APM-Applied Mathematics
Course Descriptions

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 153. Computing Methods for Engineers and
Physical Scientists (3)
Three hours of lecture per week. Introduction to programming structures: flowcharts, language statements and subprograms. Introduction to data structures: arrays, scalars and others. Introduction to data codes: numbers and characters, "natural" and binary. Introduction to algorithms at the procedural level. Spring.

APM 255. Computing Applications (3)
Three hours of lecture per week. Introduction to computing resources: timeshared and personal computers. Introduction to basic computing concepts. Introduction to computing and computer networks. Introduction to applications computing: word processing, spreadsheets and communications (electronic mail and other Internet services). Spring.

APM 360. Introduction to Computer Programming (3)
Three hours of lecture per week. The basic course in computer programming offered by the college, giving the student the skill and understanding to write computer programs to solve problems. The course will cover instruction in a commonly-used programming language such as Pascal or FORTRAN; will cover basic hardware and software concepts; will make use of electronic mail and computer networks; will introduce applications software, such as spreadsheets, statistical software or other appropriate types. No prior experience with computers or programming is required. Fall.

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 including visual and numerical data presentation, probability including set theory, conditional probability, independence, and counting techniques, the theory of discrete and continuous probability distributions including the usage of commonly employed probability distributions, confidence interval estimation and classical hypothesis testing, probability plots and associated normality and lognormality tests, simple linear regression, and an introduction to ANOVA. Spring.
Pre- or co-requisite(s): Calculus through Integral 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 500. Introduction to Computer Programming for Graduate Students (3)
Three hours of lecture per week. A basic course in computer usage. Provides the skill needed to utilize digital computer languages for problem solving. Includes a study of FORTRAN with a discussion of APL and Assembly Language. Other topics include representation of information, management of files, error control, operational systems and job control. Fall.

APM 510. Statistical Analysis (3)
Two hours of lecture and three hours of laboratory per week. A treatment of statistical inference, including paired design, group design, linear regression and correlation, one-way analysis of vari-ance and some applications of chi-square. Calculation of statistics, test of hypotheses and proper interpretation of calculated statistics. Fall.

APM 595. Probability and Statistics for Engineers (3)
Three hours of lecture per week. Calculus-based probability and statistical theory in engineering and the environmental sciences. Descriptive statistics including visual and numerical data presentation, probability including set theory, conditional probability, independence, and counting techniques, discrete and continuous probability distributions, confidence interval estimation and classical hypothesis testing, probability plots and associated normality and lognormality tests, simple linear regression, and an introduction to ANOVA. Spring.
Pre- or co-requisite(s): Calculus through integral calculus.
Note: Credit will not be granted for both APM 395 and APM 595.

APM 620. Analysis of Variance (3)
Three hours of lecture and recitation, and three hours of laboratory per week. Multi-way classifications in the analysis of variance, with emphasis on the development of models, including randomized blocks, Latin squares, split plots, and factorial designs with fixed effects, random effects and mixed effects; multiple and partial regression and correlation (including curvilinear), using matrix methods; analysis of covariance. Spring.
Prerequisites: Graduate status and an introductory course in statistics covering material through the one-way analysis of variance.

APM 625. Introduction to Sampling Techniques (3)
Two hours of lecture and three hours of laboratory per week. Intro-duction to the scientific basis of sampling: selecting an appropriate sampling unit; choosing an efficient design; calculating sampling error; determining a sample size to meet stated objectives. Fall.
Prerequisite: APM 391.

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.

AMP 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.

APM 650. Operations Research (3)
Three hours of lecture per week. A survey of optimization techniques to support decision making in the management of natural resources. Techniques examined include linear programming, integer programming, network analysis, nonlinear programming, dynamic programming, and Markov chains. Fall (odd years).
Pre- or co-requisite(s): Calculus and Probability and Statistics.

APM 653. Simulation Design and Analysis (3)
Three hours of lecture per week. Statistical aspects of computer simulation. Topics examined include: identification and para-meterization of probability distributions, evaluation of random number generators, random variate generation, and statistical analysis of simulation output. Fall (even years).
Prerequisite: Probability and Statistics.

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.