E. Example of a course syllabus

The syllabus below comes from a course where the instructor(s) have made a conscious effort to reflect the guidelines including cut some previously covered topics, go into more depth on key concepts, focus on collecting and producing data, integrate technology, alternative methods of assessment, real data sets and class activities.

STAT 1000 Fall 2004

Basic and Applied Statistics Syllabus

Intended Audience: Undergraduate students who have not studied statistics, and who have had a high school algebra course.

Course Goals/Objectives:

Students will learn the basics of descriptive and inferential statistics, so that they may develop statistical literacy and reasoning, and be able to carry out statistical investigations. By the end of this course students should be able to:

· Explain the “big picture” of statistical investigations.

· Understand the core statistical ideas

· Understand and critique articles and news stories that use statistics

· Experience and understand process of statistical investigations by “doing” statistics

· Understand why statistics cannot prove conclusions but can suggest them.

· Value what statistics can do for us, and not just think that statistics can lie.

Course Materials:

Textbook: Statistics in Action, Ann Watkins, Richard L. Scheaffer, and George W. Cobb, bundled with Fathom Dynamic Statistics™ CD. Additional materials will be available for printing and bringing to class via WebCT.

Instructional format:

This is NOT a class where you come each day, listen, watch, and take notes! The primary method for learning new statistical concepts and methods will be by reading the textbook, working out problems from the textbook, and participating in class activities, discussions, and demonstrations. Many of these activities will include using Fathom, state-of-the-art statistical software designed to help students learn statistics and eliminate much of the “math” and number crunching, so they can focus on what statistics really mean and how we use them.

Small group and large group activities will be used to apply and deepen students’ understanding. Real data sets will be used during each class to help students develop statistical thinking and learn how to analyze and interpret data.

It is essential that students attend class each day and if they have to miss a class, should make every attempt to make up the work by obtaining notes from students and copies of the materials from WebCT. However it is almost impossible to learn as much from an activity that was carried out and discussed in class. Also, some form of assessment will be used in each class period: a pop quiz, minute paper, or short task.

Course requirements:

Attend class each day and participate in small group activities and large group discussions. Read about 20 pp each week in the text book. Write out solutions to assigned problems in the text and bring to class. Complete assessments as listed below.

Assessments:

Assessments can be expected every day, some scheduled and some unscheduled.

· 30%: written research project report. Students are expected to demonstrate their learning by completing a research project and turning in a written paper. Project milestones will provide pacing and feedback in completing a high quality project

· 30% Three in-class tests on the following topics (see attached policies concerning missing a test)

§ Test # 1: Design of experiments

§ Test # 2: Descriptive statistics and the normal distribution

§ Test # 3: Analyzing bivariate relationships

· 20% Final practical exam: students are provided a data set and software to answer a set of statistical questions.

· 10%: Critiques:

§ One graph critique

§ One critique of a statistical article

· 10% In class assessments which will include:

§ Homework related quizzes to encourage attendance and completion of homework problems

§ Minute papers to discuss course concepts and provide a vehicle for informal communication

§ A take home task that completes an in-class activity

§ A meaningful paragraph or similar writing task

Grading:

Percentage Cutoff	Grade	Percentage Cutoff	Grade	Percentage Cutoff	Grade
92.5%	A	80.5%	B-	59.5%	D
89.5%	A-	76.5%	C+	Below 59.5%	F
86.5%	B+	72.5%	C
82.5%	B	69.5%	C-

Stat 1000 Course Outline: Basic and Applied Statistics

Session	Date	Topic	Lesson Goals / Objectives	Read assigned sections / pages	HW to complete before class; Assignment due dates
1	9/7	Overview/Introduction to the course	Introduce ourselves, set the stage for the course		· Install Fathom on “home computer” · Complete “Walkthrough Guide,” pp 1-10
2	9/9	A Case Study in: Data Exploration Introduction to Fathom™ and inference	Introduce role of context, explore data, introduce the logic of inference, and use simulation for inference	Read Sections 1.1 and 1.2	· 1.1: D2., D7, P1, E1, E2 · 1.2: Activity 1.1, D12, D13
3	9/14	Why take samples and how not to	· Learn the basic vocabulary of sampling and surveys · Learn reasons for using samples · Recognize common instances of selection and response bias	Read Section 4.1	· 4.1: E1, E7, E12
4	9/16	Randomizing: Playing it safe by taking chances	Learn and understand: · Why we rely on chance to pick a sample · Definition of a SRS · How to recognize and implement probability samples including: stratified, cluster, multistage, systematic	Read Section 4.2:	· 4.2: P9, E14, E18, E19
5	9/21	Experiments and Inference about Cause	Learn: · Characteristics of well defined experiment · Difference between an experiment and observational study · Instances of confounding · Randomizing treatments protects against confounding · Build the underpinnings of inference	Read Section 4.3	· 4.3: E21, E23, E25, E26 · Project Milestone: Introduction—Pose two research questions

Session			Date	Topic	Lesson Goals / Objectives	Read assigned sections / pages	HW to complete before class; Assignment due dates
6			9/23	Designing experiments to reduce variability	· Cause and effect requires randomized experiment · Distinguish variability within vs. between treatments · Understand why one reduces variability within treatments · Differentiate randomized, matched pairs, and randomized block designs · Learn advantages and disadvantages of each type of design and when it is appropriate to use them in practice	Read Section 4.4	· 4.4: P28, E27, E30, E36, E48
7			9/28	Test #1: Design of Experiments
8			9/30	Exploring distributions and graphical displays for distributions	Describe (univariate) data as a distribution, Recognize and interpret graphs (dot plot, stemplot, histogram, bar graph)	Read Section 2.1; Read 2.2 (exclude tennis ball activity)	· 2.1: P4, P5, E1, E2, E10 · 2.2: P8, D11, E16, E19
9			10/5	Measures of center	Understand and interpret mean, median, and mode and influence of outliers on these measures	Read 2.3 Part 1: pp 53-56	· 2.3: D18, D19a, P18, P19, P21 · Project Milestone: Methods—Data Sample & Organization
10			10/7	Measures of spread: range and IQR, boxplots	Understand the concept of variability and spread. Understand IQR and Range. Interpret boxplots. Comparing descriptions and graphs	Read 2.3 Part 2: pp 57-64	· 2.3: P22, P24, P25, P26.
11			10/12	Measures of spread: standard deviation	Understand and use Standard deviation as a measure of spread	Read 2.3: pp 64-72, 74	· 2.3: D26, D27, P28, P29
12			10/14	The normal distribution	Employ the normal (and standard normal) distribution as a model; use standard deviations and z-scores to measure variation from the mean	Read Section 2.4	· 2.4: E46, E49, E53, E74
Session			Date	Topic	Lesson Goals / Objectives	Read assigned sections / pages	HW to complete before class; Assignment due dates
13			10/19	Reasoning about variability	Integrate measures of variability (i.e., spread): IQR, standard deviation, range	Reread all of chapter 2	· Ch. 2: E23, E24, E31, E32 · Matching graphs to statistics
14			10/21	Test #2: Descriptive statistics and the Normal distribution			· Project Milestone: Descriptive Statistics
15			10/26	Scatterplots	Understand bivariate relationships · Understand nature of bivariate data · Describe shape (form), trend (direction), and variation (strength) in a scatterplot and interpret in context of the data · Use Fathomä to create a scatterplot · Answer contextual questions using information from a scatterplot · Know scatterplots are appropriate graphs to answer questions about the relationship between two quantitative variables · Understand how lurking variables affect the relationship between two variables	Read Section 3.1	· 3.1: P2, E2, E4, E5, E7
16			10/28	Getting a line on a pattern	· Use movable line to predict y given x	Read Section 3.2	· 3.2: P5, E11, E16, E22 · Graph Critique due
17		11/2		Correlation: Strength of a Linear Trend	· Estimate correlation from a scatterplot · Understand correlation should not be computed from nonlinear data · Understand a high correlation does not imply that the data are linear · Be aware of lurking variables and correlation does not imply causation	Read Section 3.3	· 3.3: P10, E33, E37 · Mid-term feedback
18		11/4		Bivariate Wrap Up
19		11/9		Test #3: Analyzing Bivariate relationships			· Project Milestone: Bivariate Analysis
Session		Date		Topic	Lesson Goals / Objectives	Read assigned sections / pages	HW to complete before class; Assignment due dates
20		11/11		Sampling from a population	· Understand basic Ideas of Sampling; sampling proportions	Read 5.1: pp 268-270	· All use the random number table
21	11/16			Generating Sampling Distributions	· Be able to Generate sampling distributions for a variety of statistics, observe the predictable pattern, contrast sample distributions with sampling distributions · Use the simulation process model to explain sampling distributions	Read Section 5.2	· 5.2: E7, E9, E11, E15
22	11/18			Sampling distribution of sample mean	· Use simulations to illustrate and explain the Central Limit Theorem, apply the CLT to different contexts	Read Section 5.3	· 5.3: E16, E20, E22, E24, E25
23	11/23			Probability using Simulation and experiments	Using simulations and experiments for inference: · Use experiments to predict probabilities · Conduct simulation of the experiment · Sketch a simulation process model	Read Section 6.1: pp 327-337	· 6.1: P4, P5, E2, E4
24	11/25			Thanksgiving Holiday
25	11/30			Toward a confidence interval (CI) for the mean	· Review simulation model activity · Find a CI from a sample from fixed populations · Understand CI as reasonably likely sample means · Interpret a CI for a mean, understand confidence level · Understand relationship between capture rate and confidence level	Read Section 9.1 (focus on concepts, not formulas)	· 9.1: P1, E3, E5, E7

Session	Date	Topic	Lesson Goals / Objectives	Read assigned sections / pages	HW to complete before class; Assignment due dates
26	12/2	Toward a significance test for the mean (1-sample test)	· Understand logic of significance test · Identify and perform four steps in a significance test for a mean. · Understand 1-sample test terms: Statistical significance, Null hypothesis, Alternative hypothesis · Interpret a P-value · Compare a confidence interval to a two-tailed hypothesis test.	Read Section 9.2	· Against all Odds #20: significance tests · 9.2: E11, E12, E13, E14
27	12/7	When you estimate sigma: t distribution	· Differentiate the true standard error (SE) vs. estimated SE · Check conditions · Significance tests for the mean · Differentiating P-value tests versus fixed level tests	Read Section 9.3	· 9.3: E15, E17, E18
28	12/9	Inference for the difference of 2 means (2-sample test)	· Understand CI and test of significance to compare two means · Construct CI for mean difference · Perform 4 steps in significance test for mean difference · Deepen understanding of comparing means in terms of: CI, capture rate, statistical significance, P-value, and one-tailed test and two-tailed test	Read Section 9.5	· 9.5: E26, E29, E32 · Article Critique due
29	12/14	Review and wrap up on inference			· Milestone: Inferential Statistics
Final Project	12/17	Final Project is due no later than 12:30pm Friday, 12/17/04 Deliver in hardcopy to 325 Peik Hall or 330 Burton Hall.			· Final paper with summary and conclusions.
Final Exam	12/22	Final Practical Exam, 325 Peik Hall: 1:30 – 3:30pm

More on Grading

· Tests: Tests will consist of a variety of questions (multiple-choice, open-ended, etc.) designed to test your ability to apply the knowledge you gain by working on homework problems and participating in class activities and discussion. You may always use your calculator, Fathom, and your note-card during the free-response section of the tests. However, only a pen or pencil will be permissible during the multiple-choice section.

· Making-up a test: In general tests are not to be made up. Exceptions may be granted in cases of illness or emergency. If you cannot be in class on the day of the test, it is your responsibility to notify me before the test. If a make-up is granted it will be at the discretion of the instructor. If you fail to make-up the test at the scheduled time, you will not be able to make it up at all. If you cannot notify me before missing a test, you must provide documentation explaining your absence for the instructor to determine if an exception should be granted.

· Homework assignments

As a student of statistics, working through homework problems is an important piece in building a complete understanding of the concepts, as well as allowing you to practice doing statistics. Only by trying to apply the concepts can you be sure that you really understand them. Homework assignments should be regarded as a genuine “learning experience.” We urge you to form study groups to work on these problems and master the concepts. You should, however, be sure that the effort is truly collaborative. The best strategy for completing the assignment is to begin tackling the questions alone, then discussing with others, and finally writing up your answers by yourself. Feel free to consult the teaching assistant and instructors when you are stuck – but try not to ask for more help than you need to get started.

Homework will be assigned but not collected or graded.  Many of the solutions to the homework problems are given in the back of the textbook.  In addition, instructors and the teaching assistant will have a copy of the solutions manual available in their office.  Homework assignments can also be emailed to the teaching assistant for a brief perusal to make sure you are on the right track; however thorough explanations and help will not be provided via email. It will be your responsibility to work through the assigned problems and get help on those you do not understand.  Some of the exam and quiz questions will be very similar if not identical to homework problems.

Return to GAISE Report Appendix Contents