Course Syllabus

IU Geometric Banner

 Course Details

Instructor: Shellye Suttles, PhD

Teaching Assistant: Meredith Perkins

For office hours and contact information, please see the How to Contact me page.

Class Meeting: Tuesdays + Thursdays, 6:30 - 7:45 pm, SPEA (PV) A221

Lab Meeting: Wednesdays, 5:30 - 6:45 pm, SPEA (PV) 274

 

 Course Description

This course provides graduate-level instruction in the application of statistical analysis to issues in social science; economics; public and environmental affairs; and other related fields. It is designed to assist students in learning the methods by which statistical analyses are carried out, as well as the basic theory that enables and constrains the application of statistics to real world data. The course emphasizes practical aspects of applying such methods, appropriately interpreting the results of these statistical analysis tools, and gaining a meaningful understanding of how statistical analysis can be misused or erroneously executed (either intentionally or unintentionally). As such, the course will address descriptive statistics, statistical inference, the nature of random variables, sampling distributions, point and interval estimation of parameters (mean, standard deviation, etc.), hypothesis testing, analysis of variance, and bivariate and multivariate regression. Although these are traditional topics for an introductory statistics course, the emphasis in V-506 will be on appropriately applying these techniques and extracting meaningful information from unstructured data. Use of computer tools for carrying out statistical analysis (primarily R) will also be a major emphasis.

 

 Learning Outcomes 

By the end of the semester students should be able to:

  • Apply statistical tools to problem-solving in the social sciences;
  • Improve decision-making through statistical analysis;
  • Explain the nature and use of distributions;
  • Generalize sample data to larger populations;
  • Explain the properties and limitations of estimators and hypothesis tests;
  • Measure the nature and strength of relationship between variables;
  • Transform raw data into useful information; and
  • Carry out statistical analysis using computer tools.

 

 Course Requirements 

Course Format

A lecture format with occasional in-class exercises will be used for the Tuesday and Thursday class sessions. Lectures and in-class exercises will take place in person. There is also a lab session scheduled for Wednesdays, beginning the first week of class. The lab sessions will be hosted by a TA or a lab facilitator and used for review of concepts, instruction in the use of R (statistical software), assistance with homework exercises, and return and explanation of graded homework exercises and the exams. Lab sessions will take place in person as well. Regular attendance in both lecture and lab sessions is advised for all students.

 

Grading Scale

Midterm Exams (2)

30%

Assignments (2)

30%

Canvas Discussions

15%

Term Project + Presentation

25%

 

Exams

Your exams will be conducted in person and occur online via Canvas. As a result, you will need to bring a laptop computer to class on exam days. They will be a mixture of numeric and short-answer questions. Some may be multiple choice while others will be long form. You are expected to be able to read and interpret R code without the assistance of a computer. You are allowed to have a scientific calculator for the exam, but you are not allowed to use an advanced calculator such as a graphing calculator (e.g. a TI-83 is not allowed).

 

If you have an illness or emergency that requires you to take an exam at a later date, you can make up the exam with a 20% reduction in your total score for each day the exam is postponed. As a result, you will have up to four days to make up the exam for potential credit.

 

Assignments

You may work in teams of up to three (3) students for the assignments. Each group will submit a single submission and list the names of all team members on the assignment. Assignments must be submitted via Canvas, must originate from an R Markdown format, and must be exported and submitted as a Word document. For some questions, you may wish to handwrite your answers. This is fine, but you must then incorporate an image of your written solutions into your R Markdown-generated Word document. You will be instructed on how to do this in lab.

 

Labs

The best way to learn R programming is to practice and tinker. In order to do this, a working knowledge of R and a good grasp of the underlying concepts is a must. Attending the lab sessions is the best way to do this. R is a programming language, and you can consider the lab to be a “language lab,” where you can practice the language of R in a low-stakes, supportive environment. Although the textbook’s lab exercises are structured so you learn the necessary programming techniques, you will not be required to submit the lab exercises.

 

Canvas Discussions

Both attendance and participation in class are essential for your understanding of the material and to perform satisfactorily on the exams. Similarly, your participation in both in-class discussions and online Canvas discussion forums is integral to this course. The quality and quantity of your discussion comments and questions are important. A portion of your final course grade (15%) is allocated to your contribution to Canvas discussions.

 

Term Project + Presentation

Students will complete a term project in teams of up to three (3) students. The project will entail a statistical analysis of county-level U.S. Census Bureau data on a topic of your choice. The topic, however, must be approved by the instructor. These projects will be presented in class during the last week of the semester and will involve a final write-up as an R Markdown-generated Word document. Further guidelines will be distributed by late September.

 

Final Grades

Students earn grades based on their performance.

The instructor provides rubrics for guidance on how to best perform on every assignment, discussion, and exam throughout the semester. The rubric outlines the expectations for each activity, explains the purpose of gaining specific skills, and gives students the opportunity to do self-assessment to reflect on the learning process with exams. As a result of the opportunity to perform your best in the course’s assigned activities with clarity and transparency in grading, the instructor does not offer extra credit, fudge points, or “rounding up” in grades.

The instructor does curve all final grades based on a combination of (1) re-centering the mean by moving the top-scoring student to the maximum score and all other students up to a similar degree and (2) re-weighting the distribution of grades to encompass as many students as reasonably possible in an updated normal distribution. Because of this generous curving method, the instructor does not change grades beyond this curved final grade. If you feel that you are on the cusp of a higher final grade, realize that the grade you earned based on your performance is lower than the curved final grade you see on Canvas and, again, the instructor does not change grades beyond the curved final grade.

Lastly, the instructor does not change grades beyond the curved final grade.

 

 Required Texts

Diez, D.M., Çetinkaya-Rundel, M. and C.D. Barr. OpenIntro Statistics (Fourth Edition). 2019. (ISBN  9781943450077)

Additional readings will be assigned in advance and provided via Canvas. All course readings are required unless explicitly marked as optional. The main concepts from the assigned readings will be discussed in class and are fair game for exam questions.

 

 Required Materials

 

 Additional Resources

Useful Texts

 

Useful Websites

 

 Course Policies

Artificial Intelligence (AI)

Your textbook is copyrighted material. In most cases, it is a serious copyright violation to share any copyrighted material with an AI technology to generate a work product – including to answer homework questions. Additionally, using text generated by AI technology without proper citation is a violation of Indiana University’s Student Code of Conduct (see ‘Academic Dishonesty’ below).

Nevertheless, you are permitted to use AI technology to assist with your tidyverse coding generally. Using IU-approved AI technology to assist in completing assignments will be allowed in the following ways:

  • Lab exercises (1-9), can use IU-approved AI technology as a part of learning the tidyverse code used in lab exercises.
  • Homework assignments (1-2), can use IU-approved AI technology as a part of understanding the tidyverse code required in the assignment.
  • Term project, can use IU-approved AI technology as a part of developing the tidyverse code required to complete the project.

If you use AI technology in an unauthorized manner, you will be committing plagiarism and will be subject to penalties in this class and sanctions at Indiana University. 

 

Internet Etiquette

Please take a few minutes and review the Core Rules of Netiquette and considerations for when you are attending class remotely.

 

 Course Communication Policy

Canvas mail is our preferred means of contact:

@Shellye Suttles

@Meredith Perkins

We will return your Canvas mail within 48 business hours. In case of emergency only, e-mail us directly. Please be clear in the subject line and put 'SPEA- V 506’ all direct e-mails.

 

 Assignments and Grades

Course Assessment

In order to assess your understanding of learning objectives, you will be graded based on your in-class participation as well as satisfactory completion of weekly Canvas discussion posts, two (2) assignments, two (2) midterm exams, the term project technical report, and the term project presentation.

 

Submitting Assignments

All assignments, the term project report, and exams will be submitted via Canvas. The assignment and the term project report must be submitted as a R Markdown-generated Word document.

 

How will I know how I'm doing in the course?

Grades will be posted in Canvas within 5 business days of the due date closing. Since Canvas keeps track of all of your grades, you should always be able to calculate your current grade in the course. If you need assistance, please contact the instructor.

 

 Assignment Return and Feedback Policy

Assignment and exam grades are expected to be returned within 5 business days of the due date closing. Please be aware solutions to both assignments and exams are generally posted shortly following the due date closing, if all assignments have been submitted.

 

 Schedule of Assignments

Date Topic Reading Lab Quiz Assignment Exam
Week 1
8/25 T Course introduction Purchase textbook; Read preface pp. 5-6

R, RStudio, & RMarkdown

 Survey 1
8/27 Th Introduction to data Read Ch.1, pp. 7-31
Week 2
9/1 T Summarizing data Read Ch.1, pp. 32-38; Ch. 2, pp. 39-48 Lab 1: Intro to data
9/3 Th Summarizing data Read Ch. 2, pp. 49-66
Week 3
9/8 T Summarizing data Read Ch. 2, pp. 67-78 Lab 2: Summarizing Data
9/10 Th Data Visualization Data Visualization with R
Week 4
9/15 T Distributions of random variables Read Ch. 4, pp. 131-143 Lab 4: Distributions
9/17 Th Distributions of random variables Read Ch. 4, pp. 144-162
Week 5
9/22 T Distributions of random variables Read Ch.4, pp. 163-167 Open Session
9/24 Th Foundations for inference Read Ch. 5, pp. 168-188
Week 6
9/29 T Foundations for inference Read Ch. 5, pp. 189-205 Lab 5A: Foundations for Inference Assignment 1 Due
10/1 Th Term Project Work Day
Week 7
10/6 T Inference for categorical data Read Ch. 6, pp. 206-228 Lab5B: Confidence Intervals
10/8 Th Exam Day Exam 1: Ch. 1-4
Week 8
10/13 T Inference for categorical data Read Ch. 6, pp. 229-248 Lab 6: Inference for Categorical Data Survey 2
10/15 Th Inference for numerical data Read Ch. 7, pp. 249-266
Week 9
10/20 T Inference for numerical data Read Ch. 7, pp. 267-284 Lab 7: Inference for Numerical Data
10/22 Th Inference for numerical data Read Ch. 7, pp. 285-302
Week 10
10/27 T Introduction to linear regression Read Ch. 8, pp. 303-316 Lab 8: Introduction to Linear Regression
10/29 Th Term Project Work Day
Week 11
11/3 T Introduction to linear regression Read Ch. 8, pp. 317-330 Open Session
11/5 Th Introduction to linear regression Read Ch. 8, pp. 331-340
Week 12
11/10 T Multiple and logistic regression Read Ch. 9, pp. 341-352 Lab 9: Multiple and Logistic Regression Assignment 2  Due
11/12 Th Multiple and logistic regression Read Ch. 9, pp. 353-357
Week 13
11/17 T Multiple and logistic regression Read Ch. 9, pp. 358-364 Open Session
11/19 Th Exam Day Exam 2: Ch. 5-8
Week 14: Thanksgiving Break
11/24 T No class
11/26 Th No class
Week 15
12/1 T Multiple and logistic regression Read Ch. 9, pp. 365-370 Labs 6-7 Revisited
12/3 Th Multiple and logistic regression Read Ch. 9, pp. 371-383
Week 16
12/8 T Asynchronous Term Project Work Day None Labs 8-9 Revisited
12/10 Th Term Project Recording Presentations Survey 3
12/13 F Term Project Reports Due

The above schedule is subject to limited change in the event of extenuating circumstances.

 

O'Neill School Expectations of Civility and Professional Conduct

The O’Neill School takes matters of honesty and integrity seriously because O’Neill is the training ground for future leaders in government, civic organizations, health organizations, and other institutions charged with providing resources for the public, and for members of society who are vulnerable and who are lacking in power and status. Precisely because O’Neill graduates tend to rise to positions of power and responsibility, it is critical that the lessons of honesty and integrity are learned early.

O’Neill requires that all members of its community – students, faculty, and staff – treat others with an attitude of mutual respect both in the classroom and during all academic and nonacademic activities outside the classroom. A student is expected to show respect through behavior that promotes conditions in which all students can learn without interruption or distraction. These behaviors foster an appropriate atmosphere inside and outside the classroom:

  • Students are expected to attend class regularly and to be prepared for class.
  • Students must be punctual in their arrival to class and be present and attentive for the duration of the class. Eating, sleeping, reading the newspaper, doing work for another class, wandering in and out of the classroom, and packing up or leaving class early are not civil or professional behaviors.
  • Students must abide by the course policy regarding use of electronic devices in the classroom.
  • Students must responsibly participate in class activities and during team meetings.
  • Students must address faculty members, other students, and others appropriately and with respect, whether in person, in writing, or in electronic communications.
  • Students must show tolerance and respect for diverse nationalities, religions, races, sexual orientations, and physical abilities.
  • Students must not destroy or deface classroom property nor leave litter in the classroom.

** These expectations are excerpted from the O’Neill School Honor Code.

 

 Academic Integrity

Online Course Materials

The faculty member teaching this course holds the exclusive right to distribute, modify, post, and reproduce course materials, including all written materials, study guides, lectures, assignments, exercises, and exams. While you are permitted to take notes on the online materials and lectures posted for this course for your personal use, you are not permitted to re-post in another forum, distribute, or reproduce content from this course without the express written permission of the faculty member. Any violation of this course rule will be reported to the appropriate university offices and officials, including to the Dean of Students as academic misconduct.

Note Selling

Several commercial services have approached students regarding selling class notes/study guides to their classmates. Selling the instructor’s notes/study guides in this course is not permitted. Violations of this policy will be reported to the Dean of Students as academic misconduct (violation of course rules). Sanctions for academic misconduct may include a failing grade on the assignment for which the notes/study guides are being sold, a reduction in your final course grade, or a failing grade in the course, among other possibilities. Additionally, you should know that selling a faculty member’s notes/study guides individually or on behalf of one of these services using IU email, or via Canvas may also constitute a violation of IU information technology and IU intellectual property policies; additional consequences may result.

 

 Technical Support

For additional help with technical issues, consult:

 

 Additional Information

  1. Counseling and Psychological Services (CAPS)
    • For information about services offered to students by CAPS, visit the CAPS website.
  2. Accessible Educational Services (AES)
    • Securing accommodations for a student with disabilities is a responsibility shared by the student, the instructor, and the AES Office. For information about support services or accommodations available to students with disabilities, and for the procedures to be followed by students and instructors, visit the Accessible Educational Services webpage.
  3. Religious Observation
    • In accordance with the Office of the Dean of Faculties, any student who wishes to receive an excused absence from class must submit a Request for Accommodation for Religious Observances. for each day to be absent. This form must be presented to the course instructor by the end of the second week of this semester.  A separate form must be submitted for each day. The instructor will fill in the bottom section of the form and then return the original to the student.  Information about the policy on religious observation can be found on the website for the Office of the Vice Provost for Faculty & Academic Affairs.
  4. Sexual Harassment
    • As your instructor, one of my responsibilities is to help create a safe learning environment on our campus. Title IX and our own Sexual Misconduct policy prohibit sexual misconduct.  If you have experienced sexual misconduct, or know someone who has, the University can help.
      If you are seeking help and would like to talk to someone confidentially, you can make an appointment with:
      • The Sexual Assault Crisis Service (SACS) at 812-855-8900
      • Counseling and Psychological Services (CAPS) at 812-855-5711
      • Confidential Victim Advocates (CVA) at 812-856-2469
      • IU Health Center at 812-855-4011

For more information about available resources visit the Stop Sexual Violence webpage. It is also important to know that federal regulations and University policy require me to promptly convey any information about potential sexual misconduct known to me to our campus’ Deputy Title IX Coordinator or IU’s Title IX Coordinator. In that event, they will work with a small number of others on campus to ensure that appropriate measures are taken and resources are made available to the student who may have been harmed.  Protecting a student’s privacy is of utmost concern, and all involved will only share information with those that need to know to ensure the University can respond and assist.  I encourage you to visit the Stop Sexual Violence webpage to learn more.

 

Additional information and resources, including mental health and financial aid, can be found in the O'Neill Student Portal