Degree Requirements    

Prerequisites

The requirements for regular admission to the Department of Experimental Statistics include the following:

• A minimum 3.0 grade-point average overall or in the last two years of study.
• Knowledge of differential and integral calculus (equiv. MATH 191, 192 & 291 at NMSU).
• An undergraduate or graduate level statistics course. (These courses can be taken as undergraduate deficiencies)
• Fluency in written and spoken English is essential
• Expertise in computing or programming is highly recommended

Required Course Work

Candidates for the Master of Science in Experimental Statistics must successfully complete a minimum of 36 semester credits:

Courses Offered

Statistical Methods and Elective Courses:

E ST 503. SAS Basics 2 cr. (1+2P)
A brief introduction to the statistical software package, SAS, and its utilization in an interactive computing environment, primarily CMS/ SAS. Provides a fundamental understanding of the structure of SAS, its data management capabilities, and how to invoke a variety of descriptive and simple statistical SAS procedures. Statistical concepts will not be a primary focus. Corequisite: E ST 456, E ST 501, or E ST 505, or consent of instructor.

E ST 504. Statistical Software Applications 1 cr.
Optional computing course to accompany E ST 506. Computer analysis of topics covered in E ST 505 and E ST 506. Prerequisite: E ST 503 or consent of instructor. Corequisite: E ST 506 or E ST 502 or consent of instructor.

E ST 505. Statistical Inference I 4 cr. (3+2P)
A qualitative introduction to the concepts and methods of statistical inference. Sampling, frequency distributions (z, t, x2, F), estimation, and testing. One-way analysis of variance. Simple linear regression. Prerequisite: consent of the instructor.

E ST 506. Statistical Inference II 3 cr. (2+2P)
Introduction to multiple regression; the analysis of variance for balanced studies; multiple comparisons, contrasts, factorials, experimental designs through split plots. Prerequisite: E ST 505 and the ability to use a standard computer package such as SAS (may be satisfied by E ST 503) or consent of instructor.

E ST 507. Advanced Regression 3 cr.
Examination of multiple regression; residual analysis, collinearity, variable selection, weighted least squares, polynomial models, and nonlinear regression: linearizable and intrinsically nonlinear models. Prerequisites: E ST 503 and E ST 505 or consent of instructor.

E ST 508. Analysis of Advanced Designs and Related Topics 3 cr.
Complete and incomplete block designs; fixed, mixed, and random models; unbalanced data; analysis of covariance; nested experiments; fractional factorials. Prerequisites: E ST 504, and one of E ST 502 or E ST 506; or consent of instructor.

E ST 521. Sampling Methodology 3 cr. (3+2P)
Methodology of sampling finite populations using design-based (simple random, stratified, systematic, cluster, and multistage), model-based (regression and ratio estimators), and adaptive sampling. Properties of estimators under all designs are discussed. Prerequisite: either E ST 456, E ST 501, E ST 505, E ST 565, or consent of instructor.

E ST 522. Survey Sampling 2 cr. (3+2P)
Techniques of survey sampling (mail questionnaire and telephone surveys) applicable to social sciences. Techniques of questionnaire preparation and methods of evaluating results are presented. Prerequisite: E ST 521, or consent of instructor.

E ST 523. Biological Sampling (s) 3 cr.
Methods of sampling biological populations: area frame, quadrat, line intercept, line transect, and mark-recapture. Prerequisite: E ST 501 or E ST 505 or consent of instructor.

E ST 524. Selected Topics in Sampling 2 cr.
Treatment of nonresponse in sample surveys; response error modeling and estimation. Other topics to be selected from among the following: approximate methods for variance estimation, panel rotation sampling, longitudinal survey design and estimation, telephone random-digit-dialing, model based estimation, and multiplicity sampling. Prerequisite: E-ST-521 or consent of instructor.

E ST 545. Time Series Analysis and Applications 3 cr.
A systematic exposition of the methods for analyzing, modeling, and forecasting time series. Emphasizes underlying ideas and methods rather than detailed mathematical derivations, using SAS, BMDP, IMSL, and Fortran. Prerequisites: E ST 503 and E ST 501 or E ST 505, or consent of instructor.
 

E ST 550. Special Topics 1-4 cr.
Specific subjects to be announced in the Schedule of Classes. Maximum of 4 credits per semester. No more than 9 credits toward a degree.

E ST 555. Applied Multivariate Analysis 3 cr.
Multivariate analysis of linear statistical models, including MANOVA and repeated measures. Analysis of correlation and covariance structures, including principal components, factor analysis, and canonical correlation. Classification and discrimination techniques. Prerequisites: E ST 506 and E ST 504 or consent of instructor.
 

E ST 596. Independent Study 1-3 cr.
Individual studies directed by consenting faculty with prior approval by department head. Prerequisite: consent of instructor. May be repeated for a maximum of 3 credits.
 

Consulting Courses (for Majors Only)

E ST 551. Introduction to Statistical Consulting 1 cr.
Consideration of published material in the consulting process. Prerequisite: consent of instructor. Restricted to majors. Graded S/U.

E ST 552. Advanced Statistical Consulting 1 cr.
Continuation of E ST 551 with emphasis on dealing with clients in order to identify statistically relevant features of a research study. Prerequisite: E ST 551. Restricted to majors. Graded S/U.

E ST 553. Practicum in Statistical Consulting 1 cr.
Supervised experience under the guidance of senior faculty. Prerequisite: E ST 552. May be repeated for a maximum of 2 credits. Restricted to majors. Graded S/U.
 

Theory Courses

E ST 565. Statistical Analysis I 4 cr. (3+2P)
An analytic introduction to the theory and methods of statistical inference. Sampling, frequency distributions (z, t, x2, F), estimation, testing, and simulation. Prerequisite: MATH 291 or consent of instructor.

E ST 566. Statistical Analysis II 4 cr. (3+2P)
Continuation of E ST 565. Prerequisite: E ST 565 or consent of instructor.

E ST 567. Applied Linear Models I 3 cr.
The mean model, including constraints, approach to linear models; nonidentity variance-covariance matrices. Some emphasis on computational aspects and relation to statistical packages. Prerequisite: E ST 566 or consent of instructor.

E ST 568. Applied Linear Models II 3 cr.
The relation of full to less-than-full rank linear models; complex data structures, including “messy” data, empty cells, and components of variance: extensions to categorical data analysis and nonparametric methods. Continues some emphasis on computational aspects. Prerequisite: E ST 567.
 

Research Courses

E ST 598. Special Research Problems 1-6 cr.
Individual analytical or experimental projects. Restricted to majors. Graded S/U.

E ST 599. Master’s Thesis var. cr.
Thesis.