of course, formal principles of
research design; in second half, various data collection methods, including
ethnography, interviewing, and survey design.
- Political Science
- Pol Sci 200A: Statistical Methods I, Professor Denardo
Introduction to statistical
analysis of political data. Methods of data analysis, estimation, and
inference.
- Pol Sci 209: Workshop in Basic Statistical Methods, Professor Zaller
Basic statistical methods,
including interaction terms in regression; missing data; variable
construction and measurement error; logit, probit, ordered logit, and
multinomial logit; instrumental variables; simple time series.
Psychology
- Psych 258: Multilevel Modeling for Daily Experience and other Nested
Designs, Professor Gable
Multilevel modeling
statistical techniques, also known as mixed models and random coefficient
models. These methods are useful for analyzing data with nested designs,
such as students nested within classrooms, neighborhoods nested within
counties, or daily experience data in which multiple measurement occasions
are nested within persons.
Sociology
- Sociol 210A: Intermediate Statistical Methods I, Professor McFarland
Intermediate statistical methods
using computers: probability theory, sampling distributions, hypothesis
testing, interval estimation, multiple regression and correlation,
experimental design, analysis of variance and covariance, contingency
tables, sampling theory.
- Sociol 212A: Survey Data Analysis. Professor Treiman
Analysis and interpretation
of primarily nonexperimental quantitative data, focusing on sample survey
and census data. Topics include
simple tabular analysis, log-linear analysis, ordinary least squares
regression, robust regression, binomial and multinomial logistic regression,
and scale construction. Logic of analysis and problems of statistical
inference, including diagnostic procedures and methods for handling complex
sample survey designs.
Statistics
- Stat 34: Applied Sampling, Professor Beddo
This class gives you
guidance on how to tell when a sample from a population is valid for
understanding characteristics of the entire population, and how to design
and analyze many different forms of sample surveys. This course will provide
you with practice in solving statistical sampling problems, with the theory
and practice of sampling from finite populations; simple random, stratified,
and cluster sampling; basic properties of various estimators including ratio
and regression estimators; error estimation for complex samples;
nonresponse; reporting results, working with "clients". Exercises in this
class will be solved with the statistical software packages STATA and R, and
the computer program SURVEY. Various other statistical software packages
designed to handle survey data will be introduced.
- Stat 110A: Applied Statistics, Professors Dinov and Paik Schoenberg
Probability, distributions, expectation, estimation, central limit theorem,
confidence intervals, testing. - Stat 110B: Applied Statistics, Professor
Esfandiari
One- and
two-sample problems, goodness of fit and contingency tables, correlation and
regression, analysis of variance, nonparametrics.
- Stat 170: Intro to Time Series, Professor Sanchez
In this course we
study common and specific methods for analyzing time series data that arise
in atmospheric sciences, economics, finance, astronomy, oceanography, signal
processing, biology, hydrology, computer science and many other areas of
study. The course has an emphasis on applications and on acquiring hands on
experience in analyzing time series. We will use SAS version 9.
For more information see
http://www.stat.ucla.edu/~jsanchez/teaching/course170.html
.
Spring 2003
- Biostatistics
- Biostat M210 Statistical Methods for Categorical Data, Professor Hirji
(Same as Biomathematics M231.) Statistical techniques for analysis of
categorical data; discussion and illustration of their applications and
limitations.
- Biostat 236: Modeling Continuous Longitudinal Data,
Professor Weiss.
Longitudinal data occur when the same measurement is made repeatedly on
experimental units over time, inducing correlation in the measurements
within an experimental unit. As compared with cross sectional data analysis,
modeling of longitudinal data presents additional difficulties in that we
must specify the time trend of the population mean and the correlation
structure of the observations within a person, and how covariates affect
both of these. See more information at
http://rem.ph.ucla.edu/~rob/rm/index.html
- Biostat 406: Applied Multivariate Biostatistics, Professor Afifi
This course will cover the use of multiple regression, principal components,
factor analysis, discriminant function analysis, logistic regression, and
canonical correlation in biomedical data analysis. See
http://www.ph.ucla.edu/class/biostat/406/spring04/ for more details.
- Biostat 413 Introduction to Pharmaceutical Statistics, Professor Lee
Exploration of various types of statistical techniques used in the
pharmaceutical and related industries. Topics will include bioassay and
other assay techniques (e.g. ELISAs and FACs analysis), quality control
techniques, and pharmacokinetic and pharmacodynamic modeling.
- Education
- Education 230C: Linear Statistical Models, Professor Ender &
Professor Abedi
This is the second course of the Ed 230B/C two course sequence. The purpose
of these courses is to provide solid and comprehensive training in
quantitative methods. It is designed to prepare students to carry out and
interpret research using a variety of quantitative and statistical methods.
It will cover key aspects of research design and statistical inference
involving linear statistical models. The two quarters provide an integrated
and unified approach to the application of linear statistical models in
regression, analysis of variance, and experimental and quasi-experimental
designs. This integrated approach will give students an understanding of how
the analytic approaches are closely connected and will help them develop
flexibility in applying quantitative methods correctly to a wide range of
research problems. This sequence will also provide a strong foundation for
further training in advanced statistical methods. For more information see
http://www.gseis.ucla.edu/courses/ed230bc1/
- Educ 231C: Applied Categorical & Nonnormal Data Analysis, Professor
Ender
This course will cover analysis with dichotomous, ordinal and multinomial (polytomous)
dependent variables. Topics include contigency table analysis, logistic (logit)
models, probit models, poisson models, negative binomial models, loglinear
models, regression with censored data and regression with selection. See
http://www.gseis.ucla.edu/courses/ed231c/231c.html
for more details.
- Educ 231E: Statistical Analysis with Latent Variables, Professor Muthen
This course will
cover similar material as last year's course, with additional material. A
description of his previous course can be found at
http://www.gseis.ucla.edu/faculty/muthen/ED231e/index.html . The lab
session will illustrate analyses using the newly released version 3 of the
Mplus program. This course will not be taught again until Spring 2006 due to
Prof Muthen's upcoming sabbatical. This course will cover topics such as
Growth Curve Models, Latent Class Analysis, Multilevel Modeling, and show
how these analyses can be performed in an integrated framework.
- Political Science
- Political Science 200E: Advanced Regression Analysis, Professor Honaker
Diagnostics, robust regression, cross validation, resampling, outliers,
missing data, geometry of regression, validity of assumptions, categorical
dependent variables, transformation of variables. Access to Macintosh computer
very helpful. For more information see
http://www.sscnet.ucla.edu/04S/poliscim200e-1/ .
- Psychology
- Psych 255A: Quantitative Aspects of Assessment, Professors Reise and
Sidanius
Introduction to issues concerning empirical measurement of abstract constructs
using both classical and modern empirical techniques. Hands-on approach allows
students to develop practical experience. In addition to discussion of issues
concerning reliability and validity, topics include exposure to analytic
approaches, including item response theory, multiple regression, principal
components analysis, exploratory factor analysis, confirmatory factor
analysis, path analysis, and structural equation modeling.
- Sociology
- 210C: Intermediate Statistical Methods III, Professor Sweeney
Survey of advanced statistical methods used in social research, with focus on
problems for which classical linear regression model is inappropriate,
including categorical data, structural equations, longitudinal data,
incomplete and erroneous data, and complex samples. For more information see
http://www.sscnet.ucla.edu/04S/soc210c-1/ .
- Statistics
- Stat 34: Applied Sampling, Professor Kreuter
This class gives you guidance on how to tell when a sample from a population
is valid for understanding characteristics of the entire population, and how
to design and analyze many different forms of sample surveys. This course
will provide you with practice in solving statistical sampling problems,
with the theory and practice of sampling from finite populations; simple
random, stratified, and cluster sampling; basic properties of various
estimators including ratio and regression estimators; error estimation for
complex samples; nonresponse; reporting results, working with "clients".
Exercises in this class will be solved with the statistical software
packages STATA and R, and the computer program SURVEY. Various other
statistical software packages designed to handle survey data will be
introduced.
- Stat 110A: Applied Statistics, Professor Xu
Probability, distributions, expectation, estimation, central limit theorem,
confidence intervals, testing, for more information see
http://www.stat.ucla.edu/%7Ehqxu/stat110A/
- Stat 170: Intro to Time Series, Professor Sanchez
In this course we study common and specific methods for analyzing time
series data that arise in atmospheric sciences, economics, finance,
astronomy, oceanography, signal processing, biology, hydrology, computer
science and many other areas of study. The course has an emphasis on
applications and on acquiring hands on experience in analyzing time series.
We will use a free software package called R that you can download in your
computer for most of the examples. For more information see
http://www.stat.ucla.edu/%7Ejsanchez/teaching/course170.html
- Statistics 216: High Dimensional Data Analysis, Professor Li
Dimensionality is an issue that can arise in every scientific field.
Generally speaking, the difficulty lies on how to visualize a high
dimensional function or data set. People often ask : "How do they look?",
"What structures are there?", "What model should be used?" Aside from the
differences that underlie the various scientific contexts, such kind of
questions do have a common root in Statistics. This is the driving force for
the study of high dimensional data analysis. This course will discuss
several statistical methodologies useful for exploring voluminous data. They
include Principal Component Analysis, Clustering and Classification,
Tree-structured analysis, Neural Network, Hidden Markov Models, Sliced
inverse regression(SIR) and principal Hessian direction (PHD).
- Statistics 233: Statistical Methods in Biomedical Imaging, Professor
Dinov
See
http://www.stat.ucla.edu/%7Edinov/courses_students.dir/04/Spring/Stat233.dir/STAT233.html
for more details.
Winter 2003
Fall 2002
- Statistics 217A, Professor Berk. See
handout
for more information.
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