Applied Mathematics & Statistics Summer Courses


    Statistics

  • AMS 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, IN, Q.). 

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    Instructor: Yonatan Katznelson


  • Statistics

  • AMS 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, IN, Q.) 

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    Instructor: Bruno Mendes


  • Statistical Methods for the Biological, Environmental, & Health Sciences

  • AMS 7-01

    Session 1

    Prerequisites lifted in summer for all students.

    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. Concurrent enrollment in course 7L is required. (General Education Code(s): SR, IN, Q.) 

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    Instructor: Matthew Heiner


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

  • AMS 7L-01

    Session 1

    Prerequisites lifted in summer for all students.

    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. Concurrent enrollment in course 7 is required. 

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    Instructor: Daniel Kirsner


  • Statistical Methods for the Biological, Environmental, & Health Sciences

  • AMS 7-02

    Session 2

    Prerequisites lifted in summer for all students.

    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. Concurrent enrollment in course 7L is required. (General Education Code(s): SR, IN, Q.) 

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    Instructor: Barata Raquel


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

  • AMS 7L-02

    Session 2

    Prerequisites lifted in summer for all students.

    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. Concurrent enrollment in course 7 is required. 

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    Instructor: Daniel Kirsner


  • Introduction to Probability Theory

  • AMS 131-01

    Session 1

    Prerequisites lifted in summer for all students.

    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. (General Education Code(s): SR, Q.) 

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    Instructor: David Draper


  • Introduction to Probability Theory

  • AMS 131-02

    Session 2

    Prerequisites lifted in summer for all students.

    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. (General Education Code(s): SR, Q.) 

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    Instructor: Yonatan Katznelson