Syllabus Stat 202 Spring 2019

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Basic Statistics (Stat 202) Spring 2019 (Sections 001 and 002) [Under Construction]

(For course materials, click here).

Instructor: Sean Carver, Ph.D., Professorial Lecturer, American University.

Contact:

  • office location: Don Myers Technology and Innovation Building (DMTI, East Campus), Room 208F
  • email: carver@american.edu
  • office phone: 202-885-6629

Course Description (from department website): Data presentation, display, and summary, averages, dispersion, simple linear regression, and correlation, probability, sampling distributions, confidence intervals, and tests of significance. Use of statistical software both to analyze real data and to demonstrate and explore concepts. Four credit hours.

A Word of Warning: The Math/Stat Department at AU teaches STAT 202 to prepare students to use statistics in advanced courses required for many majors. Thus the STAT 202 instructor does not always have the luxury of setting the most comfortable and easy pace through the course material. The pace will be determined by what we need to cover for your future classes. There is a lot of material in the curriculum, so be prepared to work hard and spend a lot of time studying outside of class.

Another Word of Warning: The material gets progressively harder throughout the semester, and later topics build on earlier ones. Learn the foundations well, and don't fall behind, because it is hard to catch up.

To reiterate: Studying, and in-class, Expectations:

  • Please note that this course is a 4-Credit Core Course and is part of the University’s Foundation Courses in Quantitative Literacy. We cover a large amount of material in this course (see the weekly schedule provided below). To do well in this course you will need to devote a minimum of 4 to 6 hours per week to studying the material over and above the time spent in-class. The study of Statistics is cumulative in nature – that is every single class builds on, and presumes, your mastering of statistical concepts & techniques covered in the previous class/es. In order to keep up with the class you will need to study & practice the weekly materials and maintain regular attendance throughout the semester.
  • Cell phones need to be silenced and put away during class. Personal use of electronic media during class time is highly distracting – it downgrades the quality of the classroom learning experience for everyone. Laptops (tablets, readers, etc.) should only be out during class time if they are being used for classroom activities. Please save texting, typing/sending emails, checking Facebook, etc. for outside of class time.

Accommodations:

  • Please let me know during the first week of classes if you have any special needs that require accommodations.

Incompletes:

  • A grade of incomplete will only be given under extreme circumstances and will not be granted to any student who is failing.

Prerequisite: MATH-15x or higher, or permission of department. No prior knowledge of statistics is assumed.

Text for Class: Stats: Data & Models, Fourth Edition by Deveaux, Velleman, and Bock. Note the title of the text: there are two Statistics texts available with the same three authors, however the correct e-book comes free of charge with MyStatLab registration and registration for the course. A loose-leaf printed copy can be optionally purchased via MyStatLab for additional money. A bound copy can be optionally purchased separately, but this is expensive. Bound rental copies are available on-line for a very reasonable price.

Learning Management Software: MyStatLab from Pearson. Can be purchased from the bookstore and available as a free two week trial. Access instructions will be handed out on the first day of class. You must register for the section you belong to (001 or 002). You will need to upgrade your free access to full semester access. MyStatLab comes bundled with a subscription to the e-Textbook we will use for the course. You will need this bundle to complete and turn in the required homework.

Statistical Software: StatCrunch. StatCrunch is very easy to learn, and is a great pedagogical tool. StatCrunch (web-based software comes free with MyStatLab and is accessed through MyStatLab. You can also access StatCrunch from StatCrunch.Com but you may need to pay for access through this site.

Please Bring A Laptops To Class! I will be demonstrating software in class with the idea that you follow along with your own computer. Additionally, I will be giving problems to solve in class that require a computer. You may borrow a computer from the library if yours is temporarily unavailable.

Learning Outcomes: Learning objectives: At the end of this course you will be expected to

  1. understand the major concepts related to statistical reasoning and to statistical inferences for drawing such conclusions
  2. understand how these concepts are used in experiments and observational studies across many disciplines, and (3) implement the methods yourself in statistical analyses. Work will be a balance between understanding the concepts underlying a method, implementation of the method, and interpretation of the results.

This course is divided into four related areas of study:

  1. Data collection – the theory and practice of study design.
  2. Data summary and exploration – which illustrates to us how the data actually behave.
  3. Probability and sampling theory – from which we determine how we expect the data to behave.
  4. Data analysis – which allows us to reconcile our expectations with the actual data behavior so that we can make statistical inferences and drawing conclusions.

Office Hours: Students are strongly encouraged to come to office hours if they need or want help. My office hours are tentatively scheduled, as follows (days and times may be adjusted throughout the semester, please come talk to me if you want to come but can't make those times):

  • Monday, Wednesday, Thursday: 11:40 AM - 1:00 PM
  • No office hours during MLK day or Spring Break.
  • My office is DMTI 208F.

SI Leader: Abigail Danfora.

Tutoring through MATH/STAT tutoring center: Don Myers Building, Room 103, walk-ins welcome. See http://www.american.edu/cas/mathstat/tutoring.cfm

Past tutoring lab hours were (check on-line for current schedule):

  • Monday - Thursday: 11:00 AM - 8:00 PM
  • Friday: 11:00 AM - 3:00 PM
  • Sunday: 3:00 PM - 8:00 PM
  • Saturday: Closed
  • Contact: Dr. Behzad Jalali
  • Phone: 202-885-3154
  • Alt Phone: 202-885-3120
  • E-mail: bjalali@american.edu
  • STAT Tutoring through AU's Academic Support and Access Center is now done through the Math/Stat department. See https://www.american.edu/provost/academic-access/mathstat.cfm

Class times and locations:

  • Section 001: Monday, Wednesday*, Thursday 8:10 AM - 9:25 AM, DMTI 119
  • Section 002: Monday, Wednesday*, Thursday 9:45 AM - 11:00 AM, DMTI 119
  • *Wednesday classes dismiss 15 minutes earlier.

Temp Plan

Tentative Weekly Plan:

WEEK FIRST DAY OF WEEK HOMEWORK SETS DUE FIRST DAY OF WEEK READING/LECTURE
Week 1 Jan 14 0 (Due 1-16) Chapter 1: Introduction, STATS Starts Here.
1.1 What is statistics?
1.2 Data.
1.3 Variables.

Chapter 2: Displaying and Describing Categorical Data.

2.1 Summarizing and displaying a single categorical variable.
2.2 Exploring the relationship between two categorical variables.

Chapter 3: Displaying and Summarizing Quantitative Data.

3.1 Displaying quantitative variables.
3.2 Shape.
3.3 Center.
3.4 Spread.
Week 2 Jan 21 1, 1R, 2, 2R Chapter 3: Displaying and Summarizing Quantitative Data.
3.5 Boxplots and the five-number summaries.
3.6 The center of symmetric distributions: the mean.
3.7 The spread of symmetric distributions: the standard deviation.
3.8 Summary: what to tell about a quantitative variable.
Week 3 Jan 28 3, 3R Chapter 4: Understanding and Comparing Distributions.
4.1 Comparing Groups with Histograms.
4.2 Comparing Groups with Boxplots.
4.3 Outliers.
4.4 Time series plots.
4.5 Re-expressing data a first look.

Chapter 5: The Standard Deviation as Ruler and the Normal Model.

5.1 Standardizing with z-scores.
5.2 Shifting and Scaling.
5.3 Normal models.
5.4 Finding normal percentiles.
5.5 Normal Probability Plots.
Week 4 Feb 4 4, 4R, 5, 5R Chapter 6: Scatterplots, Association and Correlation.
6.1 Scatterplots.
6.2 Correlation.
6.3 Warning: Correlation Causation.
6.4 Straightening scatterplots.
Week 5: Exam Wed Feb 11 6, 6R EXAM Wed Feb 13: Chapters 1-5

Chapter 7: Linear Regression.

7.1 Least Squares Line of “Best Fit.”
7.2 The Linear Model.
7.3 Finding the Least Squares Line.
7.4 Regression to the Mean.
7.5 Examining the Residuals.
7.6 : the variation accounted for by the model.
7.7 Regression Assumptions and Conditions.
Week 6 Feb 18 7, 7R Chapter 8: Regression Wisdom.
8.1 Examining Residuals.
8.2 Extrapolation: reaching beyond the data.
8.3 Outlier, Leverage, and Influence.
8.4 Lurking variables and causation.
8.5 Working with summary values.
Week 7 Feb 25 8, 8R Chapter 10: Understanding Randomness.
10.1 What is randomness.
10.2 Simulating by Hand.

Chapter 11: Sample Surveys.

11.1 The Three Big Ideas of Sampling.
11.2 Populations and Parameters.
11.3 Simple Random Sample.
11.4 Other Sample Designs.
11.5 From the population to the sample: you can’t always get what you want.
11.6 The Valid Survey.
11.7 Common Sampling Mistakes or How to Sample Badly.
Week 8 Mar 4 10, 10R, 11, 11R Chapter 12: Experiments and Observational Studies.
12.1 Observational Studies.
12.2 Randomized, Comparative Experiments.
12.3 Four Principles of Experimental Designs.
12.4 Control Groups.
12.5 Blocking.
12.6 Confounding.
Week 9 Mar 11 None Spring Break
Week 10 Mar 18 12, 12R Chapter 13: From Randomness to Probability.
13.1 Random Phenomena.
13.2 Modeling Probability.
13.3 Formal Probability.

Chapter 14: Probability Rules.

14.1 The General Addition Rule.
14.2 Conditional Probability and the Multiplication Rule.
14.3 Independence.

Chapter 15: Random Variables.

15.1 Center: The Expected value.
15.2 Spread: The Standard Deviation.

Chapter 16: Probability Models.

16.1 Bernoulli Trials.
16.2 The Geometric.
16.3 The Binomial Model.
Week 11: Exam Wed Mar 25 13, 13R, 14, 14R, 15, 15R, 16, 16R EXAM Wed Mar 27: Chapters 6-12

Chapter 17: Sampling Distribution Models and Confidence Intervals for Proportions.

17.1 Sampling Distribution Model for a Proportion.
17.2 When does the normal model work? Assumptions and Conditions.
17.3 Sampling Distribution of other statistics.
17.4 The Central Limit Theorem.
17.5 Sampling Distributions: a summary.
Week 12 Apr 1 17, 17R Chapter 18: Confidence Intervals for Proportion.
18.1 A Confidence Interval.
18.2 Interpreting confidence interval: What does 95% confidence really means.
18.3 Margin of error: certainty vs precision.
18.4 Assumptions and Conditions.

Chapter 19: Testing Hypothesis about Proportions.

19.1 Hypotheses.
19.2 P-values.
19.3 The Reasoning of Hypothesis Testing.
19.4 Alternative Alternatives.
19.5 P-values and decisions: What to tell about a Hypothesis Test.
Week 13 Apr 8 18, 18R, 19, 19R Chapter 20: Inference about Means.
20.1 Getting started: The Central Limit Theorem (again).
20.2 Gosset’s t.
20.3 Interpreting Confidence Interval.
20.4 A hypothesis test for the Mean.
20.5 Choosing sample size.

Chapter 21: More about tests and intervals.

21.1 Choosing Hypothesis.
21.2 How to think about p-values.
21.3 Alpha Levels.
21.4 Critical Values for Hypothesis tests.
21.5 Errors.
Week 14 Apr 15 20, 20R, 21, 21R Chapter 22: Comparing Groups.
22.1 The standard deviation of a difference.
22.2 Assumptions and conditions for comparing proportions.
22.3 A confidence interval for the difference of two proportions.
22.4 The two sample Z: testing for the difference of two proportions.
22.5 A confidence interval for the difference between two means.
22.6 the two-sample t-test: testing for the difference between two means.
22.7 The pooled t-test
Week 15 Apr 22 22, 22R Chapter 23: Paired Samples and Blocks
23.1 Paired data.
23.2 Assumptions and Conditions.
23.3 Confidence Interval for Matched Pairs.
Last Class Mon Apr 29 23, 23R Review

Temp 2

Important Dates:

  • Monday, January 14: First day of class.
  • Monday, January 21: Martin Luther King, Jr. day, no class or office hours.
  • Wednesday, February 13: Midterm Exam 1.
  • Week of March 10: Spring Break. No class or office hours.
  • Friday, March 22: Last Day to Drop Class.
  • Wednesday, March 27: Midterm Exam 2.
  • Monday, April 29: Last Day of Class.
  • Early May: Final Exams (see below for specifics).

Final Exams:

  • Please note that I will not be able to accommodate students who wish to switch their exam time with another slot that I am proctoring the exam.
  • Please note that inconvenience with travel plans is NOT a valid excuse for not taking the final as scheduled.
  • If you miss the final or any exam, you will need an excuse through the Dean of Students.
  • With an excused absence, the make up may not be until next semester (June or September)...in the meantime you will get an incomplete.
  • Section 001 (usually meets at 8:10 AM), your final exam: Monday, May 6, 8:10 AM - 10:40 AM
  • Section 002 (usually meets at 9:45 AM), your final exam: Thursday, May 2, 8:10 AM - 10:40 AM (NOTE Start time change!)

Tentative grading scheme:

ITEM PERCENT
Homework 25%
Midterm Exam 1 20%
Midterm Exam 2 20%
Final Exam 25%
Attendance and Participation 10%

Homework: Assigned through MyStatLab.

Exams: You will use a lab computer if one is available. Otherwise, you will need your computer. You can borrow a computer from the library, if needed. You will have access to StatCrunch. You will not be able to do Google searches, access this website, or use your computer in any other way, unless cleared by instructor. Absences on exam days must be excused through the Dean of Students, who needs to send a letter to me indicating that they excuse your absence. If excused through the Dean of Students, you do not need to disclose the reason to me.

Attendance: You are expected to attend every class. However, there can be compelling reasons why you may need to miss class, once in a while. You must email me if you are not going to attend within 24 hours after the class. Please indicate the reason, unless your absence is being excused by the Dean of Students---in which case, let me know this. Absences are OK only occasionally and only for good reasons (such as sick, religious holidays for faith you practice, varsity meets for a team you are on, etc---not a complete list). If you need to miss more than occasionally, even if it's for good a good reason, please consider dropping the class. If you miss one or more times unexcused, or more than occasionally excused (not counting those excused through official channels) you may receive an "early warning." After your warning you are in danger of losing all credit in the attendance and participation category. Every day builds upon the material previously covered, and missing class puts you at a disadvantage, even if it is for a good reason.

Class Etiquette: Please participate in class by asking questions when you do not understand something. Invariably other students benefit from these questions. Please engage in discussions, and please engage with the class, generally. I find it easier to give good lectures when students are asking questions, and engaging with the material.

Please give the class your full attention and refrain from talking, texting, surfing the web, and similar distractions. If it is clear to other students that you are not paying attention, it will be harder for them to pay attention to me. This statement is true in general, but it is especially true if you are talking. Also, it can also be harder for me to give good lectures, when it is clear that not everyone is paying attention. Like you, your classmates are paying a lot of money to be here. Please have some respect for the others in the room. If you need to attend to something urgently, it is OK to excuse yourself from the classroom. Please be warned that if people are not following this request, I may reread this statement to the class.

Academic Integrity: Cheating is not acceptable and will not be tolerated. Consider this: in subtle ways, cheating to get a better grade on an exam can result in lowering the grades of some of your classmates. Certainly this is true when a specific curve is used to assign grades. Even when I don't use curves explicitly, they can be implicit in decisions about writing and grading exams. I will handle all cases of suspected cheating according to the policies of American University. Cheating is giving or receiving unauthorized assistance on quizzes or exams, from other students or other people, from notes, from books, or from the web. When inappropriate copying between students is caught, both parties may be culpable.

Public Service Announcement: A representative of AU's Students Against Sexual Violence (SASV) approached me and asked me to include on my syllabi a list of resources available for survivors of sexual assault and their friends. While sexual violence is by no means the only challenge faced by students, I agree that this issue merits particular attention, so I am honoring her request by attaching the list she gave me:

Sexual Assault Resources

  • It’s never the survivor’s fault. There are many people you can talk to if you or someone you care about has been sexually assaulted:
  • AU's Sexual Assault Prevention Coordinator Daniel Rappaport (rappapor@american.edu)
  • AU's Coordinator for Victim Advocacy Sara Yzaguirre (sarayza@american.edu)
  • DC SANE Program (Sexual Assault Nurse Examiner) 1-800-641-4028
  • The only hospital in DC area that gives Physical Evidence Recover Kits (rape kits) is Medstar Washington Hospital
  • DC Rape Crisis Center: 202-333-7273
  • Students found responsible for sexual misconduct can be sanctioned with penalties that include suspension or expulsion from American University, and they may be subject to criminal charges
  • If you want to submit a formal complaint against someone who has sexually assaulted you, harassed you, or discriminated against you based on your gender identity or sexual orientation, you can do so online at http://www.american.edu/ocl/dos/, or contact the Dean of Students at dos@american.edu or 202-885-3300. These are Title IX violations, and universities are legally required to prohibit these actions.
  • Resources on campus that are required to keep what you tell them confidential are Daniel Rappaport, Sara Yzaguirre, ordained chaplains in Kay, and counselors at the counseling center. (OASIS may also belong here but it didn't exist when this list was created.)
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