### Statistics

- Jump to level:
- 200s
- 300s
- 400s
- 500s

100s

**STAT 104 Elementary Statistics 3**

Prereq.: MATH 101 (C- or higher) or placement exam. Intuitive treatment of some fundamental concepts involved in collecting, presenting, and analyzing data. Topics include frequency distributions, graphical presentations, measures of relative position, measures of variability, probability, probability distributions (binomial and normal), sampling theory, regression, and correlation. No credit given to students with credit for STAT 108, 200, 215, 314 or 315. CSUS Common Course. Skill Area II

200s

**STAT 200 Business Statistics 3**

Prereq.: MATH 101 (C- or higher) or placement exam. Application of statistical methods used for a description of analysis of business problems. The development of analytic skills is enhanced by use of one of the widely available statistical packages and a graphing calculator. Topics include frequency distributions, graphical presentations, measures of relative position, measures of central tendency and variability, probability distributions including binomial and normal, confidence intervals, and hypothesis testing. No credit given to students with credit for STAT 104, 108, 215, 314, or 315. Skill Area II

**STAT 201 Business Statistics II 3**

Prereq.: STAT 200 or equivalent (C- or higher). Application of statistical methods used for a description and analysis of business problems. The development of analytical skills is enhanced by use of one of the widely available statistical packages. Topics include continuation of hypothesis testing, multiple regression and correlation analysis, residual analysis, variable selection techniques, analysis of variance and design of experiments, goodness of fit, and tests of independence. No credit given to students with credit for STAT 216, 416 or 453. Skill Area II.

**STAT 215 Statistics for Behavioral Sciences I 3**

Prereq.: MATH 101 (C- or higher) or placement exam. Introductory treatment of research statistics used in behavioral sciences. Quantitative descriptive statistics, including frequency distributions, measures of central tendency and variability, correlation, and regression. A treatment of probability distributions including binomial and normal. Introduction to the idea of hypothesis testing. No credit given to students with credit for STAT 104, 108, 200, 314 or 315. Skill Area II

**STAT 216 Statistics for Behavioral Sciences II 3**

Prereq.: STAT 215 or permission of instructor. Continuation of STAT 215. Survey of statistical tests and methods of research used in behavioral sciences, including parametric and nonparametric methods. No credit given to students with credit for STAT 201, 416 or 453. Spring. Skill Area II

300s

**STAT 314 Introductory Statistics for Secondary Teachers 3**

Prereq.: MATH 218 and 221. Techniques in probability and statistics necessary for secondary school teaching. Topics include sampling, probability, probability distributions, simulation, statistical inference, and the design and execution of a statistical study. Computers and graphing calculators will be used. No credit given to those with credit for STAT 201, 216 or 453. Graphing calculator required. Fall.

**STAT 315 Mathematical Statistics I 3**

Prereq.: MATH 221; and MATH 218 or permission of department chair. Theory and applications in statistical analysis. Combinations, permutations, probability, distributions of discrete and continuous random variables, expectation, and common distributions (including normal). Fall.

400s

400-LEVEL CLASSES ARE FOR UNDERGRADUATE CREDIT ONLY, EXCEPT WHERE NOTED WITH "[GR]"

**STAT 416 Mathematical Statistics II 3**

Prereq.: STAT 315. Continuation of theory and applications of statistical inference. Elements of sampling, point and interval estimation of population parameters, tests of hypotheses, and the study of multivariate distributions. [GR]

**STAT 425 Loss and Frequency Distributions and Credibility Theory 3**

Prereq.: STAT 416 (may be taken concurrently). Topics chosen from credibility theory, loss distributions, simulation, and time series. Spring. [GR]

**STAT 453 Applied Statistical Inference 3**

Prereq.: Graduate standing with at least one course in statistics or STAT 315 or permission of instructor. Statistical techniques used to make inferences in experiments in social, physical, and biological sciences, and in education and psychology. Topics included are populations and samples, tests of significance concerning means, variances and proportions, and analysis of variance. No credit given to students with credit for STAT 201 or 216. Spring, Summer. [GR]

**STAT 455 Experimental Design 3**

Prereq.: STAT 201 or 216 or 416 or permission of instructor. Introduction to experimental designs in statistics. Topics include completely randomized blocks, Latin square, and factorial experiments. Fall. (O) [GR]

**STAT 456 Fundamentals of SAS 3**

Prereq.: CS 151 and STAT 201 or 216 or equivalent. Introduction to statistical software. Topics may include creation and manipulation of SAS data sets; and SAS implementation of the following statistical analyses: basic descriptive statistics, hypotheses tests, multiple regression, generalized linear models, discriminant analysis, clustering and analysis, factor analysis, logistic analysis and model evaluation. This course is cross listed with MKT 444. No credit given to students with credit for MKT 444. Spring. (E) [GR]

**STAT 465 Nonparametric Statistics 3**

Prereq.: STAT 201 or 216 or 416 or permission of instructor. General survey of nonparametric or distribution-free test procedures and estimation techniques. Topics include one-sample, paired-sample, two-sample, and k-sample problems as well as regression, correlation, and contingency tables. Comparisons with the standard parametric procedures will be made, and efficiency and applicability discussed. Fall. (E) [GR]

**STAT 476 Topics in Statistics 3**

Prereq.: Permission of instructor. Topics depending on interest and qualifications of the students will be chosen from sampling theory, decision theory, probability theory, Bayesian statistics, hypothesis testing, time series or advanced topics in other areas. May be repeated under different topics to a maximum of 6 credits. Spring. (O) [GR]

500s

**STAT 520 Multivariate Analysis for Data Mining 4**

Prereq.: Two semesters of applied statistics (such as STAT 104/453, STAT 200/201, or STAT 215/216), or two semesters of statistics approved by advisor, or permission of department chair. Concept-based introduction to multivariate analysis, useful for data mining and predictive modeling, with emphasis given to interpreting output and checking model assumptions using one of the standard statistical package. Topics may include: multivariate normal distribution, simultaneous inferences, one- and two-way MANOVA, multivariate multiple regression and ANACOVA, correlation, principle component and facor analysis, discriminant analysis, cluster analysis and multidimensional scaling, path analysis, structural equation modeling, and longitudinal data analysis. Fall.

**STAT 521 Introduction to Data Mining 4**

Prereq.: STAT 104 or STAT 200 or STAT 215 or STAT 315 or permission of department chair. Data mining models and methodologies. Topics may include data preparation, data cleaning, exploratory data analysis, statistical estimation and prediction, regression modeling, multiple regression, model building, classification and regression trees and report writing.

**STAT 522 Clustering and Affinity Analysis 4**

Prereq.: STAT 521 or permission of department chair. Investigation and application of methods and models used for clustering and affinity analysis. Topics may include dimension reduction methods, k-means clustering, hierarchical clustering, Kohonen networks clustering, BIRCH clustering, anomaly detection, market basket analysis, and association rules using the a priori and generalized rule induction algorithms. Spring.

**STAT 523 Predictive Analytics 4**

Prereq.: STAT 521 or permission of department chair. Investigation and application of methods and models used for predictive modeling and predictive analytics. Topics may include neural networks, logistic regression, k-nearest neighbor classification, the C4.5 algorithms, CHAID and QUEST decision trees, feature selection, boosting, naive Bayes classification and Bayesian networks, time series, and model evaluation techniques. Fall.

**STAT 525 Web Mining 3**

Prereq.: STAT 521 or permission of department chair. Methods and techniques for mining information from web structure, content, and usage. Topics may include web log cleaning and filtering, de-spidering, user identification, session identification, path completion exploratory data analysis for web mining, and modeling for web mining, including clustering, association, and classification. Spring.

**STAT 526 Data Mining for Genomics and Proteomics 4**

Prereq.: STAT 521 or permission of the instructor. Topics include selection of data mining methods appropriate for the goals of a biomedical study (supervised versus unsupervised, univariate versus multivariate), analysis of gene expression microarray data, biomarker discovery, feature selection, building and validation of classification models for medical diagnosis, prognosis, drug discovery, random forests, and ensemble classifiers. Fall.

**STAT 527 Text Mining 4**

Prereq.: STAT 521 or permission of the instructor. Intensive investigation of text mining methodologies, including pattern matching with regular expressions, reformatting data, contingency tables, part-of-speech tagging, top-down parsing, probability and text sampling, the bag-of-words model and the effect of sample size. Extensive use of Perl and Perl modules to analyze text documents. Spring.

**STAT 529 Current Issues in Data Mining 3**

Prereq.: Admission to the M.S. Data Mining program or permission of department chair. Topics depending on interest and qualifications of the students will be chosen from recent developments in data mining, including statistical pattern recognition, statistical natural language processing, bioinformatics, text mining, and analytical CRM. Use of statistical and data mining software. May be repeated under different topics to a maximum of 9 credits. Migration and Attrition. Extensive use of SPSS' Clementine data mining software is required. Irregular.

**STAT 534 Applied Categorical Data Analysis 3**

Prereq.: STAT 201 or STAT 216, or equivalent, or permission of department chair. Introduction to analysis and interpretation of categorical data using analysis of variance or regression analogs. Topics may include contingency tables, generalized linear models, logistic regression, log-linear models, models for matching pairs, and modeling correlated and clustered responses; use of computer software such as SAS and R. Fall.

**STAT 551 Applied Stochastic Processes 3**

Prereq.: STAT 315 and MATH 228 or permission of instructor. An introduction to stochastic processes. Topics include Markov, Poisson, birth and death, renewal, and stationary processes. Statistical inferences of Markov processes are discussed. Fall. (O)

**STAT 567 Linear Models and Time Series 3**

Prereq.: STAT 416. Introduction to the methods of least squares. Topics include general linear models, least squares estimators, inference, hypothesis testing, and forecasting with ARIMA models. Spring.

**STAT 570 Applied Multivariate Analysis 3**

Prereq.: MATH 228; STAT 416 or, with permission of instructor, STAT 201, 216, or 453. Introduction to analysis of multivariate data with examples from economics, education, psychology, and health care. Topics include multivariate normal distribution, Hotelling's T2, multivariate regression, analysis of variance, discriminant analysis, factor analysis and cluster analysis. Computer packages assist in the design and interpretation of multivariate data. Spring. (O)

**STAT 575 Mathematical Statistics III 3**

Prereq.: STAT 416 or equivalent. Continuation of theory and applications of statistical inference. Advanced topics in the estimation of population parameters and the testing of hypotheses. Introduction to Bayesian methods, regression, correlation and the analysis of variance. Fall. (E)

**STAT 576 Advanced Topics in Statistics 3**

Prereq.: Permission of instructor. Seminar in probability theory, sampling theory, decision theory, Bayesian statistics, hypothesis testing, or other advanced area. Topic depending on needs and qualifications of students. May be repeated under different topics to a maximum of 6 credits. Irregular.

**STAT 599 Thesis 3**

Prereq.: Permission of advisor, and a 3.00 overall GPA. Preparation of thesis under guidance of thesis advisor for students completing master's requirements under M.S. Plan A in Data Mining. On demand.