Applied Mathematics & Statistics Summer Courses


    Statistics (5 credits)

  • STAT 05-01

    Session 1

    Introduction to statistical methods/reasoning, including descriptive methods, data-gathering (experimental design and sample surveys), probability, interval estimation, significance tests, one- and two-sample problems, categorical data analysis, correlation and regression. Emphasis on applications to the natural and social sciences. Students cannot receive credit for this course if they have already received credit for course 7. (General Education Code(s): SR.)

    Proposed Instructor: Daniel Kirsner

    See in Schedule of Classes


  • Statistics (5 credits)

  • STAT 05-02

    Session 2

    Introduction to statistical methods/reasoning, including descriptive methods, data-gathering (experimental design and sample surveys), probability, interval estimation, significance tests, one- and two-sample problems, categorical data analysis, correlation and regression. Emphasis on applications to the natural and social sciences. Students cannot receive credit for this course if they have already received credit for course 7. (General Education Code(s): SR.)

    Proposed Instructor: Bruno Mendes

    See in Schedule of Classes 


  • Statistical Methods for the Biological, Environmental, & Health Sciences (5 credits)

  • STAT 7-01

    Session 1

    Case-study-based introduction to statistical methods as practiced in the biological, environmental, and health sciences. Descriptive methods, experimental design, probability, interval estimation, hypothesis testing, one- and two-sample problems, power and sample size calculations, simple correlation and simple linear regression, one-way analysis of variance, categorical data analysis. Prerequisite(s): score of 300 or higher on the mathematics placement examination (MPE), or course 2 or 3 or 6 or 11A or 15A or Mathematics 3 or 11A or 19A. Concurrent enrollment in course 7L is required. (General Education Code(s): SR.) 

    Visiting Students - prerequisites are lifted in summer.

    Proposed Instructor: Daniel Spencer

    See in Schedule of Classes 


  • Statistical Methods for the Biological, Environmental, and Health Sciences Laboratory (2 credits)

  • STAT 7L-01

    Session 1

    Computer-based laboratory course in which students gain hands-on experience in analysis of data sets arising from statistical problem-solving in the biological, environmental, and health sciences. Descriptive methods, interval estimation, hypothesis testing, one-and two-sample problems, correlation and regression, one-way analysis of variance, categorical data analysis. Prerequisite(s): score of 300 or higher on the mathematics placement examination (MPE), course 2 or 3 or 6 or 11A or 15A or Mathematics 3 or 11A or 19A. Concurrent enrollment in course 7 is required. 

    Visiting Students - prerequisites are lifted in summer.

    Proposed Instructor: Mattew Heiner

    See in Schedule of Classes 


  • Statistical Methods for the Biological, Environmental, & Health Sciences (5 credits)

  • STAT 7-02

    Session 2

    Case-study-based introduction to statistical methods as practiced in the biological, environmental, and health sciences. Descriptive methods, experimental design, probability, interval estimation, hypothesis testing, one- and two-sample problems, power and sample size calculations, simple correlation and simple linear regression, one-way analysis of variance, categorical data analysis. Prerequisite(s): score of 300 or higher on the mathematics placement examination (MPE), or course 2 or 3 or 6 or 11A or 15A or Mathematics 3 or 11A or 19A. Concurrent enrollment in course 7L is required. (General Education Code(s): SR.) 

    Visiting Students - prerequisites are lifted in summer.

    Proposed Instructor: Raquel Barata

    See in Schedule of Classes 


  • Statistical Methods for the Biological, Environmental, and Health Sciences Laboratory (2 credits)

  • STAT 7L-02

    Session 2

    Computer-based laboratory course in which students gain hands-on experience in analysis of data sets arising from statistical problem-solving in the biological, environmental, and health sciences. Descriptive methods, interval estimation, hypothesis testing, one-and two-sample problems, correlation and regression, one-way analysis of variance, categorical data analysis. Prerequisite(s): score of 300 or higher on the mathematics placement examination (MPE), course 2 or 3 or 6 or 11A or 15A or Mathematics 3 or 11A or 19A. Concurrent enrollment in course 7 is required.

    Visiting Students - prerequisites are lifted in summer.

    Proposed Instructor: Hyotae Kim

    See in Schedule of Classes


  • Mathematical Methods for Engineers I (5 credits)

  • AM 10

    Session 1

    Applications-oriented course on complex numbers and linear algebra integrating Matlab as a computational support tool. Introduction to complex algebra. Vectors, bases and transformations, matrix algebra, solutions of linear systems, inverses and determinants, eigenvalues and eigenvectors, and geometric transformations. Students cannot receive credit for this course and for courses 10A or Mathematics 21. Prerequisite(s): score of 400 or higher on the mathematics placement examination (MPE) or Mathematics 3. (General Education Code(s): MF.)

    Visiting Students - prerequisites are lifted in summer.

    Proposed Instructor: Skylar Trigueiro

    See in Schedule of Classes


  • Mathematical Methods for Engineers II (5 credits)

  • AM 20

    Session 2

    Applications-oriented class on ordinary differential equations (ODEs) and systems of ODEs using Matlab as a computational support tool. Covers linear ODEs and systems of linear ODEs; nonlinear ODEs using substitution and Laplace transforms; phase-plane analysis; introduction to numerical methods. Students cannot receive credit for this course and for courses 20A or Mathematics 24. Prerequisite(s): Mathematics 19B, and course 10 or 10A or Mathematics 21. (General Education Code(s): MF.)

    Proposed Instructor: Catherine Brennan

    See in Schedule of Classes


  • Introduction to Probability Theory (5 credits)

  • STAT 131

    Session 2

    Introduction to probability theory and its applications. Combinatorial analysis, axioms of probability and independence, random variables (discrete and continuous), joint probability distributions, properties of expectation, Central Limit Theorem, Law of Large Numbers, Markov chains. Students cannot receive credit for this course and course 203 and Computer Engineering 107. Prerequisite(s): course 11B or Economics 11B or Mathematics 11B or 19B or 20B. (General Education Code(s): SR.)

    Proposed Instructor: David Draper

    See in Schedule of Classes