First-Year Courses in Statistics (Division 489)

Winter Term, 2000 (September 8 - December 22, 1999)

Section – There Will Be One (1) Midterm Exam On Thursday, February 24, 6-8 pm

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100woo/

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. Alternative approaches to statistical inference are also discussed. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, and teaching fellow input. The course format includes three lectures and a laboratory (1 hour per week).

Section 002 – There Will Be One (1) Midterm Exam On Thursday, February 24, 6-8 pm

Instructor(s): Martha Aliaga (aliaga@umich.edu)

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100W00/

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

Section 003 – There Will Be One (1) Midterm Exam On Thursday, February 24, 6-8 pm

Instructor(s): Brenda Gunderson (bkg@umich.edu)

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100W00/

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

Section 001 – There Will Be Three (3) Monday Evening Mid-Term Examinations For Statistics 170.

Instructor(s): Ed Rothman (erothman@umich.edu)

Credits: (4).

Course Homepage: No Homepage Submitted.

The objective of this course is to introduce students to the learning process in a non-deterministic environment. An appreciation for measurement, bias, and variation is essential to formulate questions and learn about things. Underlying this course is the Edwards Deming philosophy. Deming, an American statistician, was invited to Japan in the early 1950's to help improve the quality of mass produced items. His success in Japan is, in part, responsible for our current balance of trade deficit; and here the Ford Motor Co. has also attained a larger market share as a result of his ideas. Implementation of the Deming message requires a critical appreciation of variation and the scientific method. Specifically, we will discuss:

1. Historical attempts to learn and the advent of the modern scientific method.
2. The differences between special or assignable causes and common causes of variation. Before we can learn how a process operates, the process must be stable.
3. Differences between observational and controlled randomized studies and associated ethical issues.
4. The 'what' and 'how' of measurement and the quantification of uncertainty-subjective and frequency notions of probability.
5. Understanding bias and variation.
6. How to use bias to design efficient studies.
7. Differences between enumerative and analytic studies.

Many of the ideas will be introduced through experimentation (e.g., the red bead and funnel experiments), and the mathematical level will not require more than a modest background in high school algebra. The course format includes three lectures and a laboratory (1.5 hours per week).