STAT 252 Statistical Inference for Management II Winter 2009

Instructor: Allan Rossman
Class Times: M-F 12:10-1:00 (section 10), 1:10-2:00 (section 9), room 02-206 (Statistics Studio Classroom)
Office: Faculty Office Building East 25-102
Phone: 756-2861 (6-2861 on campus)
Email: arossman@calpoly.edu
Office Hours:, M 3-4, Tu 4-5, W 4-5, Th 8-9, F 10-11, and by appointment and by chance
Text: Business Statistics: A Decision-Making Approach, 7^th ed., by Groebner, Shannon, Fry, and Smith

Teaching Assistant: Emily Tietjen

Course Webpage: http://statweb.calpoly.edu/arossman/stat252/

Overview: Statistics can be defined as the science of numerical reasoning from data. Statistics enables the application of the scientific method, for it provides the concepts and techniques needed to collect, analyze, and draw conclusions from data. This course continues your introduction to fundamental ideas and methods of statistical inference and decision-making.

Prerequisite: STAT 251. Specifically, we will make use of what you have learned about descriptive statistics, probability, random variables, binomial distribution, normal distribution, sampling distributions, confidence intervals and hypothesis tests for a population mean, confidence intervals and hypothesis tests for a population proportion.

Goals: By the conclusion of the course, I hope that you have improved your ability to:

• apply and interpret the results of a variety of statistical techniques, including descriptive, inferential, and modeling methods;
• understand many of the fundamental ideas of statistics, such as variability, distribution, association, causation, sampling, experimentation, confidence, and significance;
• analyze and assess statistical arguments, such as those found in the popular press as well as in scholarly publications;
• use statistical software to analyze data;
• communicate your knowledge of statistical ideas effectively.

Course Principles:  The following principles guide my development and teaching of this course:

1. Statistics is not number-crunching. Contrary to its popular perception as a black box collection of arcane magic tricks, statistics involves much more than numerical computations. The emphasis of the course will be on understanding statistical concepts and on interpreting and communicating the results of statistical analyses. In other words, you will be expected to learn to construct and analyze numerical arguments. In contrast to most mathematics courses, we will be using phrases such as “there is strong evidence that ...” and “the data suggest that ...” rather than “the exact answer is ...” and “it is therefore proven that ...” To alleviate the computational burden, we will often use the computer program Minitab to perform calculations and produce graphical displays. You will find that interpreting and explaining are at least as important, possibly more important, than calculating in this course.
2. Statistics involves the analysis of genuine data. Supporting my contention that statistics is applicable in everyday life and in most fields of academic endeavor, you will analyze real data from genuine studies covering a wide variety of applications throughout the course. Some of these data sets involve information that you will collect about yourselves and your peers; others will come from sources such as published scientific studies, official statistics from government agencies, and various web resources. The contexts for these data will span a wide variety of subject matter, including not only business and economics but also fields such as medicine and law, psychology and politics, education and sports.
3. Understanding results from investigation and discovery. Class meetings will be designed for you to actively engage with the material, rather than passively taking notes while I lecture. We will work through activities carefully designed to lead you to discover statistical concepts, explore statistical properties, and apply statistical methods. You will be encouraged to work collaboratively with a partner on some of these activities, while we will work through others as a class. Please come to class expecting to participate, think, and learn.

Course Materials: You should purchase the textbook and a three-ring binder for storing and organizing class outlines/notes.  For most class sessions I will post a handout here; I think you will find it very helpful to print out a copy of these handouts to bring to class. You must also have a scientific calculator and access to the internet and to the statistical software package Minitab outside of class.  Please bring your binder, handouts, and calculator to every class session. You might also find it helpful to bring a USB drive to every class session so you can save your computer work. 

Class Policies: I strongly encourage you to prepare for and to participate in every class session. Not only will this help you to learn the material and perform well in the course, but it will also produce a much more enjoyable learning environment for all of us. Preparing for class will typically involve reading printing out the notes/handout for that, reviewing your notes from the previous session, and perhaps reading relevant section(s) of the text. Participating in class will typically entail contributing to discussions and working on hands-on activities that I design to help you investigate and learn the material. The in-class presentations and activities will supplement the readings from the text but will not attempt to replace them.

I also expect you to devote substantial outside-of-class time to your work for this course. I anticipate that this work will be divided among:

• reading the textbook
• reviewing your class notes
• completing activities not finished in class
• answering take-home quiz questions
• working on optional homework activities
• studying for exams

Use of Computers: We will use computers fairly extensively in this course. One use is for communication: I will post lots of information on the course webpage, and I invite you to ask questions via e-mail. Computers will also prove useful for learning statistical ideas and for conducting statistical analyses.

For these statistical uses, we use the statistical analysis package Minitab and also Java applets. No prior knowledge of these software tools is assumed; you will receive detailed instructions regarding their use when the need arises. Minitab is freely available in the Studio classroom and in most on-campus computer labs. You can download a free copy of Minitab from my.calpoly.edu (see instructions here), but Minitab is not available for Macs. The Java applets can be accessed and run through any web browser.

We will also make occasional use of clickers, which are devices through which you answer questions in-class and get immediate feedback not only on the correct answers but also on other students’ responses. You will be assigned a clicker number and will be asked to take that clicker at the beginning of every class session.

Grading Policies: Your course grade will be determined by the following components, with relative weights as indicated:

• quizzes (10%)
• investigation assignments (10%)
• three midterm exams (45%; 15% each)
• cumulative final exam (25%)

Notice that these weights only add up to 90%. I’ll add another 5% of weight to whichever component is your best and another 5% to whichever component is your second best.

Quizzes: I will encourage and reward you for class preparation and participation by collecting and grading some aspect of your work in most class periods.  In some cases this will be a quiz based on what I present during the class, in others it will be a sample of your work from an in-class activity.  Specific rules for each (individual or partner, open- or closed-book, in-class or take-home) will be announced as we go.  Missed quizzes can not be made up or excused, but you may drop your lowest four quiz scores before calculating your overall score.  These quizzes, and their solutions, will be posted here so that you can check your work afterward.

Investigation Assignments: Investigation assignments build on in-class activities, asking you to investigate a concept or application in more detail. These will be assigned occasionally, roughly an average of one per week. These assignments are often fairly open-ended, requiring both writing and computer work. You may work with one other person on most investigations, submitting one report with both names, provided that both of you genuinely contribute to the work. Word-processed reports of investigations are preferred to hand-written ones, and computer output should be integrated into the report. Investigations are due at the beginning of class on the indicated day, which will be announced in class. Late investigations will not be graded, and missed investigations can not be made up. You may drop your lowest investigation score. A listing of investigation assignments will be maintained here.

I will also assign optional problems from the text, for which answers are provided in the back of the book. I strongly encourage you to work on these problems in order to judge how well you are learning the material and prepare for the kinds of questions that will be on exams. A listing of these optional homework assignments will be maintained here.

The purposes of these assignments are:

• to further your discovery and exploration of course material,
• to give you the problem-solving practice necessary to learn, understand, and apply the concepts and techniques presented,
• to provide you with feedback regarding your understanding of the material, and
• to prepare you for the kinds of questions that will be on the exams.

Exams: There will be three mid-term exams and a final exam. Dates are given in the schedule below. You may make up a missed exam only with a written medical excuse. The final exam will focus on more recent material but will also have a cumulative component. These exams will be open-book and open-notes. You will be provided with preparation advice before each exam. One thing to keep in mind is that interpretations and explanations will be as important as calculations.

Courtesy: I ask you to please observe some common courtesies, specifically to:

• arrive to class on time (but if you are late, please enter without disrupting class);
• print handouts ahead of time on your own (please do not use the printers in our classroom for this purpose);
• do not talk to others when I am talking (but please do talk to your neighbors when working on collaborative activities);
• do not allow your cell phone to ring or send text messages during class (or else you are to bring cookies for everyone to the next class session);
• staple pages together when you hand in a multi-page assignment (to minimize the chance that I lose a page or get them mixed up);
• include section number with your name when you hand in assignments;
• include your name when you send me an e-mail message (it’s often impossible to discern your name from an e-mail address).

Advice: With apologies to David Letterman, I offer the following “Top Ten” suggestions to improve your learning in this course:

• Come to class.
• Participate in class.
• Invest time outside of class.
• Read carefully.
• Make use of resources posted online.
• Ask questions.
• Use office hours.
• Work on the optional problems.
• Help each other.
• Take pride in your work.

A common theme emerges from this list: You are responsible for your own learning. As your instructor, I view my role as providing you with contexts and opportunities that facilitate the learning process. Please call on me to help you with this learning in whatever ways I can.

Here’s a bonus eleventh suggestion:

• Think!

You will be expected to think in this course. Please be prepared for me to ask you to reason and explain as much as I expect you to do calculations.

Schedule: The following is always subject to change but should give you a sense for what topics we will cover and when:

Week	Dates	Topics	Sections
1	Jan 5 - 9	Review, Two-Sample Inference	Notes, 10.4
2	Jan 12 - 16	Two-Sample Inference	10.1, 10.2, 10.3
3	Jan 20 - 23	Exam, Categorical Data	13.1, 13.2
4	Jan 26 - 30	Analysis of Variance	12.1
5	Feb 2 - 6	Simple Linear Regression	14.1, 14.2, 14.3
6	Feb 9 - 13	Exam, Multiple Regression	15.1, 15.5
7	Feb 17 - 20	Multiple Regression	15.2, 15.3
8	Feb 23 - 27	Multiple Regression, Exam	15.4
9	Mar 2 - 6	Time Series	16.1, 16.2, 16.3
10	Mar 9 - 13	Quality Control	18.1, 18.2
	Mar 16 or 18	Final Exam