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QTM100: Introduction to Statistical Inference Spring 2016 Rebecca Roberts, M.A.

Contact Information

Office Modern Languages Building, RM 403 Office Phone 404-727-4132 E-Mail rarobe3@emory.edu

Course Format

The class consists of two weekly lectures and one weekly lab session. The lectures

introduce statistical concepts and the lab sessions supplement lectures using R, a free

statistical program.

Time/Locations

Tuesday & Thursday Lecture (75 min)

Section 001 4:00-5:15pm Anthropology Building 303

Friday Lab (50 min)

Time Lab Location TA Email

9:00-9:50 LE4 Tarbutton 105 Brian Min ymin7@emory.edu

11:00-11:50 LE5 Callaway C101 Megan Warnock megan.warnock@emory.edu

1:00-1:50 LE6 Tarbutton 218 Trent Ryan tryan4@emory.edu

Required Materials

Textbook OpenIntro Statistics, 3rd ed., by David Diez, Christopher Barr, and Mine

Cetinkaya-Rundel. A free pdf of the textbook can be downloaded (pdf or

tablet version), or you can buy a hard copy on Amazon for about $10. Links

can be found on Blackboard in the “Textbook Information and Links” tab.

Clickers Turning Technology clickers are required for lecture but not for lab.

Recommended clickers include the ResponseCard NXT or the QT Device.

Clickers can be purchased at the Emory Barnes and Noble Bookstore.

Calculator A calculator (that is not on your phone) is required for lecture and is

essential for exams. You can use any type of calculator.

Computing All students are required to bring a laptop to lab. Labs use R, a free

statistical software package. This is available for both Windows and Mac

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users. Please note that older versions of Mac may require a system upgrade.

Installation instructions are on Blackboard. Laptops are prohibited in

lecture.

Course Description

This course provides an introduction to descriptive and inferential statistics with emphasis

on practice and implementation. The goal of this course is to introduce students to basic

statistical concepts and to encourage critical thinking about data. The figure below

provides an overview of how the course is organized. Conceptually, this course can be

divided into concepts covered before statistical inference (laying the foundation for

inference), sampling distributions (the underlying theory behind why statistical inference

works), and methods of statistical inference.

Algebra is the only prerequisite for the course. A primary focus of this course is on

implementation of appropriate statistical analysis and interpretation of results.

Consequently, logical reasoning, critical thinking, and writing are also skills that will be

emphasized throughout the course.

By the end of the course, students should be able to (1) understand the effect of study

design on interpretation of statistical results, (2) identify appropriate statistical methods

when presented with new data, (3) read and interpret basic statistical literature from

various sources, such as newspaper articles and academic journals, and (4) use R as a tool

to perform statistical analysis.

Office Hours

Office hours will be posted on Blackboard. Multiple office hours held by Rebecca, the TAs,

and the lab assistants will be available to you weekly on the 4th floor of the Modern

Languages Building. Both Rebecca and the TAs are available to help with content from

either lecture or lab. Lab assistants are available to answer R-related questions only. You

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may attend the office hours of any of our three teaching assistants, regardless of whether or

not they are your assigned lab TA.

Course Evaluation

Student performance will be evaluated through homework, labs, one data analysis project,

two midterms, and a cumulative final. Grades are not rounded up at the end of the

semester.

Assessment Grading Scale

Lab Quiz/Attendance 5% A 93-100 C+ 77-79.9 F 0-59.9 Lab Practice 10% A- 90-92.9 C 73-76.9

Lecture Homework 10% B+ 87-89.9 C- 70-72.9

Project 10% B 83-86.9 D+ 67-69.9

Midterm Exam 1 20% B- 80-82.9 D 60-66.9

Midterm Exam 2 20%

Final Exam 25%

Lectures

Slides will generally be posted by 8pm the day before lecture. Lecture is fast paced and

many students find it helpful to print slides prior to lecture to take notes on the slides.

Content covered during lecture may align with or diverge from content in the text book.

Clickers

Clickers will be used to answer conceptual questions during lecture. Please make sure your

clicker is on the correct channel before each lecture. Clickers are not required for lab.

Attendance

Attendance will be monitored through clickers; you are counted present in lecture if you

answer most of the clicker questions (if you miss one question you will not be counted

absent). Anything taught in class is testable material, and not everything covered will be in

the course reading material. Although attendance does not formally factor into your grade,

final grades are at the discretion of Rebecca and special consideration for students within

one half point of the next highest grade is only given to students with fewer than two

absences. Students who do not attend lecture are not eligible for the extra credit points on

exams that can come from clicker question participation.

Lecture Homework (10%)

Lecture homework is typically assigned every week and you will have approximately one

week to complete the assignment depending on the length and difficulty of the assignment.

Lecture homework may be from the book or created by Rebecca. All lecture homework

assignments will be evaluated on Blackboard. You are welcome to work with others on the

homework problems; however, your Blackboard submission must be completed by you

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alone. The online evaluation will be timed so you should complete the homework set prior

to beginning the Blackboard evaluation. The homework schedule is available on

Blackboard.

Blackboard evaluation of homework may consist of multiple choice, numeric response, or

short answer response. Questions may be verbatim from the assigned problems or may

deviate slightly from the assigned problems. For example, suppose a text book problem

requires you to calculate a probability. On Blackboard, this could be evaluated in two

questions: (1) a multiple choice question for which the student selects the correct formula,

and (2) a numeric response question for which the student inputs the answer.

You earn 50% of your homework grade simply by completing the homework assignment

by the given deadline; the remaining 50% of your homework grade comes from the

accuracy of your submitted answers. Late homework is not eligible to receive credit for

completing the assignment on time. However, students will still be able to complete the

Blackboard online assessment for accuracy. At the end of the semester homework grades

will be averaged to provide an overall homework score.

Submitting homework corrections You may earn 50% of your lost points back on Blackboard homework assignments by

discussing homework corrections during office hours. In order to get points back you must

attend the office hour of Rebecca or any TA, and bring the following:

1. A brief written explanation of why you got the answer wrong.

2. The full written solution or explanation of the correct response.

Discuss the answers during office hours with the TA or Rebecca to receive points back

within one week of the assignment’s due date. Homework corrections cannot be submitted

during lab.

Lab

During lab you will perform statistical analysis using the free statistical R with the RStudio

interface. You are expected to arrive prepared for lab, which includes both reading the

lab manual and watching a brief video on how to do analysis in RStudio. The lab manual

and video will be available one week in advance of each lab. Lab will consist of performing

statistical analysis in R in assigned groups of 3-4 students. You must attend the lab section

in which you are enrolled. You cannot attend the lab section of any other TA without

permission of your TA and the TA of the other section, and such permission will only be

given in special circumstances.

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Pre-Lab Quiz and Lab Attendance (5%)

The pre-lab quiz will consist of three brief questions that will be available on Blackboard

one week prior to lab. Pre-lab quizzes are due by 8:30AM on Friday each lab week (with

the exception of the first week). Content will come directly from the lab manual and video.

Lab attendance is required and will be recorded each week by the TA. You must arrive to

lab on-time. Lab attendance will not count if you are more than five minutes late or if you

leave early. Each week you will receive one lab grade which incorporates both the quiz

and attendance for that week. At the end of the semester lab grades will be averaged to

provide an overall lab score.

Lab Practice (10%)

During each lab students will work to complete a lab practice problem set. Students are

expected to work with their lab groups to complete the lab practice, but each student must

submit an individual lab practice that represents the student’s own work. You will have

one week to submit both: (1) a neat and legible hard copy of your lab practice, and (2) a

well-commented and legible copy of your R code printed out and attached to the lab

practice. Late lab practices will not be accepted. At the end of the semester lab practice

grades will be averaged to provide an overall lab practice score.

Dropped Grades

To allow for tough times and occasional forgetfulness, the lowest lecture homework, pre-

lab quiz/attendance grade, and lab practice will be dropped at the end of the semester.

Therefore, “I have been busy with another class,” is not a valid reason to request an

extension.

Exams (65%)

Exams will be in-class, closed-book, and closed-note. A formula sheet will be provided for

each exam. All exams are cumulative. Both lecture content and R output are testable

material. Exam review sheets and formula sheets will be posted at least one week prior to

the exam. Exams will be entirely multiple choice and administered by Scantron. Looking at

an older version of the exam is prohibited and is considered a violation of Emory’s Honor

Code.

Project (10%)

The data analysis project is designed to provide a holistic overview of statistical analysis.

Students are required to write a one page written report and provide one summary table of

statistical analysis based on an assigned prompt. There are multiple steps in the

submission process. At the end of the semester, project grades will be averaged to provide

an overall project score. More detailed instructions will be provided later in the semester.

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Item Dates Weight

Project Table Draft 1 Friday, Mar 4 (due on BB by 9:00AM) 15%

Project Table Draft 2 Sunday, Apr 10 (due on BB by 5:00PM) 20%

Final Submission Monday, Apr 25 (due on BB by 9:00AM) 65%

Ways to get help This chart can help you decide which resource(s) to use for help with specific topics.

Rebecca's Office Hours

TA Office Hours

Lab Assistant

Office Hours

Mentoring Sessions

EPASS tutors

Blackboard Discussion

Forum

Questions about lecture

Questions about exams

Questions about lab and R coding

Questions about project

Questions about homework

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Submit homework corrections

Do extra practice problems

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1EPASS tutors are not permitted to assist with graded assignments. They can advise on similar hw problems or discuss hw problems after you have submitted them.

2Provides worksheets with practice problems.

EPASS Tutoring

EPASS peer tutoring is available for QTM 100. You are allowed to attend a total of two

EPASS appointments per week during the semester. For information about EPASS policies

and scheduling appointments, visit EPASS and click Peer Tutoring. All tutoring

appointments are scheduled through ASST.

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Mentor Sessions

QTM 100 has two undergraduate mentors that host 1.5 hour long sessions weekly (starting

after Add/Drop/Swap) to do extra practice problems. These sessions are not required and

the problems are not evaluated for a grade. Mentors recommend that you bring exam

formula sheets with you to the session. We encourage you to take advantage of these

sessions weekly. Check Blackboard for mentor session times.

Blackboard Discussion Forum

Questions related to the material covered in class, labs, homework, projects, etc. should be posted on the Blackboard Discussion Forum. When posting to the forum, please use a specific title for your post. Before posting a new question please make sure to check if your question has already been answered. The TAs and Rebecca will be answering questions on the forum daily.

Communication

o Email your TAs to notify them of lab absences and to get help if locked out of a

Blackboard assignment.

o Ask questions during lecture.

o Come to office hours to ask questions. Many concepts are much easier to explain in

person rather than through electronic communication.

o Questions regarding course content should be posted to the Blackboard Discussion

Forum.

o Email with Rebecca is strictly reserved for other personal communication not covered

above.

Other Course Policies

o Please silence electronic devices at the beginning of class.

o Laptops are not allowed in lecture.

o Cell phone use is not permitted.

o Re-grade requests for any assignments will only be considered within one week after

the item has been returned to the student.

o The final exam is on Tuesday, December 15th from 6:30 to 9pm. The only reason

the final exam can be re-scheduled is if you have another exam at this time. Please

contact Rebecca if this applies to you. The final exam will not be re-scheduled for travel

arrangements or other such conflicts.

Expectations

o All course materials and important announcements will be posted on Blackboard. You

are expected to check Blackboard regularly and read your emails. o You are responsible for checking your grades on Blackboard to make sure everything is

recorded correctly. You should check your grades on Blackboard on a weekly basis.

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o You are responsible for notifying Rebecca in a timely manner of any events that may

adversely impact your performance in the class. You are responsible for discussing

such events with the Office of Undergraduate Education. o You are responsible for reading the textbook sections corresponding to lecture

material.

o Although slides are posted for each lecture, you are responsible for paying attention in

class and taking your own notes.

o This is a time-intensive course. You should expect to work 4 to 6 hours a week

outside of lecture and lab on coursework. Some students may need to work more

than that, and some students may need to work less.

Access and Disability Resources

Students with medical/health conditions that might impact academic success should visit

Access, Disability Services and Resources (ADSR) to determine eligibility for appropriate

accommodations. Students who receive accommodations must present the

Accommodation Letter from ADSR to Rebecca at the beginning of the semester or when an

accommodation is granted.

Students who are allowed extra time during an exam must schedule the exam with ADSR

two weeks prior to the exam date. Students who fail to schedule the exam two weeks in

advance may not be able to receive the extra allotted time.

Academic Integrity

Upon every individual who is a part of Emory University falls the responsibility for

maintaining in the life of Emory a standard of unimpeachable honor in all academic work.

The Honor Code of Emory College is based on the fundamental assumption that every loyal

person of the University not only will conduct his or her own life according to the dictates

of the highest honor, but will also refuse to tolerate in others action which would sully the

good name of the institution. Academic misconduct is an offense generally defined as any

action or inaction which is offensive to the integrity and honesty of the members of the

academic community. The typical sanction for a violation of the Emory Honor Code is

an F in the course. Any suspected case of academic misconduct will be referred to

the Emory Honor Council.

Frequently Asked Questions

I made a mistake entering a response on Blackboard – what can I do? You may submit a homework correction within one week of the assignment deadline for

partial credit. Please answer Blackboard questions carefully. Any homework submitted

on Blackboard is graded automatically and requires precise answers. Although a small

range of answers may be accepted, please be careful with rounding and decimal guidelines.

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What do I do if Blackboard locks me out of an assignment? Students occasionally have difficulties with Blackboard submissions. If you get locked out

of your Blackboard submission, email your TA to correct the problem.

How do I submit late lecture homework? You may submit late lecture homework on Blackboard; however, late homework is not

eligible to receive credit for completing the assignment on time.

What do I need to do for a lab absence? Students who miss lab are responsible for completing the lab practice on their own and

submitting the lab practice to their TA on time. Missing class or lab for religious holidays

or school-related travel for academics or athletics is excused. For excused lab absences, you

must notify your TA prior to lab and provide sufficient documentation.

What do I do if I forget to bring my clicker to lecture? Rebecca will generally bring two extra clickers to lecture – they are first-come first-serve.

As your clicker response is linked to your specific clicker’s device ID, you may not borrow a

clicker from a friend to click in for that lecture. If you do not have a clicker you are not

eligible to earn extra credit for that lecture. You must notify Rebecca of your presence if

you wish to have your attendance noted in her records.

What do I do if I am experiencing problems with R? Go to Student Technology Support in the first floor of the Woodruff Library.

I don’t have a laptop/my laptop is broken. Where can I use R? Please email Rebecca as soon as possible if you don’t have a laptop. If your laptop breaks,

you are still expected to complete your assignments. The computers in Cox Hall, the SAAC,

and the Woodruff Library Commons all have R.

I am struggling with the class – what can I do? If you are taking full advantage of QTM 100’s resources (EPASS tutors, mentor sessions,

and office hours) and are still struggling, please request an appointment to meet with

Rebecca.

What do I do if I have a personal event that severely affects my academic work? Extensions may be granted for legitimate reasons including severe illness or family

emergencies; too much coursework for other classes or job commitments are not

legitimate reasons. If you require an extension for what you believe is a legitimate reason,

you must discuss this with Rebecca in a timely manner. Please email her to schedule an

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appointment. You should also strongly consider discussing your situation with a Dean in

the Office of Undergraduate Education.

What do I do if I have a personal event that affects my ability to take an exam? A student who fails to take any required midterm or final examination at the scheduled

time may not make up the examination without written permission from a Dean in the

Office of Undergraduate Education. Permission will be granted only for illness or other

compelling reasons, such as participation in scheduled off-campus events as an official

representative of the University. Please email Rebecca to schedule an appointment to

discuss the situation.

Can I make up assignments at the end of the semester or do extra work to boost my grade? No. You are responsible for keeping up with your assignments throughout the semester.

That is the purpose of the dropped grades.

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Tips for success:

Lecture o Complete the reading prior to each lecture, and review your notes and readings after

lectures.

o Print lecture slides and take notes on the slides.

o Write down answers to clicker questions – answers will not be provided on

Blackboard.

o Physically mark your clicker with your name so you do not mix it up with another

student’s.

o Actively participate during lectures and labs.

o Ask questions during class, office hours, or on the Blackboard discussion page.

o Do not procrastinate.

o Attend mentoring sessions weekly to get extra practice with peer guidance.

Lab o Thoroughly prepare for lab so that you can get the most out of your lab period.

o If you are unable to complete a lab practice during the lab period, do not wait until the

night before it is due to complete the assignment. You will likely need some sort of

assistance at some point.

o If you are stuck, review the lab manual, post a question on the discussion forum, or ask

your lab group members for help.

Homework o Write up your homework as if you are submitting it for a grade. Keep it neat and

legible. That way you can use it to study or do homework corrections.

o Start homework early so that you can seek help from the TAs or Rebecca along the

way.

o Try to do the homework with the exam formula sheet equations – this will better

prepare you for the exam.

o Odd numbered extra practice problems have solutions in the back of the book – check

your work.

o Even and odd problems can be very similar. If you aren’t sure how to start an assigned

even problem, try a similar odd problem first and check the answers. Note that there

are some differences in how Rebecca explains content and how the book explains

content. You are expected to provide solutions to homework that are consistent with

Rebecca’s lectures.

Exam o Review the exam formula sheet carefully and make sure you understand when and

why each formula is used.

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o Make sure you understand why points were deducted on homework and previous

exams; otherwise, you could make the same mistake again.

o Although reviewing lecture slides, notes, and exam review sheets is a good place to

start, it is generally not the best study strategy. The best way to study is to do as many

problems as you can. This will build your confidence and help you solve these types of

problems. Extra problems can be found in the following ways:

□ Attend mentoring sessions.

□ Do the extra problems from the textbook that are assigned with each

homework. Make sure to check your answers in the back of the book.

□ Review the clicker questions and make sure you understand why each answer is

correct or incorrect.

o When doing homework, mentor worksheets, or extra practice problems, try to do the

problems working on your own and using only the exam formula sheet. This will

mimic the exam experience.

o Do not cram the night before an exam.

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Schedule

This is a tentative outline of the schedule and is subject to change.

Date Topic Covered Reading

12-Jan Tues Lec 1: Introduction to QTM 100

14-Jan Thurs Lec 2: Foundations; describing variables 1.1, 1.2, 1.3.1-2, 1.7.1, 1.7.4

15-Jan Fri Lab 1: Introduction to RStudio

19-Jan Tues Lec 3: Summarizing quantitative variables 1.6.2-4

21-Jan Thurs Lec 4: More on quant vars; associations 1.6.1,1.6.5-6, 1.7.2, 1.7.5

22-Jan Fri Lab 2: Introduction to data

26-Jan Tues Lec 5: Cautions in associations; study design 1.3.3-5,1.4,1.5

28-Jan Thurs Lec 6: Probability 2.1-2.2.5,2.4.1-2,2.5

29-Jan Fri Lab 3: Summarizing and visualizing data

2-Feb Tues Lec 7: Normal Distribution 3.1-3.2

4-Feb Thurs Lec 8: Binomial Distribution 3.4

5-Feb Fri Lab 4: Distributions

9-Feb Tues Lec 9: Sampling Distributions 4.1,4.2,4.4,6.1.1

11-Feb Thurs Lec 10: Exam Review

12-Feb Fri Lab 5: Sampling distributions

16-Feb Tues Midterm Exam 1

18-Feb Thurs Lec 11: Confidence Intervals 4.2, 5.1.4, 6.1.2

19-Feb Fri Lab 6: Data cleaning and manipulation

23-Feb Tues Lec 12: Finish CI's; Intro to Hypothesis Testing 4.3,4.5

25-Feb Thurs Lec 13: Hypothesis testing- single mean 5.1

26-Feb Fri Project Lab

1-Mar Tues Lec 14: Hypothesis testing – single proportion 6.1.3

3-Mar Thurs Lec 15: Cautions in Hypothesis Testing 4.3.3, 4.5.5

4-Mar Fri Lab 7: Inf for single mean & Type I/II Error

8-Mar Tues Spring Break - No Class!

10-Mar Thurs Spring Break - No Class! 11-Mar Fri Spring Break - No Class!

15-Mar Tues Lec 16: Inference for paired data 5.2

17-Mar Thurs Lec 17: Inference for two means 5.3

18-Mar Fri Lab 8: Inference for quantitative data

22-Mar Tues Lec 18: Inference for two proportions 6.2

24-Mar Thurs Lec 19: Inference for categorical data 6.3.3,6.4,6.5

25-Mar Fri Lab 9: Inference for categorical data

29-Mar Tues Lec 20: Exam review 31-Mar Thurs Midterm Exam 2

1-Apr Fri Project Lab

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Date Topic Covered Reading

5-Apr Tues Lec 21: Correlation and simple linear regression 7.1,7.2,7.4

7-Apr Thurs Lec 22: Assumptions of simple linear regression 7.2

8-Apr Fri Project Lab

12-Apr Tues Lec 23: Multiple linear regression 8.1,8.3

14-Apr Thurs Lec 24: One-way and two-way ANOVA 5.5

15-Apr Fri Lab 10: Linear Regression

19-Apr Tues Lec 25: Final Exam Review

21-Apr Thurs TBD

22-Apr Fri Lab 11: ANOVA

3-May Tues Final exam 6:30 - 9:00PM Location TBD