M316 Syllabus


Prerequisite and degree relevance: An appropriate score on the mathematics placement exam.. M 316 is an elementary introduction to statistical methods for data analysis; knowledge of calculus is not assumed. Students with a background in calculus are advised to take M 362K plus either M 358K or M 378K instead.  This course may not be counted toward the major requirement for the Bachelor of Arts with a major in mathematics or toward the Bachelor of Science in Mathematics. Students taking the course should have good basic algebra skills. 

This course carries the Quantitative Reasoning flag.  QR courses are designed to equip you with skils that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life.  You should, therefore, expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.

Text: StatsPortal; The Basic Practice of Statistics, 6th edition(2010) by David S. Moore

StatsPortal contains an interactive e-Book and numerous resources for students and instructors.  For students: Learning Curve, statistical videos, Stats Tutor, applets, software manuals, online quizzes, etc.  Resources for instructors include the e-Book, Power Point lecture slides, instructor's solution manual, printed test bank, i>clicker questions, grade book (which can be downloaded to Blackboard), extra exercises and solutions, etc.

To purchase StatsPortal and register your access code, go to http://courses.bfwpub.com/bps6e.php

Students can use the loose leaf version of the textbook packaged with StatsPortal for a nominal extra charge; the ISBN is 978-1-4641-2954-4.  You can ask the Coop to order copies for you.

You may go to www.whfreeman.com/bps6e to browse some of the resources mentioned above.

Responsible Party: Evelyn Schultz, June 2012


Part I: Exploring Data

  •  Chapter 1 Picturing Distributions with Graphs
  • Chapter 2 Describing Distributions with Numbers
  • Chapter 3 The Normal Distributions
  • Chapter 4 Scatterplots and Correlation
  • Chapter 5 Regression
  • Chapter 6 Two-Way Tables (optional)
  • Chapter 7 Exploring Data: Part I Review (May be assigned as reading.)  

Part II: From Exploration to Inference

  • Chapter 8 Producing Data: Sampling
  • Chapter 9 Producing Data: Experiments
    (Optional but strongly recommended: Commentary, Data Ethics. May be assigned as reading.)
  • Chapter 10 Introducing Probability (Section on Personal Probability is optional.)
  • Chapter 11 Sampling Distributions
  • (Optional: Chapter 12 General Rules of Probability)
  • (Optional: Chapter 13 Binomial Distributions)
  • Chapter 14 Confidence Intervals: The Basics
  • Chapter 15 Tests of Significance: The Basics
  • Chapter 16 Inference in Practice (more focus on Power and less on Type II error)
  • Chapter 17 From Exploration to Inference: Part II Review (May be assigned as reading.)

Part III: Inference about Variables

  • Chapter 18 Inference about a Population Mean
  • Chapter 19 Two-Sample Problems (The section on details of the t approximation is optional, and so are the sections on avoiding the pooled two-sample t procedures and avoiding inference about standard deviations.)
  • Chapter 20 Inference about a Population Proportion
  • Chapter 21 Comparing Two Proportions
  • Chapter 22 Inference about Variables: Part III Review (May be assigned as reading)

Part IV: Inference about Variables

  • Chapter 23 Two Categorical Variables: The Chi-Square Test (Section on goodness of fit optional.)
  • (Optional: Chapter 24 Inference for Regression)
  • (Optional: Chapter 25 One-Way analysis of variance: comparing several means)
  • (Optional: Chapter 26 Non-parametric Tests)
  • (Optional: Chapter 27 Statistical Process Control)
  • (Optional: Chapter 28 Multiple regression)


Comments for Instructors:
If you choose to cover any of the optional chapters, save them (with the possible exception of the Commentary on Data Ethics) until the end of the semester. Don't try to do more than two of them.  The Commentary on Data Ethics is recommended, with chapter 24 second priority. Note that chapters 12 and 13 are not needed for the rest of the course, with the exception of conditional probability.

The book is readable enough that, especially in chapters 1 – 9, you may want to cover some topics as reading assignments, to be followed by class discussion, rather than lecturing.

The material on inference (beginning with chapter 14) is more challenging for most students than the earlier chapters. To allow adequate time for the material on inference, chapter 14 should be started just before or at the midpoint of the semester.

Some instructors require students to do a (usually group) project involving designing an experiment or observational study, carrying it out, and analyzing the results.

Chapters 20 and 21: The sections on more accurate confidence intervals should be covered, reflecting currently recommended changes in statistical practice.

Statistical applets. These can be used for in-class demonstrations of concepts if your classroom is equipped for computer projection. They are also available as a resource on StatsPortal.

Access to the website StatsPortal is bundled with new copies of the textbook.