Classroom: UW1-010

Class Time: Monday/Wednesday 3:30-5:35

Instructor: Devon Brewer, Ph.D.

Course Description

This course provides a consumer- and user-oriented introduction to statistics for students in the liberal arts. The primary emphasis is on descriptive, rather than inferential, statistics. In addition, the course stresses statistical reasoning over mechanical calculation. The course highlights many of the common statistics used in the health, natural, and social sciences, and equips students with the essential skills for understanding and evaluating statistical issues encountered in daily life (such as those reported in the media). Although this course is designed to satisfy the needs of students who do not plan to take another course in statistics, it also serves as a solid foundation for further study in the area. Classes involve lecture, discussion, and computer exercises. The only prerequisite is an understanding of basic algebra.
 

Course Objectives

This course has 3 main objectives. Students will:

• gain a basic understanding of common statistical methods
• learn how to interpret and critique statistics encountered in daily life and apply statistical reasoning to real-world problems
• become familiar with statistical software for data analysis

Textbooks, readings, and software

The following book is required for the course and is available at the UWB bookstore:

Utts, Jessica M. (1999). Seeing through Stastistics, 2^nd ed. Pacific Grove, CA: Duxbury Press.

For this course you will need to have regular access to a computer that can access the Internet and has a Java-enabled browser. The statistical software we will be using runs on the Web. The two main sites we'll be using are:

WebStat 2.0 (http://www.stat.sc.edu/webstat/version2.0/)

SDA: Survey Documentation and Analysis (http://csa.berkeley.edu:7502/)

The free programs at these web sites will allow you to do all the data analysis required for homework assignments and class projects. These sites also have many interesting data sets available for analysis (as well as the capability to import or enter other data), and are easier to use than most standard statistical software packages.

If you do not already have access to the Internet on your home computer, you may download the UW Internet Connectivity Kit (UWICK) from http://www.washington.edu/computing/software/uwick/ or buy it at the bookstore for $19. This software will allow you to have Internet access. There are also free Internet service providers that offer access for free (e.g., freeinternet.com at http://www.freei.net/).

Requirements and Grading

Attendance, notes, and reading assignments

Attendance is not mandatory. However, you are responsible for all material presented in class and handing in assignments on time. This means that if you miss class, you must get copies of notes from a classmate--I will not provide notes for anyone. I will make copies of my overheads available on the web at http://faculty.washington.edu/ddbrewer/bls315/. Most handouts and homework assignments will not be distributed in class; rather, they will be posted on this web page. Therefore, you will need to check this web page every week or more often to get all the materials you need for this course. If you need handouts that are actually given in a previous meeting of the class that you missed, you must make copies from a classmate or get them from me during office hours.

Make sure to read the material assigned for a particular day before coming to that class session.

Homework assignments

Homework assignments will consist of selected exercises at the end of assigned chapters, web-based data analysis exercises, and additional questions/problems.  There are a total of 9 homework assignments, due at the beginning of class on the specified date.  I will not accept any late homework assignments, but you may miss one homework assignment and still receive full course credit for homework (to compute your total homework grade, I will take your 8 highest homework scores).

Exams and paper

Exams consist of multiple choice, true-false, fill-in-the-blank, and short answer questions.

Grading components

Grades are based on the following components and weights:

quiz 15%

homework assignments 25%

midterm exam 30%

final exam 30%
 

4.0 = 97-100%	3.3 = 88%	2.6 = 81%	1.9 = 74%	1.2 = 67%
3.9 = 95-96%	3.2 = 87%	2.5 = 80%	1.8 = 73%	1.1 = 66%
3.8 = 93-94%	3.1 = 86%	2.4 = 79%	1.7 = 72%	1.0 = 65%
3.7 = 92%	3.0 = 85%	2.3 = 78%	1.6 = 71%	0.9 = 64%
3.6 = 91%	2.9 = 84%	2.2 = 77%	1.5 = 70%	0.8 = 62-63%
3.5 = 90%	2.8 = 83%	2.1 = 76%	1.4 = 69%	0.7 = 60-61%
3.4 = 89%	2.7 = 82%	2.0 = 75%	1.3 = 68%	0.0 < 60%

Extra credit

Students can earn up to 12% additional course credit by completing optional extra assignments. One extra assignment, worth up to 10%, involves designing and executing a small data analysis project.  In addition, students may turn in up to 2 brief reports (worth 1% each) that describe and critique examples of the inappropriate use of statistics in the media or some other daily life context. Details on all of these extra credit assignments will be available on the course web page.
 

Policies

• Come visit me during office hours if you have any questions about the requirements, assignments, exams, or any other aspect of the course. I cannot help you if I am not aware of your particular circumstances, concerns, or needs. I want you to succeed in this course and will do what I can to help make that happen.

• Cheating and plagiarism (passing off someone else’s writing as your own) each will result in severe penalties, including zero credit for the particular assignment or examination. Other penalties may be imposed by the UWB administration. Refer to http://faculty.washington.edu/davidgs/Integrity.html and your University Handbook for the University’s standards for academic honesty and integrity.

• Make-up exams will be given only to those who make special arrangements in advance or who have medical excuses or personal catastrophes that are documented in writing.

• If you disagree with the score I have given to your answer on an exam question or an assignment and you wish to challenge it, you may submit an appeal to me. The written appeal should be a paragraph that explains why you think you are correct and cites supporting materials presented in class or the readings.

• Late papers will be penalized.

• Please make an appointment with me as soon as possible if you need accommodations for or adaptations to the course because of a disability, if you have emergency medical information to share with me, or if you need special arrangements in case the building must be evacuated. If you have any questions about the services available for special needs at UWB, please call Disabled Student Services at 425-352-5307 (TDD: 425-352-5303) or visit http://www.bothell.washington.edu/students/dss/index.html.

• You are responsible for all deadlines and other information in this syllabus and any changes that might be announced in class.

• Read and follow directions carefully. Attend class, review your notes regularly, ask questions, and do the chapter exercises not assigned for homework (especially those with answers in the back of the book). Plan and work ahead.

• Keep a copy of all assignments you submit and the graded work returned to you.

• Professor David Goldstein-Shirley here at UWB has some good tips for finding success and fulfillment at UWB with which I agree entirely. Visit http://faculty.washington.edu/davidgs/Diamonds.html for his advice.

Monday, September 25: Introduction to course, Purposes of statistics, Research process

Wednesday, September 27: Research design & methods, Read: ch. 1 & 2

Monday, October 2: Research design & methods (cont.), Scales of measurement, Read: ch. 3-5

Wednesday, October 4: Homework #1 due, Frequency distributions, Displaying univariate data, Measures of central tendency, Read: ch. 6 & 9, pp. 108-112

Monday, October 9: Homework #2 due, Distribution shapes, Measures of dispersion, Read: Ch. 7

Wednesday, October 11:  Quiz, Normal distribution, Read: Ch. 8

Monday, October 16: Homework #3 due, Bivariate relationships: categorical data, Read: ch. 12

Wednesday, October 18: Bivariate relationships: categorical data (cont.)

Monday, October 23: Homework #4 due, Scatterplots, Regression, Read: ch. 10 & 11

Wednesday, October 25: Correlation

Monday, October 30: Homework #5 due, Review of descriptive data analysis

Wednesday, November 1: Midterm Exam

Monday, November 6: Time series, Probability, Read: ch. 14 & 15

Wednesday, November 8: Homework #6 due, Probability, Read: ch. 16 & 17

Monday, November 13: Homework #7 due, Sampling distributions, Read: ch. 18

Wednesday, November 15: Estimation, Read: ch. 19

Monday, November 20: Estimation, Read: ch. 20

Wednesday, November 22: Homework #8 due, Significance testing, Read: ch. 21 & 22

Monday, November 27: Significance testing, Read: ch. 23

Wednesday, November 29: Homework #9 due, Meta-analysis, Read: ch. 24

Monday, December 4: Multivariate analysis

Wednesday, December 6: Review