The University of New Mexico |

STAT: Statistics Catalog Description |

Three Year Fall Semester Average | ||||
---|---|---|---|---|

Course | Catalog Description | Enrollment | Section Capacity |
Credits |

STAT145 | Techniques for the visual presentation of numerical data, descriptive statistics, introduction to probability and basic probability models used in statistics, introduction to sampling and statistical inference, illustrated by examples from a variety of fields. Prerequisite: ACT = >22 or SAT = >510 or MATH 120 or 121 or 123 or 150 or 162 or 163 or 180 or 181 or 264. {Summer, Fall, Spring} | 1,109.67 | 1,230.00 | 3,329.00 |

STAT345 | An introduction to probability including combinatorics, Bayes' theorem, probability densities, expectation, variance and correlation. An introduction to estimation, confidence intervals and hypothesis testing. Prerequisite: MATH 181 or MATH 163. {Summer, Fall, Spring} | 71.00 | 115.00 | 213.00 |

STAT425 | A detailed introduction to the SAS programming language. Topics covered include reading data, storing data, manipulating data, data presentation, graphing, and macro programming. SAS software will be used. Prerequisites: 345 and 427 or permission of instructor. | |||

STAT427 | Statistical tools for scientific research, including parametric and non-parametric methods for ANOVA and group comparisons, simple linear and multiple linear regression, and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as Minitab and SAS. Prerequisite: 145. {Fall} | |||

STAT428 | A continuation of 427 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principal components, discriminant analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression. Prerequisite: 427 or permission of instructor. | 18.00 | 43.33 | 54.00 |

STAT434 | This course examines the use of log-linear models to analyze count data. It also uses graphical models to examine dependence structures for both count data and measurement data. Prerequisites: 345 and 427. | 1.00 | 6.67 | 3.00 |

STAT440 | Simple regression and multiple regression. Residual analysis and transformations. Matrix approach to general linear models. Model selection procedures, nonlinear least squares, logistic regression. Computer applications. Prerequisite: 427. {Fall} | |||

STAT445 | A data-analytic course. Multifactor ANOVA. Principles of experimental design. Analysis of randomized blocks, Latin squares, split plots, etc. Random and mixed models. Extensive use of computer packages with interpretation, diagnostics. Prerequisite: 440. {Spring} | 4.67 | 11.67 | 14.00 |

STAT453 | Transformations of univariate and multivariate distributions to obtain the special distributions important in statistics. Concepts of estimation and hypothesis testing in both large and small samples with emphasis on the statistical properties of the more commonly used procedures, including student's t-tests, F-tests and chi-square tests. Confidence intervals. Performance of procedures under non-standard conditions (i.e., robustness). Prerequisite: 461. {Spring} | 1.00 | 25.00 | 3.00 |

STAT461 | (Also offered as MATH 441.) Mathematical models for random experiments, random variables, expectation. The common discrete and continuous distributions with application. Joint distributions, conditional probability and expectation, independence. Laws of large numbers and the central limit theorem. Moment generating functions. Prerequisite: MATH 264. {Fall} | |||

STAT470 | Basic ideas of statistical quality control and improvement. Topics covered: Deming's 14 points and deadly diseases, Pareto charts, histograms, cause and effect diagrams, control charts, sampling, prediction, reliability, experimental design, fractional factorials, Taguchi methods, response surfaces. Prerequisite: 345. | |||

STAT472 | Basic methods of survey sampling; simple random sampling, stratified sampling, cluster sampling, systematic sampling and general sampling schemes; estimation based on auxiliary information; design of complex samples and case studies. Prerequisite: 345. {Alternate Falls} | 2.33 | 3.33 | 7.00 |

STAT474 | A detailed overview of methods commonly used to analyze medical and epidemiological data. Topics include the Kaplan-Meier estimate of the survivor function, models for censored survival data, the Cox proportional hazards model, methods for categorical response data including logistic regression and probit analysis, generalized linear models. Prerequisite: 428 or 440 or permission of instructor. | 1.67 | 10.00 | 5.00 |

STAT476 | Tools for multivariate analysis including multivariate ANOVA, principal components analysis, discriminant analysis, cluster analysis, factor analysis, structural equations modeling, canonical correlations and multidimensional scaling. Prerequisite: 428 or 440 or permission of instructor. {Offered upon demand} | |||

STAT477 | An introduction to Bayesian methodology and applications. Topics covered include: probability review, Bayes' theorem, prior elicitation, Markov chain Monte Carlo techniques. The free software programs WinBUGS and R will be used for data analysis. Prerequisites: 461 and either of 427 or 440, or permission of instructor {Alternate Springs}. | 0.33 | 3.33 | 1.00 |

STAT479 | Modern topics not covered in regular course offerings. | 0.00 | 0.67 | 0.00 |

STAT481 | Introduction to time domain and frequency domain models of time series. Data analysis with emphasis on Box-Jenkins methods. Topics such as multivariate models; linear filters; linear prediction; forecasting and control. Prerequisite: 461. {Alternate Springs} | |||

STAT495 | Guided study, under the supervision of a faculty member, of selected topics not covered in regular course offerings. | 0.33 | 75.00 | 1.00 |

STAT525 | A detailed introduction to the SAS programming language. Topics covered include reading data, storing data, manipulating data, data presentation, graphing, and macro programming. SAS software will be used. Prerequisites: 345, 427 or permission of instructor. | |||

STAT527 | Statistical tools for scientific research, including parametric and non-parametric methods for ANOVA and group comparisons, simple linear and multiple linear regression and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as Minitab and SAS. Course cannot be counted in the hours needed for graduate degrees in Mathematics and Statistics. Prerequisite: 145. {Fall} | |||

STAT528 | A continuation of 527 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principal components, discriminate analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression. Prerequisite: 527 or permission of instructor. | 21.00 | 47.33 | 63.00 |

STAT531 | A detailed examination of the statistical methods used in analyzing genetic data. Topics covered include the estimation of allele frequencies, testing for Hardy-Weinberg equilibrium, classical and complex segregation analysis, linkage analysis for Mendelian and complex diseases, and the detection of allelic association. Popular genetic software will be used for data analysis. Prerequisites: 345, 427 or permission of instructor. {Alternate Falls} | |||

STAT532 | A continuation of 531. Topics covered include statistical methods for describing variation in quantitative traits, methods of mapping and characterizing quantitative trait loci and other current topics in statistical genetics, including the analysis of microarray data and phylogenetic methods. Popular genetic software will be used for data analysis. Prerequisite: 531 or permission of instructor. {Alternate Springs} | |||

STAT534 | This course examines the use of log-linear models to analyze count data. It also uses graphical models to examine dependence structures for both count data and measurement data. Prerequisites: 345, 527. | 2.33 | 8.33 | 7.00 |

STAT538 | Covers basic statistical methods, including statistical summaries and inference. Methods of summarizing data include graphical displays and numerical summaries. Statistical inference includes hypothesis testing and confidence intervals. Methods for continuous and categorical data are studied. Prerequisite: B or better in MATH 121 or permission of instructor. {Fall} | |||

STAT539 | Covers basic models used in the statistical analysis of studies in the medical sciences and public health field, with an emphasis on epidemiology. Linear regression, analysis of variance, logistic regression, and survival models are studied. Prerequisite: 538 or permission of instructor. {Spring} | 4.33 | 4.00 | 13.00 |

STAT540 | Simple regression and multiple regression. Residual analysis and transformations. Matrix approach to general linear models. Model selection procedures, nonlinear least squares, logistic regression. Computer applications. Prerequisite: 527. {Fall} | |||

STAT545 | A data-analytic course. Multifactor ANOVA. Principles of experimental design. Analysis of randomized blocks, Latin squares, split plots, etc. Random and mixed models. Extensive use of computer packages with interpretation, diagnostics. Prerequisite: 540. {Spring} | 9.00 | 16.67 | 27.00 |

STAT546 | Theory of the Linear Models discussed in 440/540 and 445/545. Linear spaces, matrices, projections, multivariate normal distribution and theory of quadratic forms. Non-full rank models and estimability. Gauss-Markov theorem. Distribution theory for normality assumptions. Hypothesis testing and confidence regions. Prerequisites: 553, 545, linear algebra. {Alternate Falls} | |||

STAT547 | Hotelling T2, multivariate ANOVA and Regression, classification and discrimination, principal components and factor analysis, clustering, graphical and computational techniques, topics in linear models. Prerequisite: 546. {Alternate Springs} | 1.67 | 11.67 | 5.00 |

STAT553 | Transformations of univariate and multivariate distributions to obtain the special distributions important in statistics. Concepts of estimation and hypothesis testing in both large and small samples with emphasis on the statistical properties of the more commonly used procedures, including Students t-tests, F-tests and chi-square tests. Confidence intervals. Performance of procedures under non-standard conditions (i.e., robustness). Prerequisite: 561. {Spring} | 11.33 | 27.00 | 34.00 |

STAT556 | Theory and methods of point estimation, sufficiency and its applications. Prerequisite: 553, 561 and MATH 510. {Alternate Falls} | |||

STAT557 | Standard limit theorems, hypothesis testing, confidence intervals and decision theory. Prerequisite: 556. {Alternate Springs} | 1.67 | 8.33 | 5.00 |

STAT561 | Mathematical models for random experiments, random variables, expectation. The common discrete and continuous distributions with application. Joint distributions, conditional probability and expectation, independence. Laws of large numbers and the central limit theorem. Moment generating functions. Prerequisite: MATH 264. {Fall} | |||

STAT565 | (Also offered as MATH 540.) Markov chains and processes with applications. Classification of states. Decompositions. Stationary distributions. Probability of absorption, the gambler's ruin and mean time problems. Queuing and branching processes. Introduction to continuous time Markov processes. Jump processes and Brownian motion. Prerequisite: 561 or permission of instructor. {Offered on demand} | 3.00 | 5.67 | 9.00 |

STAT567 | (Also offered as MATH 541.) A measure theoretic introduction to probability theory. Construction of probability measures. Distribution and characteristic functions, independence and zero-one laws. Sequences of independent random variables, strong law of large numbers and central limit theorem. Conditional expectation. Martingales. Prerequisite: MATH 563. {Alternate Springs} | 1.33 | 2.67 | 4.00 |

STAT569 | (Also offered as MATH 549.) May be repeated for credit, no limit. | |||

STAT570 | Basic ideas of statistical quality control and improvement. Topics covered: Demings 14 points and deadly diseases, Pareto charts, histograms, cause and effect diagrams, control charts, sampling, prediction, reliability, experimental design, fractional factorials, Taguchi methods, response surfaces. Prerequisite: 345. | |||

STAT572 | Basic methods of survey sampling; simple random sampling, stratified sampling, cluster sampling, systematic sampling and general sampling schemes; estimation based on auxiliary information; design of complex samples and case studies. Prerequisite: 345. {Alternate Falls} | 1.67 | 6.67 | 5.00 |

STAT574 | A detailed overview of methods commonly used to analyze medical and epidemiological data. Topics include the Kaplan-Meier estimate of the survivor function, models for censored survival data, the Cox proportional hazards model, methods for categorical response data including logistic regression and probit analysis, generalized linear models. Prerequisite: 528 or 540 or permission of instructor. | 5.67 | 9.33 | 17.00 |

STAT576 | Tools for multivariate analysis including multivariate ANOVA, principal components analysis, discriminant analysis, cluster analysis, factor analysis, structural equations modeling, canonical correlations and multidimensional scaling. Prerequisite: 528 or 540 or permission of instructor. {Offered upon demand} | |||

STAT577 | An introduction to Bayesian methodology and applications. Topics covered include: probability review, Bayes' theorem, prior elicitation, Markov chain Monte Carlo techniques. The free software programs WinBUGS and R will be used for data analysis. Prerequisites: 561 and either of 527 or 540, or permission of instructor {Alternate Springs}. | 5.00 | 6.67 | 15.00 |

STAT579 | May be repeated for credit, no limit. | 13.33 | 29.67 | 40.00 |

STAT579A | ||||

STAT579B | ||||

STAT579C | ||||

STAT579D | ||||

STAT581 | Introduction to time domain and frequency domain models of time series. Data analysis with emphasis on Box-Jenkins methods. Topics such as multivariate models; linear filters; linear prediction; forecasting and control. Prerequisite: 561. {Alternate Springs} | |||

STAT582 | Time series models in the time and spectral domains. Linear filters. Multivariate models. Autoregressive and moving average models. Filtering and prediction. Distribution theory. Design of experiments. Prerequisite: 581. {Alternate Falls} | |||

STAT586 | Nonparametric regression, density estimation, filtering, spectral density estimation, image reconstruction and pattern recognition. Tools include orthogonal series, kernels, splines, wavelets and neural networks. Applications to medicine, engineering, biostatistics and economics. Prerequisite: 561 or permission of instructor. {Offered upon demand} | |||

STAT590 | A detailed examination of essential statistical computing skills needed for research and industrial work. Students will use S-Plus, Matlab and SAS to develop algorithms for solving a variety of statistical problems using resampling and simulation techniques such as the bootstrap, Monte Carlo methods and Markov chain methods for approximating probability distributions. Applications to linear and non-linear models will be stressed. Prerequisite: 528 or permission of instructor. | 4.00 | 4.33 | 12.00 |

STAT595 | May be repeated for credit, no limit. | |||

STAT597 | Provides experience in statistical consulting and analysis of real data. May be repeated for credit, no limit. Prerequisite: 528 or permission of instructor. | 0.33 | 25.00 | 0.33 |

STAT599 | Offered on a CR/NC basis only. | 2.67 | 125.00 | 4.67 |

STAT605 | Students present their current research. | 0.67 | 25.00 | 0.67 |

STAT649 | (Also offered as MATH 649.) May be repeated for credit, no limit. | 2.33 | 19.00 | 2.33 |

STAT650 | 3.67 | 175.00 | 8.67 | |

STAT699 | Offered on a CR/NC basis only. | 7.00 | 175.00 | 46.00 |

"STAT: Statistics "Three Fall Subject Average - Enroll: 1,312.00 Capacity: 2,256.33 Credits: 3,948.67' |