Dr. Goad is a member of the American Statistical Association. She also serves as the Treasurer for the Oklahoma Chapter of the ASA.   

Dr. Goad teaches the SAS Programming Class, Experimental Design, Linear Models, Engineering Statistics with Experimental Design and some of the other methods courses that the Statistics Department offers. 

Course Information   Courses that are presently taught by Dr. Goad are linked to a course website.

       STAT 4091 (5910) SAS Programming 

An Introduction to programming and point and click operations in SAS.  The prerequisite for this course is a one-semester introductory course in statistical methods.

      STAT 4910 Engineering Statistics with Design of Experiments

Topics covered in this course are data summarization, graphical methods, random variables, probability distributions, hypothesis testing for population means, variances and proportions, linear regression, completely randomized designed, blocked experiments, factorial experiments, fractional factorials, response surface methods, quality control.

    STAT 5013 Statistics for Experimenters I 

Introductory statistics course for graduate students.  Descriptive statistics, basic probability, probability distributions, fundamentals of statistical inference, hypothesis testing, one-way classification, analysis of variance, correlation and linear regression, introduction to categorical data analysis.

       Prerequisite:  graduate standing and Math 1513 College Algebra

    STAT 5023 Statistics for Experimenters II

Analysis of variance, covariance, use of variance components and their estimation, completely randomized, randomized block and Latin square designs, multiple comparisons.

        Prerequisites: graduate standing and 4023 or 5013.

       STAT 5303 Experimental Design  

Review of basic concepts and principles of comparative experiments, the role of randomization in experimentation, interpretation of effects and interactions in multi-factor designs, error term selection principals, multiple comparisons, split-unit experiments, incomplete block designs, confounding of factorial effects in 2n and 3n series of factorials, single and fractional replication, optimum seeking designs, pooling of experiments over time and/or space, crossover and switch back designs. 

STAT 5323 Theory of Linear Models I 

Topics covered:  matrix theory, multivariate normal distribution, quadratic forms, general linear models, Markov theory, variance components, general hypotheses of full rank models

Prerequisite courses:  STAT 4223 Inference, MATH 3013 Linear Algebra and STAT 5023  Statistics for Experimenters II or an equivalent course

STAT 5333 Theory of Linear Models II

Maximum Likelihood Estimation of Model Parameters, Likelihood Ratio Tests, Confidence Interval Estimation,  Vector Spaces, Generalized Inverse of a Matrix, Geometric Interpretations, Less Than Full Rank Models, General Linear Model, Repeated Measures

More Information
       OSU Department of Statistics

Find out more about statistics degree programs, courses, and faculty at Oklahoma State University. 

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Top Ten Reasons Statisticians Become Statisticians - credit for this list is given to the Department of Statistics at Kansas State University, Manhattan, Kansas.