Introduces the tools of statistical analysis. Combines theory with extensive data collection and computer-assisted laboratory work. Develops an attitude of mind accepting uncertainty and variability as part of problem analysis and decision-making. Topics include: exploratory data analysis and data transformation, hypothesis-testing and the analysis of variance, simple and multiple regression with residual and influence analyses.
| Day | Start Time | End Time | Room |
|---|---|---|---|
Tuesday | 15:20 | 16:40 | PL-4 |
Friday | 15:20 | 16:40 | PL-4 |
Wednesday | 15:20 | 16:40 | PL-2 |
Functions Modeling Change provides the algebraic and geometric skills needed to succeed in a Calculus course. The central topic is functions (in particular linear, polynomial, exponential and logarithmic), function notation and graphs, transformations, composition and inverses. Students also work with computers building mathematical models based on these functions, and implemented using graphing calculators, mathematical software and Excel.
Note: this course will first be offered in fall 2015
| Day | Start Time | End Time | Room |
|---|---|---|---|
Tuesday | 09:00 | 10:20 | PL-1 |
Wednesday | 09:00 | 10:20 | PL-1 |
Friday | 09:00 | 10:20 | PL-1 |
Introduces differential and integral calculus. Develops the concepts of calculus as applied to polynomials, logarithmic, and exponential functions. Topics include: limits, derivatives, techniques of differentiation, applications to extrema and graphing; the definite integral; the fundamental theorem of calculus, applications; logarithmic and exponential functions, growth and decay; partial derivatives. Appropriate for students in the biological, management, computer and social sciences.
| Day | Start Time | End Time | Room |
|---|---|---|---|
Monday | 10:35 | 11:55 | PL-1 |
Wednesday | 10:35 | 11:55 | PL-1 |
Thursday | 10:35 | 11:55 | PL-1 |
Introduces differential and integral calculus. Develops the concepts of calculus as applied to polynomials, logarithmic, and exponential functions. Topics include: limits, derivatives, techniques of differentiation, applications to extrema and graphing; the definite integral; the fundamental theorem of calculus, applications; logarithmic and exponential functions, growth and decay; partial derivatives. Appropriate for students in the biological, management, computer and social sciences.
| Day | Start Time | End Time | Room |
|---|---|---|---|
Monday | 15:20 | 16:40 | PL-4 |
Thursday | 15:20 | 16:40 | PL-4 |
Wednesday | 13:45 | 16:40 | PL-4 |
Treats applications in economics and computer science, limited to Euclidean n-space. Topics include: the linear structure of space, vectors, norms and angles, transformations of space, systems of linear equations and their applications, the Gauss-Jordan method, matrices, determinants, eigenvalues and eigenvectors. Uses Mathematica for graphics and algorithms.
| Day | Start Time | End Time | Room |
|---|---|---|---|
Tuesday | 13:45 | 15:05 | PL-1 |
Friday | 13:45 | 15:05 | PL-1 |
| Day | Start Time | End Time | Room |
|---|---|---|---|
Tuesday | 15:20 | 16:40 | C-102 |
Friday | 15:20 | 16:40 | C-102 |
Examines examples from the physical sciences to illustrate the introduced concepts. Topics include: trigonometric and hyperbolic functions; polar coordinates, parametric curves and conic sections; vectors, curves and surfaces in space; vector fields, line integrals, theorems of Green and Stokes.
| Day | Start Time | End Time | Room |
|---|---|---|---|
Monday | 13:45 | 15:05 | PL-4 |
Thursday | 13:45 | 15:05 | PL-4 |
This course is designed to extend the statistical analysis of environmental and social science data: it will highlight the building blocks of multivariate analysis from the definition of the research problem to the interpretation of the results. Both dependence methods (that is in which one or several variables can be expressed in terms of the others – for instance Multivariate Analysis of Variance or Discriminant Analysis) and interdependence methods (where all the variables are analysed simultaneously – for instance Factor & Cluster Analyses or Multidimensional Scaling) will be studied.
Significant applications will be analysed and discussed so as to develop new insights.
Projects (individual or with peers), will allow the students to apply the multivariate models, thereby enhancing the importance of work and knowledge sharing.
Statistical software package: SPSS.
Prerequisite: MA 1020
| Day | Start Time | End Time | Room |
|---|---|---|---|
Monday | 12:10 | 13:30 | PL-2 |
Thursday | 12:10 | 13:30 | PL-2 |
A Senior Project is an independent study representing a Major Capstone Project that needs to be registered using the Senior Project registration form.
(Download: https://www.aup.edu/sites/default/files/download/Academics/academic-reso...)