Difference between revisions of "Syllabus: Stat 203 Fall 2019"

Latest revision as of 09:44, 26 August 2019

Basic Statistics with Calculus (Stat 203) Fall 2019 (Sections 001, 003, and 004)

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: 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 timeline as Stat 202 (Basic Statistics) and includes all of 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, notably more mathematical detail on confidence intervals and hypothesis tests. 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. That said, the differences between STAT 203 and STAT 202 will only be a small part of the class. The most important parts of the STAT 203 curriculum are what it shares with STAT 202.

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): carver05237 https://portal.mypearson.com/course-home/handout/carver05237/Student_Registration_Handout_carver05237.pdf
Section 003 (meets at 11:20 am): carver21374 https://portal.mypearson.com/course-home/handout/carver21374/Student_Registration_Handout_carver21374.pdf
Section 004 (meets at 12:55 am): carver61936 https://portal.mypearson.com/course-home/handout/carver61936/Student_Registration_Handout_carver61936.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. I am going to still have Regular Office Hours, as listed above, but in addition, I am going hold Hybrid Office Hours, described here, that will be both in person and online. For hybrid office hours, 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: As mentioned above, this timeline is the same as the one used for most Stat 202 sections, please see Stat 203 Versus Stat 202 above for an explanation of the differences. I'll try my best to keep us to this schedule, however, some adjustments may be needed.

WEEK	FIRST DAY OF WEEK	HOMEWORK SETS DUE FIRST DAY OF WEEK	READING/LECTURE
Week 1	Aug 26	No HW Due	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	H0, H1, H2	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 September 02 is Labor Day!
Week 3	Sep 09	H3	Chapter 4: Understanding and Comparing Distributions. 4.1 Displays for Comparing Groups 4.2 Outliers 4.3 Time plots: Order, Please! 4.5 Re-expressing Data: A First Look Chapter 5: The Standard Deviation as Ruler and the Normal Model 5.1 Using the Standard Deviation to Standardize Values 5.2 Shifting and Scaling 5.3 Normal Models 5.4 Working with Normal Percentiles 5.5 Normal Probability Plots
Week 4	Sep 16	H4, H5	Chapter 6: Scatterplots, Association and Correlation 6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation Does Not Imply Causation 6.4 Straightening Scatterplots
Week 5	Sep 23	H6	Chapter 7: Linear Regression 7.1 Least Squares: The 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 R-Squared : the Variation Accounted for by the Model 7.7 Regression Assumptions and Conditions
Week 6	Sep 30	H7	Chapter 8. Regression Wisdom 8.1 Examining Residuals 8.2 Extrapolation: Reaching Beyond the Data 8.3 Outliers, Leverage, and Influence 8.4 Lurking Variables and Causation 8.5 Working with Summary Values
Week 7	Oct 07	H8	Chapter 10 Sample Surveys 10.1 The Three Big Ideas of Sampling 10.2 Populations and Parameters 10.3 Simple Random Samples 10.4 Other Sampling Designs 10.5 From the Population to the Sample: You Can’t Always Get What You Want 10.6 The Valid Survey 10.7 Common Sampling Mistakes, or How to Sample Badly Chapter 11 Experiments and Observational Studies 11.1 Observational Studies 11.2 Randomized, Comparative Experiments 11.3 The Four Principles of Experimental Designs 11.4 Control Groups 11.5 Blocking 11.6 Confounding
Week 8	Oct 14	H10, H11	Chapter 12 From Randomness to Probability 12.1 Random Phenomena 12.2 Modeling Probability 12.3 Formal Probability Chapter 13 Probability Rules! 13.1 The General Addition Rule 13.2 Conditional Probability and the Multiplication Rule 13.3 Independence
Week 9	Oct 21	H12, H13	Chapter 14 Random Variables 14.1 Center: The Expected value 14.2 Spread: The Standard Deviation Chapter 15 Probability Models 15.1 Bernoulli Trials: 15.2 The Geometric 15.3 The Binomial Model
Week 10	Oct 28	H14, H15	Chapter 16 Sampling Distribution Models and Confidence Intervals for Proportions 16.1 The Sampling Distribution Model for a Proportion 16.2 When Does the Normal Model Work? Assumptions and Conditions 16.3 A Confidence Interval for a Proportion 16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 16.5 Margin of Error: Certainty vs. Precision 16.6 Choosing the Sample Size Friday 11/01 Last day to drop a spring class or change a grade option.
Week 11	Nov 4	H16	Chapter 17 Confidence Intervals for Means 17.1 The Central Limit Theorem 17.2 A Confidence Interval for the Mean 17.3 Interpreting Confidence Intervals 17.4 Picking Our Interval up by Our Bootstraps Chapter 18 Testing Hypotheses 18.1 Hypotheses 18.2 P-values 18.3 The Reasoning of Hypothesis Testing 18.4 A Hypothesis Test for the Mean 18.5 Intervals and Tests 18.6 P-Values and Decisions: What to Tell About a Hypothesis Test
Week 12	Nov 11	H17, H18	Chapter 19 More About Tests and Intervals 19.1 Interpreting P-Values 19.2 Alpha Levels and Critical Values 19.3 Practical vs. Statistical Significance 19.4 Errors
Week 13	Nov 18	H19*	Chapter 20 Comparing Groups 20.1 A Confidence Interval for the Difference Between Two Proportions 20.2 Assumptions and Conditions for Comparing Proportions 20.3 The Two Sample Z-Test: Testing the Difference Between Proportions
Week 14	Nov 25	HW??*	Chapter 20 Comparing Groups, continued 20.4 A Confidence Interval for the Difference Between Two Means 20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means 20.6 Randomization Tests and Confidence Intervals for Two Means 20.7 Pooling Thanksgiving Break this week!
Week 15	Dec 02	H20*	Chapter 21 Paired Samples and Blocks 21.1 Paired data 21.2 The Paired t-Test 21.3 Confidence Interval for Matched Pairs 21.4 Blocking Last Day of Class, H21 due, Thursday Dec 5.

*HW Sets 17-21 have apparently not yet been created. Stay tuned!

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, August 26: First day of class.
Monday, September 2: Martin Luther King, Jr. day, no class or office hours.
Wednesday, October 2: Midterm Exam 1.
Friday, November 1: Last Day to Drop Class.
Wednesday, November 13: Midterm Exam 2.
November 27 and 29: Thanksgiving Break. No class or office hours.
Thursday, December 5: Last Day of Class.
Early December: 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.
An incomplete will ONLY be given under extreme circumstances. A student receiving an incomplete must be passing the class. If you miss the final exam, the only options available to me are to give you a zero for the final exam or an incomplete in the class.
Section 001 (usually meets at 8:10 AM), your final exam: Mon, Dec 09, 2019, between 8:10AM-10:40AM
Section 003 (usually meets at 11:20 AM), your final exam: Mon, Dec 09, 2019, between 11:20AM-01:50PM
Section 004 (usually meets at 12:55 PM), your final exam: Thu, Dec 12, 2019, between 11:20AM-01:50PM (NOTE: The start time for our final exam is different from the usual start of our class!)

Grading scheme:

PERCENTAGE RANGE	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:

ITEM	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.

Labs/Assignments: Periodically, there will be assignments or labs to complete at home, or in class, or both, depending on the assignment. Some of these labs, such as the first one, see this link, will have a writing component.

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! If you want to see more pictures of Knoll, check out his Instagram page: "knollisbusy".

Difference between revisions of "Syllabus: Stat 203 Fall 2019"

Latest revision as of 09:44, 26 August 2019

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@@ Line 1: / Line 1: @@
-<big> '''Basic Statistics with Calculus (Stat 203) Fall 2019 (Sections 001, 003, and 004) [Under Construction...] '''</big>
+<big> '''Basic Statistics with Calculus (Stat 203) Fall 2019 (Sections 001, 003, and 004)'''</big>
 '''[[Stat_203_2019F_Course_Materials|(For course materials, click here).]]'''
@@ Line 13: / Line 13: @@
 '''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.
+'''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 timeline as Stat 202 (Basic Statistics) and includes all of 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, notably more mathematical detail on confidence intervals and hypothesis tests.  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.  That said, the differences between STAT 203 and STAT 202 will only be a small part of the class.  The most important parts of the STAT 203 curriculum are what it shares with STAT 202.
 '''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.
@@ Line 26: / Line 26: @@
 '''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 001 (meets at 8:10 am): carver05237 https://portal.mypearson.com/course-home/handout/carver05237/Student_Registration_Handout_carver05237.pdf
-* Section 002 (meets at 9:45 am): carver48006: https://portal.mypearson.com/course-home/handout/carver48006/Student_Registration_Handout_carver48006.pdf
+* Section 003 (meets at 11:20 am): carver21374 https://portal.mypearson.com/course-home/handout/carver21374/Student_Registration_Handout_carver21374.pdf
+* Section 004 (meets at 12:55 am): carver61936 https://portal.mypearson.com/course-home/handout/carver61936/Student_Registration_Handout_carver61936.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/.
@@ Line 52: / Line 53: @@
 * 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.
+'''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.  I am going to still have Regular Office Hours, as listed above, but in addition, I am going hold Hybrid Office Hours, described here, that will be both in person and online.  For hybrid office hours, 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.
@@ Line 67: / Line 68: @@
 '''Tentative Weekly Plan:'''
+As mentioned above, this timeline is the same as the one used for most Stat 202 sections, please see ''Stat 203 Versus Stat 202'' above for an explanation of the differences.  I'll try my best to keep us to this schedule, however, some adjustments may be needed.
 {| border="1" cellspacing="0" cellpadding="4"
 !width="20"|WEEK
@@ Line 75: / Line 77: @@
 | Week 1
 | Aug 26
-| HW Due
+| No HW Due
 | '''Chapter 1: Introduction, STATS Starts Here'''
 :1.1 What is Statistics?
@@ Line 90: / Line 92: @@
 | Week 2
 | Sep 02
-| HW Due
+| H0, H1, H2
 |'''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
+* September 02 is Labor Day!
 |-
 | Week 3
 | Sep 09
-| HW Due
+| H3
-|'''Chapter 4: Understanding and Comparing Distributions.'''
+| '''Chapter 4: Understanding and Comparing Distributions.'''
 :4.1 Displays for Comparing Groups
 :4.2 Outliers
@@ Line 112: / Line 116: @@
 :5.5 Normal Probability Plots
 |-
-| Week 3
+| Week 4
-| Jan 28
+| Sep 16
-| 3, 3R
+| H4, H5
+| '''Chapter 6: Scatterplots, Association and Correlation'''
-:4.1 Comparing Groups with Histograms.
+:6.1 Scatterplots
-:4.2 Comparing Groups with Boxplots.
+:6.2 Correlation
-:4.3 Outliers.
+:6.3 Warning: Correlation Does Not Imply Causation
-:4.4 Time series plots.
+:6.4 Straightening Scatterplots
-: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
+| Week 5
-| Feb 4
+| Sep 23
-| 4, 4R, 5, 5R
+| H6
-| '''Chapter 6: Scatterplots, Association and Correlation.'''
+| '''Chapter 7: Linear Regression'''
-:6.1 Scatterplots.
+:7.1 Least Squares: The Line of “Best Fit”
-:6.2 Correlation.
+:7.2 The Linear Model
-:6.3 Warning: Correlation Causation.
+:7.3 Finding the Least Squares Line
-:6.4 Straightening scatterplots.
+:7.4 Regression to the Mean
+:7.5 Examining the Residuals
+:7.6 R-Squared : the Variation Accounted for by the Model
+:7.7 Regression Assumptions and Conditions
 |-
-| 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
+| Sep 30
-| 7, 7R
+| H7
-| '''Chapter 8: Regression Wisdom.'''
+| '''Chapter 8. Regression Wisdom'''
-: 8.1 Examining Residuals.
+:8.1 Examining Residuals
-: 8.2 Extrapolation: reaching beyond the data.
+:8.2 Extrapolation: Reaching Beyond the Data
-: 8.3 Outlier, Leverage, and Influence.
+:8.3 Outliers, Leverage, and Influence
-: 8.4 Lurking variables and causation.
+:8.4 Lurking Variables and Causation
-: 8.5 Working with summary values.
+:8.5 Working with Summary Values
 |-
 | Week 7
-| Feb 25
+| Oct 07
-| 8, 8R
+| H8
-| '''Chapter 10: Understanding Randomness.'''
+| '''Chapter 10 Sample Surveys'''
-: 10.1 What is randomness.
+:10.1 The Three Big Ideas of Sampling
-: 10.2 Simulating by Hand.
+:10.2 Populations and Parameters
+:10.3 Simple Random Samples
+:10.4 Other Sampling Designs
+:10.5 From the Population to the Sample: You Can’t Always Get What You Want
+:10.6 The Valid Survey
+:10.7 Common Sampling Mistakes, or How to Sample Badly
-'''Chapter 11: Sample Surveys.'''
+'''Chapter 11 Experiments and Observational Studies'''
-: 11.1 The Three Big Ideas of Sampling.
+:11.1 Observational Studies
-: 11.2 Populations and Parameters.
+:11.2 Randomized, Comparative Experiments
-: 11.3 Simple Random Sample.
+:11.3 The Four Principles of Experimental Designs
-: 11.4 Other Sample Designs.
+:11.4 Control Groups
-: 11.5 From the population to the sample: you can’t always get what you want.
+:11.5 Blocking
-: 11.6 The Valid Survey.
+:11.6 Confounding
-: 11.7 Common Sampling Mistakes or How to Sample Badly.
 |-
 | Week 8
-| Mar 4
+| Oct 14
-| 10, 10R, 11, 11R
+| H10, H11
-| '''Chapter 12: Experiments and Observational Studies.'''
+| '''Chapter 12 From Randomness to Probability'''
-: 12.1 Observational Studies.
+:12.1 Random Phenomena
-: 12.2 Randomized, Comparative Experiments.
+:12.2 Modeling Probability
-: 12.3 Four Principles of Experimental Designs.
+:12.3 Formal Probability
-: 12.4 Control Groups.
-: 12.5 Blocking.
+'''Chapter 13 Probability Rules!'''
-: 12.6 Confounding.
+:13.1 The General Addition Rule
+:13.2 Conditional Probability and the Multiplication Rule
+:13.3 Independence
 |-
 | Week 9
-| Mar 11
+| Oct 21
-| None
+| H12, H13
-| Spring Break
+| '''Chapter 14 Random Variables'''
+:14.1 Center: The Expected value
+:14.2 Spread: The Standard Deviation
+'''Chapter 15 Probability Models'''
+:15.1 Bernoulli Trials:
+:15.2 The Geometric
+:15.3 The Binomial Model
 |-
 | Week 10
-| Mar 18
+| Oct 28
-| 12, 12R
+| H14, H15
-| '''Chapter 13: From Randomness to Probability.'''
+| '''Chapter 16 Sampling Distribution Models and Confidence Intervals for Proportions'''
-: 13.1 Random Phenomena.
+:16.1 The Sampling Distribution Model for a Proportion
-: 13.2 Modeling Probability.
+:16.2 When Does the Normal Model Work? Assumptions and Conditions
-: 13.3 Formal Probability.
+:16.3 A Confidence Interval for a Proportion
+:16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
+:16.5 Margin of Error: Certainty vs. Precision
+:16.6 Choosing the Sample Size
-'''Chapter 14: Probability Rules.'''
+* Friday 11/01 Last day to drop a spring class or change a grade option.
-: 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
+| Week 11
-| Mar 25
+| Nov 4
-| 13, 13R, 14, 14R, 15, 15R, 16, 16R
+| H16
-|'''EXAM Wed Mar 27: Chapters 6-12'''
+| '''Chapter 17 Confidence Intervals for Means'''
+:17.1 The Central Limit Theorem
+:17.2 A Confidence Interval for the Mean
+:17.3 Interpreting Confidence Intervals
+:17.4 Picking Our Interval up by Our Bootstraps
-'''Chapter 17: Sampling Distribution Models and Confidence Intervals for Proportions.'''
+'''Chapter 18 Testing Hypotheses'''
-: 17.1 Sampling Distribution Model for a Proportion.
+:18.1 Hypotheses
-: 17.2 When does the normal model work? Assumptions and Conditions.
+:18.2 P-values
-: 17.3 Sampling Distribution of other statistics.
+:18.3 The Reasoning of Hypothesis Testing
-: 17.4 The Central Limit Theorem.
+:18.4 A Hypothesis Test for the Mean
-: 17.5 Sampling Distributions: a summary.
+:18.5 Intervals and Tests
+:18.6 P-Values and Decisions: What to Tell About a Hypothesis Test
 |-
 | Week 12
-| Apr 1
+| Nov 11
-| 17, 17R
+| H17*, H18*
-|'''Chapter 18: Confidence Intervals for Proportion.'''
+| '''Chapter 19 More About Tests and Intervals'''
-: 18.1 A Confidence Interval.
+:19.1 Interpreting P-Values
-: 18.2 Interpreting confidence interval: What does 95% confidence really means.
+:19.2 Alpha Levels and Critical Values
-: 18.3 Margin of error: certainty vs precision.
+:19.3 Practical vs. Statistical Significance
-: 18.4 Assumptions and Conditions.
+:19.4 Errors
-'''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
+| Nov 18
-| 18, 18R, 19, 19R
+| H19*
-|'''Chapter 20: Inference about Means.'''
+| '''Chapter 20 Comparing Groups'''
-: 20.1 Getting started: The Central Limit Theorem (again).
+:20.1 A Confidence Interval for the Difference Between Two Proportions
-: 20.2 Gosset’s t.
+:20.2 Assumptions and Conditions for Comparing Proportions
-: 20.3 Interpreting Confidence Interval.
+:20.3 The Two Sample Z-Test: Testing the Difference Between Proportions
-: 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
+| Nov 25
-| 20, 20R, 21, 21R
+| HW??*
-|'''Chapter 22: Comparing Groups.'''
+| '''Chapter 20 Comparing Groups, ''continued'''''
-: 22.1 The standard deviation of a difference.
+:20.4 A Confidence Interval for the Difference Between Two Means
-: 22.2 Assumptions and conditions for comparing proportions.
+:20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means
-: 22.3 A confidence interval for the difference of two proportions.
+:20.6 Randomization Tests and Confidence Intervals for Two Means
-: 22.4 The two sample Z: testing for the difference of two proportions.
+:20.7 Pooling
-: 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.
+* Thanksgiving Break this week!
-: 22.7 The pooled t-test
 |-
 | Week 15
-| Apr 22
+| Dec 02
-| 22, 22R
+| H20*
-|'''Chapter 23: Paired Samples and Blocks'''
+| '''Chapter 21 Paired Samples and Blocks'''
-: 23.1 Paired data.
+:21.1 Paired data
-: 23.2 Assumptions and Conditions.
+:21.2 The Paired t-Test
-: 23.3 Confidence Interval for Matched Pairs.
+:21.3 Confidence Interval for Matched Pairs
-|-
+:21.4 Blocking
-| Last Class
-| Mon Apr 29
+* Last Day of Class, H21 due, Thursday Dec 5.
-| 23, 23R
-| Review
 |}
+* *HW Sets 17-21 have apparently not yet been created. Stay tuned!
 '''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, August 26: First day of class.
-* Monday, January 21: Martin Luther King, Jr. day, no class or office hours.
+* Monday, September 2: Martin Luther King, Jr. day, no class or office hours.
-* Wednesday, February 13: Midterm Exam 1.
+* Wednesday, October 2: Midterm Exam 1.
-* Week of March 10: Spring Break.  No class or office hours.
+* Friday, November 1: Last Day to Drop Class.
-* Friday, March 22: Last Day to Drop Class.
+* Wednesday, November 13: Midterm Exam 2.
-* Wednesday, March 27: Midterm Exam 2.
+* November 27 and 29: Thanksgiving Break.  No class or office hours.
-* Monday, April 29: Last Day of Class.
+* Thursday, December 5: Last Day of Class.
-* Early May: Final Exams (see below for specifics).
+* Early December: Final Exams (see below for specifics).
 '''Final Exams:'''
@@ Line 308: / Line 280: @@
 * 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.
+* An incomplete will ONLY be given under extreme circumstances.  A student receiving an incomplete must be passing the class.  If you miss the final exam, the only options available to me are to give you a zero for the final exam or an incomplete in the class.
-* Section 001 (usually meets at 8:10 AM), your final exam: Monday, May 6, 8:10 AM - 10:40 AM
+* Section 001 (usually meets at 8:10 AM), your final exam: Mon, Dec 09, 2019, between 8:10AM-10:40AM
-* 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!)
+* Section 003 (usually meets at 11:20 AM), your final exam: Mon, Dec 09, 2019, between 11:20AM-01:50PM
+* Section 004 (usually meets at 12:55 PM), your final exam: Thu, Dec 12, 2019, between 11:20AM-01:50PM (NOTE: The start time for our final exam is different from the usual start of our class!)
 '''Grading scheme:'''
@@ Line 385: / Line 358: @@
 '''Quizzes:''' Quizzes will not be for credit and will be self-graded.  Bring a pen or pencil and something to write on.
+'''Labs/Assignments:''' Periodically, there will be assignments or labs to complete at home, or in class, or both, depending on the assignment. Some of these labs, such as the first one, see this [[Stat_203_2019F_Course_Materials|link]], will have a writing component.
 '''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.
@@ Line 406: / Line 381: @@
 * 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!
+'''Class Mascot:''' Meet Knoll, my pet rabbit.  Knoll will serve as the mascot for our class!  If you want to see more pictures of Knoll, check out his Instagram page: "knollisbusy".
 [[File:knoll.jpg]]