• You are very welcome to choose this class. For questions regarding enrollment, please contact Prof. Ettinger: bree.d.ettinger@emory.edu.
• Test 1 has been postponed for a week from Feb. 19th to Feb. 26th.

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• Contacts
• Course description
• Textbook
• Coverage
• Prerequisite
• Students obligations
• Homework
• Midterm tests
• Due dates for homework and tests
• Final exam
• Attendance
• Assessment
• Slides
• Tentative schedule
• Gradescope
• Feedback
• Netiquette
• Honor code
• Accessibility
• Harassment
• Acknowledgement

Lecture Instructor	Dr. Le Chen
Email	le.chen@emory.edu, santiago.arango@emory.edu (please include "Math 362" in the subject field of your email)
Synchronous Session	Wednesday 9:40AM -- 10:55AM
Office hours	Monday and Wednesday 1:00pm -- 2:00pm, or by appointments
Zoom link	https://emory.zoom.us/j/94863155226?pwd=NjducFE0b3hFQ2V0MVYxVXptS2Rxdz09

• The password for both Zoom links are on Canvas page.
• The Zoom link is for both synchronous session and office hours.
• When you send us emails, please do include the keyword Math 362 in the subject field of your email to ensure a timely response.

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Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistics is a highly interdisciplinary field; research in statistics finds applicability in virtually all scientific fields and research questions in the various scientific fields motivate the development of new statistical methods and theory.

This course is the second course of the two-semester sequential courses -- Math 361 and Math 362. In the previous course we studied probability, which lays foundation for this course. In this course, we will study some fundamental ideas in statistics and various tools in statistical inference. In particular, we will cover parameter estimation, hypothesis testing, linear regression, analysis of variance (ANOVA) and various nonparametric counterparts. We will introduce and mostly use the R for data analysis. We may also use the statistical Python depending on students' request.

• "An Introduction to Mathematical Statistics", by Richard J. Larsen and Morris L. Marx, 6th Ed.
• 

The book consists of fourteen chapters, we will cover most parts of the following ten chapters:

• Chapter 5. Estimation
• Chapter 6. Hypothesis testing
• Chapter 7. Inferences based on normal distribution
• Chapter 8. Types of data: a brief overview
• Chapter 9. Two-sample inference
• Chapter 10. Goodness-of-fit tests
• Chapter 11. Regression
• Chapter 12. The analysis of variance
• Chapter 13. Randomized block designs
• Chapter 14. Nonparametric statistics

• Math 361

In order to successfully master the material and complete the course, you are expected to

• Read the textbook and watch video lectures for each section.
• Take the advantage of the office hours, which give you additional chance to interact lively with the instructor.
• Since problems solved or question/answers in the office hour will be beneficial to other students, be default the office hours will be recorded automatically.
  In case you do not want put the office hours on Canvas, please let me know.
• Attend weekly Zoom class meeting, and watch video recordings for the asynchronous materials.
• Participate actively in group activities during Zoom meetings.
• Complete and submit weekly homework through Gradescope.
• Read solutions and any feedback you receive for each problem set.
• Complete two midterm tests and one final exam.
• Use appropriate etiquette and treat other students with respect in all discussions.
• Do not hesitate to ask for help whenever needed.

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Note: The syllabus was created in Dec. 2020, and it is subject to changes during the semester.

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• There will be about 11 weekly homework assignments scheduled as follows:

Releasing	Due at
Wednesday 6:00pm EST	The following Wednesday, 6pm EST

• No late homework will be accepted.
• The homework consists of two phases:
  • In Phase I, you need to complete questions on Canvas Quiz.
  • In Phase II, you need to write details of some problems and upload your solutions to gradescope.
• Keep in mind that, the Canvas Quiz part, no matter it is for homework, test or final exam, you
  will be given two attempts and the highest attempt will be recorded.
• The lowest grade will be dropped, that is, the final score for the homework will be averaged over the rest HWs.
  • Note that the drop policy is not a bonus. It aims at accounting for all circumstances such as
    sickness, injuries, family emergencies, religion holidays, etc.
• Note that, in Phase I, there will be some numerical problems, here are the rounding policies.

• There will be two midterm tests starting from the following three Fridays.

	Releasing at Friday	Due at Saturday	Coverage
Test I	02/26/2021, 6pm EST	02/27/2021, 6pm EST	Chapter 5
Test II	03/26/2021, 6pm EST	03/27/2021, 6pm EST	Chapters 6 -- 10
Final Exam	05/07/2021, 8AM EST	05/08/2021, 8AM EST	Chapters 5 -- 14, comprehensive

• Please note down the above two dates. The setup has taken into account of different time zones.
  Hence, no late exam and further extension will be given.
• You will have 24 hours to complete take-home problems and upload your solutions to Gradescope.
• The test consists of two phases as the homework. The only difference is that you need to complete
  both Phases in 24 hours.

• Here are a list of due dates for eleven homeworks and three tests (the Phase II part).

  	Wednesday				Friday		Saturday
  Week 1	01/27 6pm EST	HW01 releases
  Week 2	02/03 6pm EST	HW02 releases	HW01 is due
  Week 3	02/10 6pm EST	HW03 releases	HW02 is due	Synchronous session canceled
  Week 4	02/17 6pm EST	HW04 releases	HW03 is due	Break day
  Week 5	02/24 6pm EST	HW05 releases	HW04 is due		02/26 6pm EST	Test I releases	02/27 6pm EST	Test I is due
  Week 6	03/03 6pm EST	HW06 releases	HW05 is due
  Week 7	03/10 6pm EST	HW07 releases	HW06 is due
  Week 8	03/17 6pm EST		HW07 is due					(no assignment week)
  Week 9	03/24 6pm EST	HW08 releases			03/26 6pm EST	Test II releases	03/27 6pm EST	Test II is due
  Week 10	03/31 6pm EST	HW09 releases	HW08 is due
  Week 11	04/07 6pm EST	HW10 releases	HW09 is due
  Week 12	04/14 6pm EST	HW11 releases	HW10 is due
  Week 13	04/21 6pm EST		HW11 is due
  Week 14	04/28
  Week 15	05/05				05/07 8am EST	Final Exam releases	05/08 8am EST	Final Exam is due

• Final exam will be cumulative.
• The final exam will consist only of Phase II part.
• You will have 24 hours to complete it and upload them to Gradescope.

• The attendance will be collected during Zoom automatically.
• Attendance will not directly counted into your final score.
• But sufficient attendance will make your eligible for grade curving at the end of semester.

• The final score will be determined as follows:
• 
• Based on the final score (plus potential bonus points), the final letter grade will be determined as follows:

  Grade (+)		Grade		Grade (-)
  		A	92%-100%	A-	90%-91.9%
  B+	87%-89.9%	B	82%-86.9%	B-	80%-81.9%
  C+	77%-87.9%	C	72%-76.9%	C-	70%-71.9%
  D+	67%-67.9%	D	67%-67.9%	D-	60%-61.9%
  		F	0%-59.9%

• Since we will have scores in both Canvas and Gradescope, you are encouraged
  to can download this spread sheet to keep track of your progress.

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• Slides will be updated constantly throughout the semester and please check the time stamp on the front page.
• I strongly encourage you to study in advance.

Chapter/Section	Slides	Slides
Chapter 5: Estimation	presentation	handout
5.1 Introduction	presentation	handout
5.2 Estimating parameters: MLE and MME	presentation	handout
5.3 Interval Estimation	presentation	handout
5.4 Properties of Estimators	presentation	handout
5.5 Minimum-Variance Estimators: The Cramer-Rao Lower Bound	presentation	handout
5.6 Sufficient Estimators	presentation	handout
5.7 Consistency	presentation	handout
5.8 Bayesian Estimation	presentation	handout
Chapter 6: Hypothesis Testing	presentation	handout
6.1 Introduction	presentation	handout
6.2 The Decision Rule	presentation	handout
6.3 Testing Binomial Data -- \(H_0:p=p_0\)	presentation	handout
6.4 Type I and Type II Errors	presentation	handout
6.5 A Notion of Optimality: The Generalized Likelihood Ratio	presentation	handout
Chapter 7: Inferences Based on the Normal Distribution	presentation	handout
7.1 Introduction	presentation	handout
7.2 Comparing \(\frac{\overline{Y}-\mu}{\sigma/\sqrt{n}}\) and \(\frac{\overline{Y}-\mu}{S/\sqrt{n}}\)	presentation	handout
7.3 Deriving the Distribution of \(\frac{\overline{Y}-\mu}{S/\sqrt{n}}\)	presentation	handout
7.4 Drawing Inferences About \(\mu\)	presentation	handout
7.5 Drawing Inferences About \(\sigma^2\)	presentation	handout
Chapter 9: Two-Sample Inferences	presentation	handout
9.1 Introduction	presentation	handout
9.2 Testing \(H_0:\mu_X=\mu_Y\)	presentation	handout
9.3 Testing \(H_0:\sigma_X^2=\sigma_Y^2\)	presentation	handout
9.4 Binomial Data: Testing \(H_0:p_X=p_Y\)	presentation	handout
9.5 Confidence Intervals for the Two-Sample Problem	presentation	handout
Chapter 10: Goodness-of-fit Tests	presentation	handout
10.1 Introduction	presentation	handout
10.2 The Multinomial Distribution	presentation	handout
10.3 Goodness-of-Fit Tests: All Parameters Known	presentation	handout
10.4 Goodness-of-Fit Tests: Parameters Unknown	presentation	handout
10.5 Contingency Tables	presentation	handout
Chapter 11: Regression	presentation	handout
11.1 Introduction	presentation	handout
11.2 The Method of Least Squares	presentation	handout
11.3 The Linear Model	presentation	handout
11.4 Covariance and Correlation	presentation	handout
11.5 The Bivariate Normal Distribution	presentation	handout
11.A Appendix Multiple/Multivariate Linear Regression	presentation	handout
Chapter 12: The Analysis of Variance	presentation	handout
12.1 Introduction	presentation	handout
12.2 The \(F\) Test	presentation	handout
12.3 Multiple Comparisons: Turkey's Method	presentation	handout
12.4 Testing Subhypotheses with Contrasts	presentation	handout
Chapter 13: Randomized Block Designs	presentation	handout
13.1 Introduction	presentation	handout
13.2 The \(F\) Test for a Randomized Block Design	presentation	handout
13.A Appendix: Some Discussions and Extensions	presentation	handout
Chapter 14: Nonoparametric Statistics	presentation	handout
14.1 Introduction	presentation	handout
14.2 The Sign Test	presentation	handout
14.3 Wilcoxon Tests	presentation	handout
14.4 The Kruskal-Wallis Test	presentation	handout
14.5 The Friedman Test	presentation	handout
14.6 Testing for Randomness	presentation	handout

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• Below is the tentative schedule that may change during the semester:

  	Monday -- Friday	Coverage	Test
  Week 1	01/25 -- 01/29	5.1 -- 5.2
  Week 2	02/01 -- 02/05	5.3 -- 5.5
  Week 3	02/08 -- 02/12	5.6 -- 5.7
  Week 4	02/15 -- 02/20	5.8 (Rest Day)
  Week 5	02/22 -- 02/27	6.1 -- 6.4	Test I (Chapter 5)
  Week 6	03/01 -- 03/05	6.5, 7.1 -- 7.5
  Week 7	03/08 -- 03/12	9.2 -- 9.5
  Week 8	03/15 -- 03/19	10.2 -- 10.5	No assignment week
  Week 9	03/22 -- 03/26	11.2 -- 11.3	Test II (Chapters 6-10)
  Week 10	03/29 -- 04/02	11.4 -- 11.5
  Week 11	04/05 -- 04/09	12.2 -- 12.4
  Week 12	04/12 -- 04/16	13.2 -- 13.3
  Week 13	04/19 -- 04/23	14.2 -- 14.6
  Week 14	04/26	Reviewing week

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• We will use gradescope to handle submissions of homework, take-home tests and exams,
  which allows us to provide fast and accurate feedback on your work.
• As soon as grades are posted, you will be notified immediately so that you can log in and see your
  grades and feedback.
• Your Gradescope login is your university email, and your password can be changed there. The same
  link can be used if you need to set your password for the first time.
  • You should have received an email from Gradescope for the registration by 2021-01-23;
  • If you do not receive this email, please use the Entry Code to register yourself: 86KJZ6.
• If you have any questions regarding Gradescope, please send your message to
  • help@gradescope.com
• Printer+scanner or tablet
  • The easiest way to submit the homework/tests/exams is the following steps:
    1. print the given template;
    2. complete the problem sets;
    3. scan the resulting paper (make sure it is legible);
    4. upload the scanned file to gradescope.
  • Alternatively, if you have a tablet that you can write on it, you may simply write on the
    template pdf file and upload the resulting file.
  • Make sure that you make the correct association of your solutions to the problems.
  • Double check your scan quality and make sure your solutions are legible.
• The following short video (1 minutes 40 seconds) shows the basic usage of gradescope, which should
  explain everything you need to be able to do.

• More instruction will be available towards the fall 2020.

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• Your feedbacks are important for us to improve the teaching and make the learning process more effective and enjoyable.
• Here are two ways that you could let me know what your think:
  1. You may send me an email.
  2. If you want to send me some feedback in an anonymous way, you may fill in the following form:

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Not all forms of communication found online are appropriate for an academic community or respectful of others. In this course (and in your professional life that follows), you should practice appropriate etiquette online (``netiquette''). Here are some guidelines:

• You should read and follow Rasmussen College's 10 Netiquette guidelines every online students needs to know.
• If you need extra accommodations, please contact the instructor as soon as possible.
• You are encouraged to login Zoom in time (preferably one or two minutes early) and stay until the end.
• During the zoom class session,
  • please silence all cell phones and other electronic devices;
  • Please do not read email or look at websites, social media, etc;
  • please actively participate in the zoom class meeting:
    • You are highly encouraged to ask and answer questions.
    • You should also expect that I randomly pick students to help me solve exercises
      and/or answer specific questions.
    • There will be Zoom Poll from time to time:
      You need to response the questions during Zoom Sessions to justify your active attendance.

• Students should familiarize themselves with the Emory College honor code here
  • http://catalog.college.emory.edu/academic/policies-regulations/honor-code.html.
• Students are encouraged to share ideas and solutions on problem sets and labs, but must
  express those ideas in their own words in their submitted work.
• Students are not authorized to view or use the work of another student during exams.

Your success in this class is important to me. We will all need accommodations because we all learn differently. If there are aspects of this course that prevent you from learning or exclude you, please let me know as soon as possible. Together we’ll develop strategies to meet both your needs and the requirements of the course.

I encourage you to visit the Office of Accessibility Services (OAS) to determine how you could improve your learning as well. You can register and make a request for services from OAS. In this case, please do inform me of such requests. See the following link for more information:

• http://equityandinclusion.emory.edu/access/index.html

• According to the Emory University policies: http://policies.emory.edu/1.3

Discriminatory harassment of any kind, whether it is sexual harassment or harassment on
the basis of race, color, religion, ethnic or national origin, gender, genetic
information, age, disability, sexual orientation, gender identity, gender expression,
veteran’s status, or any factor that is a prohibited consideration under applicable law,
by any member of the faculty, staff, administration, student body, a vendor, a contractor,
guest or patron on campus, is prohibited at Emory.

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