STAT-STATISTICS -- 2001-03 Catalog
Statistics Department

STAT 100  Orientation to Statistics (1) (CR/NC)

Intended for new statistics majors. Overview of the statistics profession, career opportunities in statistics, and an introduction to the discipline of statistics and the nature of statistical reasoning. Credit/No Credit grading only. 1 lecture.

STAT 130  Introduction to Statistical Reasoning (4)           GE B1

Survey of statistical ideas and philosophy. Emphasis on concepts rather than in-depth coverage of statistical methods. Topics include sampling, experimentation, data exploration, chance phenomena, and methods of statistical inference. Credit not allowed for students with a previous statistics course. 4 lectures. Prerequisite: Satisfactory completion of ELM requirement.

STAT 200  Special Problems for Undergraduates (1–2)

Individual investigation, research, studies, or surveys of selected problems. Total credit limited to 4 units, with a maximum of 2 units per quarter. Prerequisite: Consent of department head.

STAT 211  Elementary Probability and Statistics (3)

Classification of statistical data. Calculation and uses of various averages, measures of variability, elementary probability. Binomial and normal distributions. Random sampling, confidence intervals. Introduction to hypothesis testing. 3 lectures. Prerequisite: Intermediate algebra, appropriate score on ELM.

STAT 212  Statistical Methods (2)

Tests of hypotheses and confidence intervals on common parameters, linear regression and correlation, analysis of variance, and analysis of enumerative data. 2 lectures. Prerequisite: STAT 211 or equivalent.

STAT 217  Introduction to Statistical Concepts and
Methods (4)                                    GE B1

Sampling and experimentation, descriptive statistics, confidence intervals, two-sample hypothesis tests for means and proportions, Chi-square tests, linear and multiple regression, analysis of variance. Not open to students with credit in STAT 212 or STAT 218 or STAT 221 or STAT 251. 4 lectures. Prerequisite: Intermediate algebra, appropriate score on ELM.

STAT 218  Applied Statistics for the Life Sciences (4)       GE B1

Data collection and experimental design, descriptive statistics, confidence intervals, parametric and non parametric one and two-sample hypothesis tests, analysis of variance, correlation, simple and multiple linear regression, chi-square tests. Applications of statistics to the life sciences. Use of a statistical computer package. Not open to students with credit in STAT 212 or STAT 217 or STAT 221 or STAT 251. 4 lectures. Prerequisite: Intermediate Algebra, appropriate score on ELM.

STAT 221  Introduction to Probability and Statistics (5)     GE B1

Data classification, descriptive statistics, elementary probability. Binomial and normal distributions. Random sampling, confidence intervals and hypothesis testing on common parameters. Introduction to regression and correlation, analysis of variance, contingency table analysis. Not open to students with credit in STAT 217 or STAT 218. 5 lectures. Prerequisite: Intermediate algebra, appropriate score on ELM.

STAT 251  Statistical Inference for Management I (4)       GE B1

Descriptive statistics. Probability and counting rules. Random variables and probability distributions. Sampling distributions. Large sample point and interval estimation of population parameters. Large sample hypothesis tests for population means and proportions. 4 lectures. Prerequisite: Passing score on precalculus MAPE or equivalent.

STAT 252  Statistical Inference for Management II (5)     GE B1

Small sample confidence intervals and hypothesis tests. Introduction to ANOVA, regression, correlation, multiple regression, time series, and forecasting. Statistical quality control. Enumerative data analysis. SPSS used throughout course. 5 lectures. Prerequisite: STAT 251 with a minimum grade of C-, and CSC 120 or one course in computer programming.

STAT 312  Statistical Methods for Engineers (4)    GE B6

Descriptive and graphical methods. Discrete and continuous probability distributions. One and two sample confidence intervals and hypothesis testing. Single factor analysis of variance. Quality control. Introduction to regression and to experimental design. Use of computer to solve problems. 4 lectures. Prerequisite: MATH 142

STAT 313  Applied Experimental Design and Regression Models (4)      GE B1

Analysis of variance and regression analysis for students not majoring in statistics or mathematics. Includes one-way classification, randomized blocks, Latin squares, factorial designs, multiple regression, diagnostics, and model comparison.  4 lectures. Prerequisite: STAT 212 or STAT 217 or STAT 218 or STAT 221.

STAT 321  Probability and Statistics for Engineers and
Scientists (4)                                   GE B6

tabular and graphical methods for data summary, numerical summary measures, probability concepts and properties, discrete and continuous probability distributions, expected values, statistics and their sampling distributions, point estimation, confidence intervals for a mean and proportion. Use of MINITAB computer package. 4 lectures. Prerequisite: MATH 142.

STAT 322  Statistical Analysis for Engineers and
Scientists (4)

Confidence intervals, hypothesis testing, one and two-factor analysis of variance, simple linear regression, nonlinear and multiple regression, chi-square tests, introduction to statistical quality control. 4 lectures. Prerequisite: STAT 321.

STAT 323  Design and Analysis of Experiments I (4)

Principles, construction and analysis of experimental designs. Includes completely randomized, randomized complete block, Latin squares, Graeco Latin squares, factorial, and nested designs. Fixed and random effects, expected mean squares, multiple comparisons, and analysis of covariance. 4 lectures. Prerequisite: STAT 322.

STAT 324  Applied Regression Analysis (4)

Simple linear regression and associated special topics, multiple linear regression, indicator variables, influence diagnostics, assumption analysis, selection of "best subset", nonstandard regression models, logistic regression, nonlinear regression models. 4 lectures. Prerequisite: STAT 252 or STAT 313 or STAT 322.

STAT 330  Statistical Computing I: SAS (4)

Techniques available to the statistician for efficient use of computers to perform statistical computations and to analyze large amounts of data. Use of SAS throughout the course. Includes data preparation, report writing, and basic statistical methods. 4 lectures. Prerequisite: STAT 212 or STAT 252 or STAT 313 or STAT 322.

STAT 400  Special Problems for Advanced Undergraduates (1–2)

Individual investigation, research, studies or surveys of selected problems. Total credit limited to 4 units, with a maximum of 2 units per quarter. Prerequisite: Consent of department head.

STAT 416  Statistical Analysis of Time Series (4)

Time series components, descriptive smoothing methods, regression models for time series data, forecasting via exponential smoothing, evaluation of forecasts, autocorrelation, ARIMA models and Box-Jenkins methods, combining forecasts, frequency domain analysis, filtering. 4 lectures. Prerequisite: STAT 252 or STAT 322.

STAT 418  Analysis of Cross-Classified Data (4)

Discrete multivariate statistics, including analysis of cross-classified data, log-linear models for multidimensional contingency tables, goodness of fit statistics, measures of association, model selection, and hypothesis testing. 4 lectures. Prerequisite: Two courses in statistics and MATH 206.

STAT 419  Applied Multivariate Statistics (4)

Continuous multivariate statistics. Multivariate linear model, principal components and factor analysis, discriminant analysis, clustering, and canonical correlation. Use of Minitab and SAS throughout the course. 4 lectures. Prerequisite: Two courses in statistics and MATH 206, or consent of instructor.

STAT 421  Sampling Techniques (4)

Planning, execution, and analysis of sampling from finite populations. Sampling designs, including simple random, stratified, systematic, cluster and two-stage cluster. Estimation procedures and sample size calculations. Post-stratification techniques. Estimating population size. 4 lectures. Prerequisite: One of the following: STAT 212, STAT 217, STAT 218, STAT 221, STAT 252, STAT 322, or STAT 512.

STAT 423  Design and Analysis of Experiments II (4)

Continuation of STAT 323. 2k factorial designs, 3k factorial designs, balanced and partially balanced incomplete block designs, nested designs, split-plot designs, response surface methodology, confounding, repeated measures, and other design approaches. 4 lectures. Prerequisite: STAT 323.

STAT 425  Probability Theory (4)

Basic probability theory, combinatorial methods, stochastic independence, conditional and marginal probability, probability models for random phenomena, random variables, probability distributions, distributions of functions of random variables, mathematical expectation, covariance and correlation, conditional expectation. 4 lectures. Prerequisite: STAT 321, MATH 241, and MATH 248.

STAT 426  Estimation and Sampling Theory (4)

Properties of statistics obtained from samples. Sample mean properties, convergence in probability, law of large numbers, and central limit theorem. Selected probability distributions. Theory of estimation. Sampling distribution of estimators. Introduction to hypothesis testing. 4 lectures. Prerequisite: STAT 425.

STAT 427  Mathematical Statistics (4)

The theory of hypothesis testing and its applications. Nonparametric methods. Linear statistical models including linear regression, and analysis of variance. The general linear model, full-rank models, constrained models, and tests of linear hypotheses. 4 lectures. Prerequisite: STAT 426.

STAT 430  Statistical Computing II: S-Plus (4)

Design and use of statistical software in programming statistical applications; object oriented statistical languages; random number generation; Monte Carlo methods including resampling (bootstrap and jack-knife), randomization tests, and simulation; exploratory data analysis using linked, Trellis, and dynamic graphics; smoothing algorithms; and regression trees. 4 lectures. Prerequisite: STAT 322, STAT 330, and STAT 323 or STAT 324.

STAT 461, 462  Senior Project (2) (2)

Selection and completion of a project under faculty supervision. Projects typical of problems which graduates must solve in their fields of employment. Project results are presented in a formal report. Minimum 120 hours total time.

STAT 463  Undergraduate Seminar (2) (CR/NC)

Reports and discussions by students through seminar methods, based on topics of interest to persons preparing for a career in statistics. Offered

only on a Credit/No Credit basis. 2 seminars. Prerequisite: Junior standing.

STAT 470  Selected Advanced Topics (1–4)

Directed group study of selected topics for advanced students. Open to undergraduate and graduate students. Class Schedule will list topic selected. Total credit limited to 8 units. 1–4 lectures. Prerequisite: Consent of instructor.

STAT 485  Cooperative Education Experience (6) (CR/NC)

Part-time work experience in business, industry, government, and other areas of student career interest. Positions are paid and usually require relocation and registration in course for two consecutive quarters. Formal report and evaluation by work supervisor required. Total credit limited to 16 units. Credit/No Credit grading only. Prerequisite: Sophomore standing and consent of instructor.

STAT 495  Cooperative Education Experience (12) (CR/NC)

Full-time work experience in business, industry, government, and other areas of student career interest. Positions are paid and usually require relocation and registration in course for two consecutive quarters. Formal report and evaluation by work supervisor required. Total credit limited to 16 units. Credit/No Credit grading only. Prerequisite: Sophomore standing and consent of instructor.

STAT 512  Statistical Methods (4)

Statistical methods in research for graduate students not majoring in mathematical sciences. Probability distributions, confidence intervals, hypothesis testing, contingency tables, linear regression and correlation, multiple regression, analysis of variance. Use of computer packages. 4 seminars. Prerequisite: Graduate standing and intermediate algebra or equivalent.

STAT 513  Applied Experimental Design and Regression Models (4)

Applications of statistics for graduate students not majoring in mathematics. Analysis of variance including the one-way classification, randomized blocks, Latin squares, and factorial designs. Introduction to multiple regression and to analysis of covariance. Use of computer software in the solution of statistical problems. 4 lectures. Not open to students with credit in STAT 313. Prerequisite: One of the following: STAT 512, STAT 212, STAT 217, STAT 218, STAT 221, STAT 252, or equivalent.

STAT 530  Statistical Computing I: SAS (4)

Techniques available to the statistician for efficient use of computers to perform statistical computations and to analyze large amounts of data. Use of the SAS software system. Includes data preparation, report writing, basic statistical methods, and a research project. Not open to students with credit in STAT 330. 4 lectures. Prerequisite: STAT 512 or STAT 513 or STAT 542 or equivalent

STAT 542  Statistical Methods for Engineers (4)

Descriptive and graphical methods. Discrete and continuous probability distributions. One and two sample confidence intervals and hypothesis testing. Single factor analysis of variance. Quality control. Introduction to regression and to experimental design. Use of computer to solve problems. 4 lectures. Prerequisite: MATH 142 and graduate standing.