Statistics classes

Does the thought of statistics fill you with terror or are you just a little rusty? Especially if you are going to be doing research or completing a thesis/dissertation, statistics are critical in graduate school. Below are some class options ranked from introductory to more advanced.

General Advice from Dr. Stehman:
1) APM 510 is intended for graduate students who have had no previous statistics or want a refresher course to prepare for more advanced courses. APM 510 or equivalent is the pre-requisite for all other courses.
2) APM 620 and APM 630 are the foundation courses for design and analysis. Taking both courses would provide students with the core statistical methods most commonly used in practice.
3) Students seeking to expand their statistics ‘toolbox’ would take additional courses depending on their interest and research needs.
- Source: Dr. Stephen V. Stehman, Department of Forest Natural Resources, SUNY-ESF, personal communication

Can’t fit one of these classes in your schedule? Consider sitting in or auditing. You won’t get as much out of the class but you will get your feet wet. Note: There are some classes that do not allow “sit-ins”.

APM 510 Statistical Analysis (3)
A great starter class! Three hours of lecture per week. Applications of descriptive and inferential statistics to natural resource problems. Basic concepts and techniques of estimation, confidence intervals, and hypothesis testing applied to one- and two-sample settings, paired designs, simple linear regression and correlation, contingency tables, and goodness of fit tests. Statistical software (MiniTab) used to enhance data analysis skills. Fall.

APM 620 Experimental Design and ANOVA (3)
Three hours of lecture per week. Designing and analyzing experiments and observational studies; completely randomized, split plot, randomized complete block, and nested experiment designs; single-factor, factorial, and repeated measures treatment designs; expected mean squares and variance components; fixed, random, and mixed effects models; multiple comparison and contrast analyses; analysis of covariance; statistical computing. Spring.
Prerequisites: Graduate status and an introductory course in statistics covering material through the one-way analysis of variance.

APM 625 Sampling Methods (3)
Three hours of lecture per week. Application of probability sampling methods to environmental science and forestry. Simple random, stratified, cluster, systematic, two-phase, line-intercept, point, variable radius plot, adaptive cluster, and other variable probability sampling designs; model-assisted ratio and regression estimators; inclusion probabilities; properties of estimators for design-based inference; Horvitz-Thompson estimation as a unifying theory. Fall.

APM 630 Regression Analysis (3)
Three hours of lecture per week. Review of basic statistical concepts and matrix algebra. Classical simple and multiple linear models, indicator or dummy variables, residual analysis, transformation and weighted least squares, influence diagnostics, multicollinearity, nonlinear models and linear mixed models. Statistical computing using SAS and applications in forestry, biology, engineering, and social sciences. Spring. Prerequisite: APM 391 or equivalent.

APM 635 Multivariate Statistical Methods (3)
Three hours of lecture per week. Review of basic statistical concepts and matrix algebra. Multivariate normal distribution, Hotelling's T2, multivariate analysis of variances, principal component analysis, factor analysis, discrimination and classification, cluster analysis, and canonical correlation analysis. Statistical computing using SAS and applications in forestry, biology, engineering, and social sciences. Fall. Prerequisites: APM 391 or equivalent.

APM 645 Nonparametric Statistics and Categorical Data Analysis (3)
Three hours of lecture per week. Topics include: review of basic statistics, sign and ranked sign tests, median and Wilcoxon tests, c2 binomial tests, -test and contingency tables (with correspondence analysis), goodness-of-fit, nonparametric correlation and association analysis, logistic and Poisson regression, nonparametric regression techniques such as LOESS, GAM, and robust regression, bootstrapping and jackknifing. Fall (even years). Prerequisite: APM 391 or equivalent.

SU Classes:

MAT 521 and MAT 525. These two courses provide the foundation of statistical theory ("mathematical statistics") that is useful for someone who wants a good theoretical basis to underlie his or her understanding of applied statistics.

MAT 521 Introduction to Probability and Statistics
3 Credits - Offered each semester
Algebra of sets. Probability in finite samples spaces. Binomial and multinomial coefficients. Random variables. Expected value and standard deviation. Density functions. Statistical applications.

MAT 525 Mathematical Statistics
3 Credits - Offered every year
Estimation and confidence intervals. Normal distribution and central limit theorem. Testing hypotheses, chi-square, t, and F distributions. Least squares, regression, and correlation.

Partial Definition of Terms:

Hypothesis test: a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level.

ANOVA (Analysis of Variance): In its simplest form ANOVA provides a statistical test of whether or not the means of several groups are all equal.

Regression: Regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables change.

Multivariate: Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one statistical variable. The application of multivariate statistics is multivariate analysis. Methods of bivariate statistics, for example simple linear regression and correlation, are special cases of multivariate statistics in which two variables are involved.

Nonparametric Statistics: The first meaning of non-parametric covers techniques that do not rely on data belonging to any particular distribution. The second meaning of non-parametric covers techniques that do not assume that the structure of a model is fixed. Typically, the model grows in size to accommodate the complexity of the data.

Linear programming: Linear programming (LP) is a mathematical method for determining a way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model for some list of requirements represented as linear relationships.