View source for Syllabus: Stat 203 Fall 2019

<big> '''Basic Statistics with Calculus (Stat 203) Fall 2019 (Sections 001, 003, and 004) [Under Construction...] '''</big> 

'''[[Stat_203_2019F_Course_Materials|(For course materials, click here).]]'''

'''Instructor:''' <big> Sean Carver, Ph.D., </big> 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:''' A calculus-based introduction to basic statistics including data presentation, display and summary, correlation, development of least squares regression models, probability, independence, probability density functions, moments, use of moment generating functions, sampling distributions, confidence intervals, and tests of significance. Concepts are explored through simulation and the use of the calculus tools of finding maxima and minima of a function and the area under a curve. AU Core Foundation: Quantitative Literacy I. Usually Offered: fall and spring. Prerequisite: MATH-221. Restriction: Registration not allowed in both STAT-203, and STAT-202 or STAT-204. Note: Students may not receive credit toward a degree for both STAT-203, and STAT-202 or STAT-204.

'''Stat 203 Versus Stat 202:'''  Stat 203 is a flavor of Basic Statistics that deepens the presentation of certain topics with the use of calculus and more rigorous mathematics.  Stat 203 shares the same curriculum as Stat 202 (Basic Statistics) and includes the same topics, but goes into more detail in certain respects especially the derivation of least squares regression, the axioms of probability with non-finite sample spaces, the relationship of the probability density function and the cumulative distribution function, expected values of random variables, and perhaps a few other things.  Also, I will not shy away from using in lectures and assigning problems that involve calculus (derivatives, integrals and infinite series) and other mathematics from precalculus but that will actually only be a small part of the class.

'''Word of Warning:'''  The material gets progressively harder throughout the semester, and later topics build on earlier ones.  The time/study requirements increase as the semester progresses. 

'''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.

'''Prerequisite:''' MATH-211 or higher, or permission of department.  No prior knowledge of statistics is assumed.

'''Text for Class:''' ''Stats: Data & Models'', Fifth 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, 003 or 004).  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.

'''Instructions for logging into MyStatLab for the first time:'''
* Section 001 (meets at 8:10 am): carver34822: https://portal.mypearson.com/course-home/handout/carver34822/Student_Registration_Handout_carver34822.pdf
* Section 002 (meets at 9:45 am): carver48006: https://portal.mypearson.com/course-home/handout/carver48006/Student_Registration_Handout_carver48006.pdf

'''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.  With AU credentials, you can access StatCrunch through https://statcrunch.american.edu/.

'''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:''' At the end of this course you will be expected to
# understand the major concepts related to statistical reasoning and to statistical inferences for drawing such conclusions
# understand how these concepts are used in experiments and observational studies across many disciplines, and
# 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:
# Data collection – the theory and practice of study design.
# Data summary and exploration – which illustrates to us how the data actually behave.
# Probability and sampling theory – from which we determine how we expect the data to behave.
# 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):

* Regular Office Hours: Monday, Wednesday, Thursday: 10:00 AM - 11:00 AM
* Hybrid Office Hours: Tuesday, Friday (tentative, see below)
* No office hours during holidays.
* My office is DMTI 208F.

'''Hybrid Office Hours:'''  In the past, my office hours have been inconvenient for many students, and attendance has been poor.  So I am going to try an experiment this semester.  If the experiment isn't successful, we will make adjustments.  We are going to try to use a tool called slack: https://slack.com/ which describes itself as a collaboration hub.  During the first week of class, I will provide instructions for logging in.  With slack we will be able to communicate, and students can ask questions for all to see.  I can answer questions for all to see, and other students can chime in.  On Tuesday and Friday, I will make sure I answer slack questions.  On other days I may answer questions, too.  The hybrid aspect comes in that if there are issues which need face to face time, we can make arrangements publicly over slack, so others can join.  Private messaging is also available, as is posting images, etc.  I am not yet sure what my availability will be for these face-to-face meetings, but they will be arranged flexibly, so that those who want to come can come.

'''SI Leader:''' Evan Steinberg.

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

'''Class times and locations:'''
* Section 001: Monday, Wednesday*, Thursday 8:10 AM - 9:25 AM, Kerwin 302
* Section 003: Monday, Wednesday*, Thursday 11:20 AM - 12:35 PM, DMTI 215
* Section 004: Monday, Wednesday*, Thursday 12:55 PM - 2:10 PM, DMTI 215
* *Wednesday classes dismiss 15 minutes earlier.

'''Emergency Preparedness:''' In the event of an emergency, students should refer to the AU Web site http://www.american.edu/emergency and the AU information line at (202) 885-1100 for general university-wide information.  Information specific to this course during a prolonged closure of the university will be posted on Blackboard.

'''Tentative Weekly Plan:'''
{| border="1" cellspacing="0" cellpadding="4"
!width="20"|WEEK
!width="30"|FIRST DAY OF WEEK
!width="40"|HOMEWORK SETS DUE FIRST DAY OF WEEK
!width="500"|READING/LECTURE
|-
| Week 1 
| Aug 26
| 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 Data.'''
:2.1 Summarizing and displaying a categorical variable.
:2.2 Displaying a quantitative variable.
:2.3 Shape.
:2.4 Center.
:2.5 Spread.
|-
| Week 2
| Sep 02
| 1, 1R, 2, 2R
|'''Chapter 3: Relationships Between Categorical Variables—Contingency Tables.''' 
:3.1 Contingency Tables.
:3.2 Conditional Distributions.
:3.3 Displaying Contingency Tables.
:3.4 Three Categorical Variables
|-
| 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 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. 
'''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 
|}

'''Responsibility:''' There is a lot of material on the schedule listed above, and ultimately you are responsible for learning all of it---by paying attention and asking questions during class, by reading the text, by doing the homework sets, etc.

'''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: The start time for our final exam is different from the usual start of our class!)

'''Grading scheme:'''
{| border="1" cellspacing="0" cellpadding="4"
!width="100"|PERCENTAGE RANGE
!width="20"|GRADE
|-
| 95%-100%
| A
|-
| 90%-94.9999%
| A-
|-
| 87%-89.9999%
| B+
|-
| 83%-86.9999%
| B
|-
| 80%-82.9999%
| B-
|-
| 77%-79.9999%
| C+
|-
| 68%-76.9999%
| C
|-
| 65%-67.9999%
| C-
|-
| 55%-64.9999%
| D
|-
| 0%-54.9999%
| F
|}

'''Grading rubric:'''
{| border="1" cellspacing="0" cellpadding="4"
!width="250"|ITEM 
!width="50"|PERCENT
|-
| Homework
| 15%
|-
| Quizzes
| 0%
|-
| Midterm Exam 1
| 25%
|- 
| Midterm Exam 2
| 25%
|-
| Final Exam
| 25%
|-
| Labs/Assignments
| 10%
|}

'''Rules:'''
* Collaboration on a homework or lab is OK, even encouraged!  Exams are to be done individually.

'''Cell Phones and Other Media:''' 
* 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.  If you need to attend to something urgently, it is OK to excuse yourself from the classroom.  

'''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.

'''Homework:''' Assigned, completed, and automatically graded through MyStatLab.  MyStatLab has on-line help to assist with problems.  An incorrect problem can be attempted again.

'''Quizzes:''' Quizzes will not be for credit and will be self-graded.  Bring a pen or pencil and something to write on.

'''Exams:'''  Exams may require a computer.  You can borrow a computer from the library, if needed.  In the past, I have allowed StatCrunch and calculator use on exam but not Google searches, access to this website, or use of your computer in any other way.  I am considering rewriting some of the exams so that computer use is not necessary. 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.

'''Participation:''' Periodically, I will assign labs to be completed in class.  Some of them will include a writing component.
  
'''Attendance:''' You are expected to come to class, and stay until the end of class.  I expect this, the department and the university expect this, and the board that accredits the university does, too.  However, I understand that there are circumstances that warrant an absence.  Please email or slack me if you miss class.  If you miss an exam, you need to get an excuse through the Dean of Students.

'''Class time:''' Class time will be divided between lectures, quizzes, and labs.  In addition, there will be plenty of class time devoted to working on homework with my supervision (active learning).  You are permitted to access the book at any time during class, except during the no-credit quizzes and for-credit exams.  Think of the no-credit quizzes as a dry run for the exams, so they will operate under the same rules, unless otherwise stated.

'''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.

'''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.  Specifically, I am required to report cases of academic dishonesty to the Dean of the College of Arts and Sciences.  Cheating is giving or receiving unauthorized assistance on exams, from other students or other people, from notes, from books, or from the web.  AU’s academic code is found at http://www.american.edu/academics/integrity/ 

'''Support Services:''' A wide range of services is available to support you in your efforts to meet the course requirements. 

* Mathematics & Statistics Tutoring Lab (Myers Building, room 103) provides tutoring in Mathematics and Statistics. Lab hours are Monday-Thursday 11 am – 8 pm, Friday 11 am – 3 pm, and Sunday 3 pm – 8 pm.  Strongly recommended!!!    http://www.american.edu/cas/mathstat/tutoring.cfm 
* One-on-one tutoring is also available for math through Calc II and stats through Intermediate Statistics. Students may sign up for 45-minute appointments, up to 2 times per week. They can schedule appointments at american.mywconline.net.              
* Academic Support and Access Center (x3360, MGC 243) offers study skills workshops, individual instruction, tutor referrals, Supplemental Instruction, writing support, and technical and practical support and assistance with accommodations for students with physical, medical, or psychological disabilities.  Writing support is also available in the Writing Center, Battelle-Tompkins 228.
* Counseling Center (x3500, MGC 214) offers counseling and consultations regarding personal concerns, self-help information, and connections to off-campus mental health resource.
* 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, including: AU's Office of Advocacy Services for Interpersonal and Sexual Violence (OASIS): http://www.american.edu/ocl/wellness/sexual-assault-resources.cfm

'''Class Mascot:''' Meet Knoll, my pet rabbit.  Knoll will serve as the mascot for our class!

[[File:knoll.jpg]]