### Catalog Information Subject to Change

The current ESF Catalog is online only, and is updated as needed throughout the year. To view the version officially associated with a particular year of entry to the College, please refer to the appropriate **catalog of record**.

# ESF Course Descriptions

- APM—Applied Mathematics
- BPE—Bioprocess Engineering
- BTC—Biotechnology
- CME—Construction Management Engineering
- EFB—Environmental and Forest Biology
- EHS—Environmental Health
- ENS—Environmental Science
- ERE—Environmental Resources Engineering
- ESC—Environmental Science
- ESF—College-wide
- EST—Environmental Studies
- EWP—Environmental Writing Program
- FCH—Chemistry
- FOR—Forestry (Resources Management)
- FTC—Forest Technology
- GNE—General Engineering
- LSA—Landscape Architecture
- MCR—Microscopy
- PSE—Paper Science and Engineering
- RMS—Renewable Materials Science
- SRE—Sustainable Renewable Energy

Scroll down to view after selecting:

**APM 101 Fundamentals of College Algebra (3)**

Three hours of lecture/discussion per week. Algebraic operations on polynomials and rational functions as expressions, in equations, or inequalities. Graphing of linear and polynomial equations. An emphasis is placed on algebraic operations of expressions with rational exponents. Fall.

**APM 103 Applied College Algebra and Trigonometry (3)**

Three hours of lecture per week. This course is designed to enable non-science students to solve practical problems in their specific areas of study. Topics include algebraic, exponential, logarithmic, and trigonometric functions used in measurement and modeling. Applications include percents, scaling, slopes,and contour mapping. Spring, Fall.

Prerequisite(s): Math Placement or Consent of Instructor.

**APM 104 College Algebra and Precalculus (3)**

Three hours of lecture/discussion per week. Course meets the SUNY general education requirement for mathematics. Elements of analytic geometry. Emphasis on the concepts of polynomial and rational functions, exponential and logarithmic functions, trigonometry and trigonometric functions and their application to design and life and management sciences. Fall and Spring.

Prerequisite: Three years of high school mathematics.

**APM 105 Survey of Calculus and Its Applications I (4)**

Four hours of lecture per week. Introduction to calculus for students in the life and management sciences. Elements of analytic geometry, functions and their graphs, with an emphasis on the concepts of limits, and differentiation techniques for algebraic, exponential and logarithmic functions and their application to economics, and the life and management sciences. Some multivariable calculus including constrained optimization. Fall and Spring.

Prerequisite: Precalculus or 3 1/2 years of high school mathematics.
Note: Credit will not be granted for APM 105 after successful completion of MAT 284, MAT 285, or MAT 295 at SU.

**APM 106 Survey of Calculus and Its Applications II (4)**

Four hours of lecture per week. A continuation of calculus for students in the life and management sciences. Elements of analytic geometry. An introduction to integration and applications of the definite integral. Differentiation and integration of trigonometric functions. Applications of first order differential equations and partial derivatives. Spring.

Prerequisite: APM 105 or permission of the instructor.
Note: Credit will not be granted for APM 106 after successful completion of MAT 286 or MAT 296 at SU.

**APM 115 Essential Calculus (4)**

A one semester course in differential and integral calculus. An emphasis on the
concepts of limits, differentiation and integration techniques for algebraic,
exponential, logarithmic functions, and trigonometric functions. This course is not
intended for students that plan on taking additional Calculus courses. Offered in fall and spring.

Credits will not be granted for APM 115 after successful completion of any Calculus course such as
APM105, MAT 284, or beyond.
Prequisites: APM 103 or APM 104, or equivalent.

**APM 205 Calculus I for Science and Engineering (4)**

Four hours of lecture/discussion per week. Analytic geometry, limits, derivatives of functions and equations, optimization, rates, graphs, differentials, mean-value theorem, and applications of the derivative. Fall.

Prerequisite: APM 104 or permission of instructor.

**APM 206 Calculus for Science and Engineering II (4)**

Four hours of lecture/discussion per week. This course is a one semester continuation of differential calculus. Integral calculus is used to describe growth and size. Topics include: techniques of integration and their application, convergence of sequences and series, separable and first-order differential equations, and polar coordinates. Spring.

Prerequisite(s): Successful completion of a differential calculus course such as APM205 or MAT295.

**APM 307 Multivariable Calculus (4)**

4 hours of lecture/discussion per week. Topics include vectors three dimensions, analytic geometry of three dimensions, parametric curves, partial derivatives, the gradient, optimization in several variables, multiple integration with change of variables across different coordinate systems, line integrals, and Green's Theorem. Fall and Spring.

Prerequisites: Completion of Differential and Integral Calculus with at least a C-; APM206 / MAT296, or the equivalent
Note: Credit cannot be given for both APM307 and MAT397.

**APM 391 Introduction to Probability and Statistics (3)**

Three hours of lecture per week. Introduction to concepts and methods of statistics as applied to problems in environmental science and forestry. Topics include inference (confidence intervals and hypothesis testing), sampling distributions, descriptive statistics, exploratory data analysis, comparison of population means and proportions, categorical data analysis, regression and correlation, and nonparametric methods. Fall or Spring.

**APM 395 Probability and Statistics for Engineers (3)**

Three hours of lecture per week. This course provides a rigorous introduction to calculus-based probability and statistical theory, with applications primarily drawn from engineering and the environmental sciences. Topics include: descriptive statistics and data presentation, probability, the theory and use of discrete and continuous probability distributions, confidence intervals, classical and distributional hypothesis testing, and regression analyses. Spring.

Prerequisite(s): One year of Calculus.
Note: Credit will not be granted for both APM 395 and APM 595.

**APM 485 Differential Equations for Engineers and Scientists (3)**

Three hours of lecture per week. First and second order ordinary differential equations, matrix algebra, eigen values and eigen vectors, linear systems of ordinary differential equations, numerical solution techniques and an introduction to partial differential equations. Spring.

Prerequisite: MAT 295, MAT 296, MAT 397.

**APM 510 Statistical Analysis (3)**

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 used to enhance data analysis skills. Fall.

Prerequisite(s): Graduate standing.

**APM 585 Partial Differential Equations for Engineers and Scientists (3)**

Three hours of lecture per week. Analytical solutions of parabolic, hyperbolic and elliptic partial differential equations which appear in science and engineering. Numerical and approximate methods of solution. Spring.

Prerequisites: APM 485; or equivalent course.

**APM 585 Partial Differential Equations for Engineers and Scientists (3)**

Three hours of lecture per week. Analytical solutions of parabolic, hyperbolic and elliptic partial differential equations which appear in science and engineering. Numerical and approximate methods of solution. Spring.

Prerequisites: APM 485; or equivalent course.

**APM 595 Probability and Statistics for Engineers (3)**

Three hours of lecture per week. This course provides a rigorous introduction to calculus-based probability and statistical theory, with applications primarily drawn from engineering and the environmental sciences. Topics include: descriptive statistics and data presentation, probability, the theory and use of discrete and continuous probability distributions, confidence intervals, classical and distributional hypothesis testing, and regression analyses. Spring.

Prerequisite(s): One year of Calculus.
Note: Credit will not be granted for both APM 395 and APM 595.

**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. Topics include review of basic statistical concepts and matrix
algebra, classical simple and multiple linear regression models, indicator or dummy variables in regression, residual analysis, transformation and logistic regression, weighted least squares, influence diagnostics, multicollinearity, nonlinear regression models, linear mixed models, statistical computing using SAS and interpretation of results. Fall.

Prerequisite: APM 391 or equivalent.

**APM 635 Multivariate Statistical Methods (3)**

Three hours of lecture per week. Topics include review of basic statistical concepts and matrix
algebra, multivariate normal distribution, Hotelling's T 2, multivariate analysis of variances, principal
component analysis, factor analysis, discrimination and classification, cluster analysis, and canonical
correlation analysis, statistical computing using SAS and interpretation of results. Spring.

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, binomial tests, x 2-test and contingency tables (with correspondence analysis), goodness-of-fit, nonparametric correlation and association analysis,
nonparametric and robust regression, generalized linear models (Logistic and Poisson regression), and re-sampling methods (bootstrapping and cross-validation), statistical computing using SAS and interpretation of results. Fall.

Prerequisite: APM 391 or equivalent.

**APM 671 Map Accuracy Assessment (1)**

One hour of lecture per week.Statistical concepts and methods for quantifying the accuracy of maps. Sampling design and analysis for assessing accuracy of categorical attributes (e.g. land cover) is emphasized, with some discussion of continuous variables. Spring, even numbered years.

**APM 696 Special Topics in Quantitative Methods (1 - 3)**

Experimental and developmental courses in areas of quantitative methods not covered in regularly scheduled courses. A course syllabus will be available to students and faculty advisors prior to registration. Fall or Spring.

**APM 730 Advanced Regression Modeling Methods (3)**

Three hours of lecture per week. Topics include: review of basic regression modeling techniques, theory of generalized linear models and techniques (e.g. Logistic, Poisson and Beta regression), quantile regression, linear and nonlinear mixed models, variogram and kriging, spatial regression models (e.g., spatial lag and spatial error models), local spatial statistics and models (geographically weighted regression), statistical computing using SAS, and interpretation of results. Spring.

Prerequisite: APM 630 or equivalent