ࡱ > 9 bjbjcTcT . > > 1 + 8 1 T 4 >S R R R R R R R $ U W R R R d R R N M O d X | N @ R S 0 >S N @X @X ` O O F @X Q R R >S @X : Math 217K: Applied Statistics, Winter 2010
Professor: Dr. Barbara Wahl
Email: wahl@hanover.edu
Office: Fine Arts 137 (x 7326)
Office Hours: MWF 2 pm, and by appointment
Course Handouts & etc. available online at vault.hanover.edu/~wahl
Course description: Applied Statistics begins with a discussion of descriptive statistics, which is the use of graphs and numerical summaries for describing data from individual variables and for investigating relationships among two or more variables. The middle section of the course provides a brief introduction to the design of statistical experiments (emphasizing that each discipline has its own favored procedures and terminology), and a survey of the fundamental concepts of probability, especially sampling distributions. The final part of the course introduces inferential statistics, which is the use of sample data to estimate and test hypotheses about unknown population parameters. Students are actively engaged with the course material through discussions, probability experiments, computer lab exercises, written assignments, oral presentations, and the investigation of real data.
Course features:
You will be engaged in analyzing real data from a wide range of disciplines.
You will work with a small group of your classmates to complete a term project, including collection of data, summary of data, and well-justified conclusions.
You will be challenged to understand the fundamental concepts of probability: probability models; disjoint events; independent events; basic laws of probability; discrete and continuous random variables; mean and variance of a random variable; visual displays for random variables; sampling distributions.
You will increase your skills in the areas of critical thinking, working with abstract concepts, and communicating in the realm of statistics.
You will be supported by a friendly and constructive classroom environment.
You will use computer software (SPSS, Word, PowerPoint) to create accurate and coherent statistical reports.
You will be held accountable for learning the material; there will be frequent quizzes and exams.
Required instructional materials (bring to class each day):
Text: Introduction to the Practice of Statistics (fifth edition), by David S. Moore and George P. McCabe, Freeman pub. (2003). ISBN: 0-7167-6400-8.
Calculator: This course requires each student to have a graphing calculator (with two-variable statistical functions) for computing mean, standard deviation, correlation, linear regression, and so on. The TI-83 is an excellent choice. The TI-89 is not appropriate unless you take the time to download the needed software; it's probably easier to get hold of a TI-83. Calculators may not be used to store information you're supposed to know for use on exams, and may not be shared during exams.
Please bring your calculator to class each day.
Storage medium: USB flash memory device, or the equivalent, for storing computer work.
LADR Objectives
Math 217 will address at least the following two overall LADR objectives.
Objective #2: Understand the key differences in ways of knowing and of evaluating quality work in various disciplines.
Objective #4: Make critical reflective judgments.
Explanation: At its core, statistics is the art and science of evaluating the strength of the evidence represented by numerical data. In the course of analyzing real data from a wide range of disciplines, students in Applied Statistics will grapple with a number of questions which call for evaluating evidence and reaching logically defensible conclusions, such as the following:
Were the data produced in a reliable and objective manner?
Which statistical procedure is most appropriate for analyzing these data? Why?
Are the assumptions of a proposed procedure satisfied well enough in a given situation to apply the procedure effectively?
Based on the experimental data, is the research hypothesis almost certainly true? Why, or why not?
Are there other variables inherent in this situation which the researcher should have considered?
How can random sampling play a key role in making inferences that are almost certainly correct? (Why isnt it contradictory that a random process can be the basis for near certainty?)
Math 217 will also address all of the specific objectives for Abstraction and Formal Reasoning:
Objective #1: Understand the nature of symbolic language, formal reasoning, and the process of solving problems by means of abstract modeling.
Objective #2: Identify the essential qualities of these tools that underlie their effectiveness in the solution of real-world problems.
Objective #3: Explain the limitations of these formal systems of reasoning.
Explanation: The mathematics underlying Applied Statistics is in the construction and analysis of appropriate probability models. Hypothesis testing is a specific example of formal reasoning, in which a working hypothesis is accepted for the sake of argument, the experimental evidence is analyzed (using a probability model) in the light of this working hypothesis, and the resulting probability (p value), if close enough to zero, is used to argue against the working hypothesis and in favor of some alternative hypothesis.
Students in Applied Statistics will repeatedly encounter the use of such reasoning in real-life situations, including the misuse of statistical techniques; lessons will emphasize repeatedly that statistical tools must be applied with wisdom. For example, NASA officials misused statistics to conclude, prior to the Challenger disaster, that there was little evidence of an association between temperature and O-ring failures. The data regarding O-ring failures in the 23 shuttle flights prior to the Challenger disaster provide students with a compelling illustration of what can happen when statistical techniques are applied without proper care.
Grading: Your grade will be determined by the following components.
Quizzes: 15%
Computer labs: 10%
Project, written report: 15%
Project, oral report: 10%
Exams: 50%
-----------------------------------------
Maximum possible: 100%
Quizzes: We will cover approximately 23 sections from our text, and I will suggest exercises for you to practice from each section. Every couple sections we will have a quiz; you will know ahead of time which section(s) will be quizzed on which day (see tentative schedule).
Quizzes will be given at the beginning of class and will last 10 15 minutes. If you miss a quiz due to lateness or absence, that will count as a zero. In exceptional circumstances (extended absence for a compelling cause) I will consider make-up quizzes after the fact. More commonly, if you know ahead of time you will be absent for good cause (like an athletic event or doctors appointment), I might be able to give you the quiz early. Let me know as soon as possible that youd like to take a quiz early and Ill see what arrangements I can make for this.
Quiz questions will be taken directly from the assigned study problems and questions, but number values may be changed at my discretion.
For each quiz there is a 50% chance of the quiz counting in your grade. At the beginning of class on a quiz day, I will roll a six-sided die. If the roll is low, we will work through the quiz together as a review; if the roll is 4 or higher, you will take the quiz. At the end of the term, I will drop your lowest two quiz scores; the remaining scores will be averaged to determine your quiz grade (15% of your final grade).
Exams: We will have two "regular" exams plus a final exam. Before each exam I will provide you a study guide. If you need more help preparing for an exam, please let me know. Your exam average counts for 50% of your final grade.
If you miss an exam due to an emergency situation, you must (if possible) discuss your situation with me on or before the day of the exam. At the very least, send me an email or voicemail describing the nature of your emergency. Consult the schedule for tentative exam dates.
Computer labs: Some days we will meet in the computer lab, CFA 112C (see schedule). You will learn to use the statistical software package SPSS (Statistical Package for the Social Sciences). You will gain the skills you need to complete your term projects and to continue your study of statistics in your major subject (psychology, sociology, biology, political science, exercise science, etc.), if applicable. Lab assignments will be graded for 10% of your final course grade.
You must attend the lab session to receive credit for the assignment unless you have made other arrangements with me.
Term projects: Each of you will work in a group of 2-3 students to complete a term project. Each group will devise a statistical experiment, gather relevant data, analyze their data, and present their findings in written and oral reports. I will provide you with detailed information about the project assignment, the written report (15% of your final grade), and the oral report (10% of your final grade). Consult the course schedule for information on due dates.
Late policy: In general, a 10% per day penalty will be levied for turning in a late assignment such as a lab or a project report. Please see me if you have exceptional circumstances, and be prepared to show documentation such as a note from your physician, etc.
Attendance: I expect and encourage you to come to class each day, except in case of serious illness or other emergencies. If you do miss a class, I appreciate knowing why please send me a brief email, or leave a voicemail message at x7326.
Plagiarism: Submission of someone else's work as your own is plagiarism. It is unacceptable behavior in all situations. Consult your Student Handbook for the consequences of academic dishonesty.
Office Hours: My office is in CFA 137 (x7326). Please do not hesitate to call or stop by my office if there are any ideas you want to discuss, advice I can offer, or just to talk. I am also happy to answer some questions by email ( HYPERLINK "mailto:wahl@hanover.edu" wahl@hanover.edu). My official office hours are MWF at 2 pm, but other times are easily available by appointment.
Math 217 -- Winter 2010-- Tentative Scheduleweek ofdaytopic / activitylab duequiz overJan 11Mon1.1: Introduction(week 1)WedLab #1: Getting started with SPSSThu1.2: Numerical measures of center and spreadFriLab #2: Analyzing a quantitative variableJan 18Mon1.2: cont.lab 1(week 2)WedLab #3: Analyzing categorical variablesThu1.3: Normal distributionsFri1.3: cont.1.1 &1.2Jan 25MonBinomial distributionslabs 2 & 3(week 3)WedLab #4: Importing data; more graphsThuBinomial distributions, cont.1.3FriLab #5: Binomial and normal distributionsFeb 1MonBinomial distributions, cont.lab 4(week 4)WedLab #6: Correlation & regressionThu2.1: ScatterplotsFri2.2: CorrelationbinomialFeb 8Mon2.3: Regressionlabs 5 & 6(week 5)WedLab #7: Correlation & regressionThuHandout: term project informationThu2.4: Cautions for correlation & regressionFri3.3: Sampling design2.1 - 2.3Feb 15MonReview daylab 7(week 6)WedExam #1, thru 2.3Thu3.4: Toward statistical inferenceFri3.4, cont.Feb 22Mon4.1: Randomness, 4.2: Probability models(week 7)Wed4.3: Random variables3.3 & 3.4Thu4.3: cont.Fri4.4: Means and variances of random variables4.2Feb 29Winter Break!Mar 8Mon4.4: cont.(week 8)WedLab #8: Statistical appletsThu5.1: Sampling distributions for counts and proportionsFri5.1: cont.4.3 & 4.4Mar 15Mon5.1: cont.lab 8(week 9)Wed5.2: Sampling distribution for sample meanThu6.1: Estimating with confidenceFri6.1: cont.5.1 & 5.2Mar 22Mon6.2: Tests of significance(week 10)WedLab #9: Confidence intervalsThuReview dayFriExam #2, thru 5.2Mar 29Mon6.2: cont.lab 9(week 11)WedLab #10: Analyzing Project DataThu6.3: Uses and AbusesFri7.1: Inference with the t distribution6.1 & 6.2Apr 5Mon7.2: Comparing two meanslab 10(week 12)WedLab #11: Inference with SPSSThu8.1: Inference for a Single ProportionFri8.2: Comparing Two Proportions7.1 & 7.2Apr 12Moncatch-up daylab 11(week 13)WedProject final reports due Weds 4/14/2010WedProject oral presentations ThuProject oral presentations FriReview day8.1 & 8.2(week 14)Final exam
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