Instructor contact information

Your primary contact in STAT 216 is your instructor. If you have concerns that cannot be answered by your instructor, you may reach out to the Student Success Coordinator or the Course Supervisor.

Refer to your section’s Instructor Contact Information under D2L Content for your instructor and co-instructor/TA contact information.

Student Success Coordinator

Jade Schmidt
email:
Office: Wilson 2-263
Phone: (406) 994-6557

Assistant Coordinator

Melinda Yager
email:
Office: Wilson 2-260

Course Supervisor

Dr. Mark Greenwood
email:
Office: Wilson 2-228

Course calendars

Course description

Stat 216 is designed to engage you in the statistical investigation process from developing a research question and data collection methods to analyzing and communicating results. This course introduces basic descriptive and inferential statistics using both traditional (normal and \(t\)-distribution) and simulation approaches including confidence intervals and hypothesis testing on means (one-sample, two-sample, paired), proportions (one-sample, two-sample), regression and correlation. You will be exposed to numerous examples of real-world applications of statistics that are designed to help you develop a conceptual understanding of statistics. After taking this course, you should be able to:

  • Understand and appreciate how statistics affects your daily life and the fundamental role of statistics in all disciplines.
  • Evaluate statistics and statistical studies you encounter in your other courses.
  • Critically read news stories based on statistical studies as an informed consumer of data.
  • Assess the role of randomness and variability in different contexts.
  • Use basic methods to conduct and analyze statistical studies using statistical software.
  • Evaluate and communicate answers to the four pillars of statistical inference: How strong is the evidence of an effect? What is the size of the effect? How broadly do the conclusions apply? Can we say what caused the observed difference?

MUS STAT 216 learning outcomes

  1. Understand how to describe the characteristics of a distribution.
  2. Understand how data can be collected, and how data collection dictates the choice of statistical method and appropriate statistical inference.
  3. Interpret and communicate the outcomes of estimation and hypothesis tests in the context of a problem.
  4. To understand the scope of inference for a given dataset.

CORE 2.0

This course fulfills the Quantitative Reasoning (Q) CORE 2.0 requirement because learning probability and statistics allows us to disentangle what’s really happening in nature from “noise” inherent in data collection. It allows us to evaluate claims from advertisements and results of polls and builds critical thinking skills which form the basis of statistical inference. Students completing a Core 2.0 Quantitative Reasoning (Q) course should demonstrate an ability to:

Interpret and draw inferences from mathematical models such as formulas, graphs, diagrams or tables. Represent mathematical information numerically, symbolically and visually. Employ quantitative methods in symbolic systems such as, arithmetic, algebra, or geometry to solve problems.

Prerequisites

Entrance to STAT 216 requires at least one of the following be met:

  • Grade of C- or better in a 100-level math course (or equivalent)
  • Grade of B or better M090 or the M063/M090 co-requisite
  • Level 30 on the Math Placement Exam or a combination of a good score on Math portion of SAT (540 or higher) or ACT (23 or higher) and/or good high school GPA

You should have familiarity with computers and technology (e.g., Internet browsing, word processing, opening/saving files, converting files to PDF format, sending and receiving e-mail, etc.).

Course materials and resources

Online textbook and coursepack

Two “textbooks” are required for this course:

  1. Montana State Introductory Statistics with R — our free, online textbook
  2. STAT 216 Coursepack — workbook with reading guides and in-class activities and labs

The Stat 216 Coursepack of in-class activities is available for purchase in the MSU Bookstore. You may purchase the coursepack in person, or you may purchase online and have the coursepack shipped to you. Students are expected to bring the coursepack to class each day and to complete the activities within the coursepack. Chapter 1 of the coursepack is provided here if you do not have the coursepack by your first day of class.

RStudio

We will be using the statistical software R through the IDE RStudio for data visualization and statistical analyses.

You will access this software through the MSU RStudio server: rstudio.math.montana.edu. Your username is your 7-character NetID (in the form x##x###, where x is a letter and # is a number), and your password is the password associated with your NetID. Your email address will not work to log in to the RStudio server.

  • Please note: Your netID password expires every 6 months. It is HIGHLY recommended that you reset your netID password BEFORE attempting to login to the Rstudio server. You can reset your netID password in the MSU password portal.

See the Statistical Computing section in the Welcome chapter of our textbook for alternative options for accessing RStudio.

Required course software

All students are required to have a word processor and spreadsheet software installed on the personal device they plan to use for this course. We highly recommend the use of Word and Excel. If you do not currently have Word and/or Excel installed on your device, you can download the Microsoft Office 365 for Students for free by following the instructions here

Learning management tools

  • D2L: Find your instructor and co-instructor/TA contact info, announcements, exploration information, instructor notes, exam review material, assignment and data files, discussion forums, gradebook.

    • Important: Make sure you are receiving email notifications for any D2L activity. In D2L, click on your name, then Notifications. Check that D2L is using an email address that you regularly check; you have the option of registering a mobile number. Check the boxes to get notifications for announcements, content, discussions, and grades.
    • If you have a question about the course materials, computing, or logistics, please post your question to your D2L discussion board instead of emailing your instructor. This ensures all students can benefit from the responses. Other students are encouraged to respond.

  • Gradescope: Submit and review quizzes and assignments, review exam grades. For more details, see our Gradescope Help for Students document

  • Mathematics and Statistics Center: Free drop-in tutoring for 100- and 200-level math and stat courses in Romney Hall 220.

Course format and organization

Stat 216 will meet 3 times per week. Each week, students will:

  • read assigned sections of the online textbook on that week’s content prior to the first day of class each week.
  • use guided notes from the required coursepack during lectures the first day of class each week and complete lecture quizzes on Gradescope after class.
  • complete assigned out-of-class activities prior to the second day of class each week.
  • work in assigned groups two class periods per week for in-class group activities and discussion (Wednesdays) and in-class Rstudio labs (Friday), with these groups changing each unit.
  • complete one assignment in Gradescope.

Course assessment

Your grade in STAT 216 will contain the following components.

Lecture Quizzes (5%)

You will be expected to complete the assigned textbook reading and we recommend using the reading guides provided in the coursepack prior to each Monday class (as shown on the STAT 216 calendar). On Monday, your lead instructor will lead a guided lecture over the course material, after which you will need to complete a quiz over the lecture. Lecture quizzes are found on Gradescope and due by 9pm every Tuesday. Up until the 9pm Tuesday deadline, you can retake the lecture quizzes as many times as you like by clicking Resubmit in Gradescope to open and edit any question answer.

  • Policy for missed lecture days:
    • We will not record or post lectures/asynchronous learning opportunities.
    • If you email your section instructor by 9pm on the date of the missed lecture, you will receive access to a set of videos covering the lecture material which can be used to complete the lecture quiz.
  • Lecture quizzes are due Tuesday at 9pm Mountain Time each week.
  • The lowest lecture grade will be dropped.

Activities (10%)

After Monday lectures, there will be an out-of-class activity to complete. The out-of-class activity can be found in your coursepack and will be handed in at the start of class on Wednesday.

Every Wednesday during class, you will meet with your classmates and instructor to work through that day’s coursepack group activity. Attendance and completion of the in-class and out-of-class activities counts towards this portion of your grade.

  • Activities must be completed in the Stat 216 Coursepack. If you prefer to complete the activity on a pdf copy using a stylus-enabled device, please speak to your instructor, and he or she will provide you with a PDF copy of the coursepack.
  • Activities will be checked for completion at the beginning of the following class period.
  • Policy for missed activities:
    • We will not record or post lectures/asynchronous learning opportunities.
    • Students get a “free pass” for up to three activities per Unit, no questions asked, but students are expected to communicate any absences with their section instructor. Illnesses/emergencies/school-related absences are included in these three; if students have extraneous circumstances, they are encouraged to talk to their instructor.
    • For illnesses or when students cannot attend class, we recommend that the student WebEx into class with their group (or any video conferencing tool of choice). Students attending class remotely can show their activity via WebEx for credit.
    • WebEx should be set up by students’ teammates. If the student is uncomfortable asking, the instructor can facilitate that conversation (e.g., email the students’ teammates, cc’ing the student, and ask if anyone can setup a WebEx meeting for class).

Labs (10%)

Every Friday, you will meet with your classmates and instructor to work through that day’s coursepack Rstudio group lab. The lab will reinforce the ideas learned in the activities and lecture completed Monday and Wednesday but with the use of Rstudio for exploring and analyzing data.

  • Each group will turn in selected questions from the lab to Gradescope. Labs are due Friday at 9pm Mountain Time each week.
    • Exceptions: There will be no labs due on exam weeks or weeks with only two class meetings due to university holidays.
  • The lowest lab grade will be dropped.
  • Each student will also turn in each lab for completion at the beginning of the following class period.

Assignments (10%)

You will complete weekly assignments in Gradescope. These should be completed individually (meaning all answers should be written in your own words), but you may use your classmates, tutors, or your instructor/co-instructor/TA for assistance.

  • Weekly assignments are due Monday at 9pm Mountain Time each week, covering the previous week’s content.
  • The lowest assignment grade will be dropped.

Midterm exams (40%)

There will be two midterm exams (worth 20% of the course grade each). Midterm exams will be taken in class during your normal in-class time. Each exam has a group (Wednesday) and individual (Friday) component. A practice exam will be released in D2L two weeks prior to the exam, with solutions to the practice exam released in D2L one week prior to the exam. Further details, resources, and instructions for each exam will be posted the week prior to the exam in D2L.

Group portion:

Group midterm exam 1 will be February 14th; Group midterm exam 2 will be April 3rd.

  • Group portions of the midterms are worth 20% of your midterm exam grade.
  • Group midterm exams are open book, open notes.
  • You will be allowed a calculator on the group midterm exams.
  • You will be required to use Rstudio on the group midterm exams.
  • If you miss more than 3 class days within a unit, you must complete the group exam individually.

Individual portion:

Individual midterm exam 1 will be February 16th; Individual midterm exam 2 will be April 5th.

  • Individual portions of the midterms will be worth 80% of your midterm exam grade.
  • Potential individual midterm exam questions will be released one week prior to the exam. All exam questions will be selected from this set.
  • On the exam day, you will be given a randomly chosen subset of the previously released potential exam questions for your exam.
  • Individual midterm exams are closed book, closed notes.
  • A formula sheet will be provided to use during the exam (also released with the potential midterm exam questions).
  • You will be allowed a calculator on the individual midterm exams.
  • You will not be required to use Rstudio on the individual midterm exams.

Final exam (25%)

The group final exam will be taken in class during your normal in-class time on the final Wednesday and Friday of classes (prior to Finals week). A practice exam will be released in D2L one week prior to the exam, with solutions to the practice exam released in D2L the Sunday prior to the exam. Further details, resources, and instructions for each exam will be posted the week prior to the exam in D2L.

The individual final exam is a common hour exam. Understand that attending that common hour exam is part of your commitment when you enroll in the course.

Group portion:

Group final exam will be May 1st and 3rd (during normal class time).

  • The group portion of the final exam will be worth 20% of your exam grade.
  • The group final exams is open book, open notes.
  • You will be allowed a calculator on the group final exam.
  • You will be required to use Rstudio on the group final exam.
  • If you miss more than 3 class days in Unit 3, you must complete the group exam individually.

Individual portion:

Individual common hour final exam will be Tuesday, May 7th from 6:00 - 7:50 PM.

  • The individual portion of the final exam will be worth 80% of your exam grade.
  • No potential final exam questions will be released.
  • The individual final exam is closed book.
  • You will be allowed to create a one page note sheet for the exam. You will also be provided a one page formula sheet during the exam.
  • You will be allowed a calculator on the individual final exam.
  • You will not be required to use Rstudio on the individual final exams.
  • Understand that attending that common hour exam is part of your commitment when you enroll in the course.

Letter grades

Final course grades will be determined according to the following scale.

Letter Grade Weighted Score
A 93-100%
A- 90-92.99%
B+ 87-89.99%
B 83-86.99%
B- 80-82.99%
C+ 77-79.99%
C 70-76.99%
D 60-69.99%
F <59.99%

The grade cutoffs may be shifted downward at the end of the semester based on student performance (never upward).

Late work policies

Note: we highly recommend saving your answers for each question while you complete all work in Gradescope. This will ensure you can return to labs, lecture quizzes, or assignments at a later date without fear of losing any progress. Additionally, Gradescope will automatically submit any saved work when the due date passes, ensuring you earn up to full credit for all problems completed on time.

  • Lecture Quizzes:
    • You may take the lecture quizzes in Gradescope as many times as you like up until the due date using the Resubmit button to re-open a quiz.
    • While the due date is Tuesdays at 9pm, you will be allowed to turn in lecture quizzes until 11:59pm on the due date. Lecture quiz submissions that are received between 9pm and 11:59pm will receive a 5% grade deduction.
    • Further extensions on these quizzes are not given unless extenuating circumstances are present which are communicated to the Student Success Coordinator, Jade Schmidt.
  • Activities, Labs, and Assignments: Attendance in this course is critical for success and is therefore required. The in-class and out-of-class activity and lab grades are a proxy for attendance and engagement. Students are expected to be in class during in-class activities and labs to provide support to each other and their teammates while working through the material. We will not record or post lectures/asynchronous learning opportunities. Students get a “free pass” for up to three activities per Unit, no questions asked, but are requested to communicate with their section instructor anytime they miss class. Illnesses/emergencies/school-related absences are included in these three; if students have extraneous circumstances, they are encouraged to talk to their instructor.
    • For illnesses or when students cannot attend class, we recommend that the student WebEx into class with their group (or any video conferencing tool of choice). Students attending class remotely can show their activity via WebEx for credit. WebEx should be set up by students’ teammates. If the student is uncomfortable asking, the instructor can facilitate that conversation (e.g., email the students’ teammates, cc’ing the student, and ask if anyone can setup a WebEx meeting for class).
    • If you need to miss a lab due to illness, quarantine, or other extenuating circumstances, please email your section instructor and group-mates letting them know prior to the lab meeting. You may participate in the lab via video conferencing if desired or you may complete the lab on your own. If the latter, your section instructor will determine an appropriate extension on the lab based on your individual circumstances.
    • For labs and assignments, while the due dates are 9pm on Fridays and Mondays, respectively, you will be allowed to turn in work until 11:59pm on the due date. Lab and assignment submissions that are received between 9pm and 11:59pm will receive a 5% grade deduction.
  • Exams:
    • Students that are in quarantine but healthy enough to take the exam should email Student Success Coordinator Jade Schmidt to make alternative arrangements. Group exams may be taken remotely and proctored via WebEx but all individual exams must be taken in person.
    • If you are ill to the point of not being able to take the exam, please email Student Success Coordinator Jade Schmidt to make alternative arrangements.
    • Students who miss the exam without contacting the instructor prior to the exam will receive a zero on the exam.
    • Work is not a legitimate reason for an exam absence.

Diversity and inclusivity

Respect for Diversity: It is our intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender identity, sexual orientation, disability, age, socioeconomic status, ethnicity, race, religion, culture, perspective, and other background characteristics. Your suggestions about how to improve the value of diversity in this course are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups.

In addition, in scheduling exams, we have attempted to avoid conflicts with major religious holidays. If, however, we have inadvertently scheduled an exam or major deadline that creates a conflict with your religious observances, please let us know as soon as possible so that we can make other arrangements.

Support for Inclusivity: We support an inclusive learning environment where diversity and individual differences are understood, respected, appreciated, and recognized as a source of strength. We expect that students, faculty, administrators and staff at MSU will respect differences and demonstrate diligence in understanding how other peoples’ perspectives, behaviors, and worldviews may be different from their own.

Policy on academic misconduct

Students in an academic setting are responsible for approaching all assignments with rigor, integrity, and in compliance with the University Code of Student Conduct. This responsibility includes:

  1. consulting and analyzing sources that are relevant to the topic of inquiry;
  2. clearly acknowledging when they draw from the ideas or the phrasing of those sources in their own writing;
  3. learning and using appropriate citation conventions within the field in which they are studying; and
  4. asking their instructor for guidance when they are uncertain of how to acknowledge the contributions of others in their thinking and writing.

When students fail to adhere to these responsibilities, they may intentionally or unintentionally “use someone else’s language, ideas, or other original (not common-knowledge) material without properly acknowledging its source” http://www.wpacouncil.org. When the act is intentional, the student has engaged in plagiarism.

Plagiarism is an act of academic misconduct, which carries with it consequences including, but not limited to, receiving a course grade of “F” and a report to the Office of the Dean of Students. Unfortunately, it is not always clear if the misuse of sources is intentional or unintentional, which means that you may be accused of plagiarism even if you do not intentionally plagiarize. If you have any questions regarding use and citation of sources in your academic writing, you are responsible for consulting with your instructor before the assignment due date. In addition, you can work with an MSU Writing Center tutor at any point in your writing process, including when you are integrating or citing sources. You can make an appointment and find citation resources at www.montana.edu/writingcenter.

In STAT 216, assignments that include the same wording as another student, regardless of whether that student was cited in your sources, will be considered plagiarism and will be treated as such. Students involved in plagiarism on assignments (all parties involved) will receive a zero grade on that assignment. The second offense will result in a zero on that assignment, and the incident will be reported to the Dean of Students. Academic misconduct on an exam will result in a zero on that exam and will be reported to the Dean of Students, without exception.

More information about Academic Misconduct from the Dean of Students

Policy on intellectual property

This syllabus, course lectures and presentations, and any course materials provided throughout this term are protected by U.S. copyright laws. Students enrolled in the course may use them for their own research and educational purposes. However, reproducing, selling or otherwise distributing these materials without written permission of the copyright owner is expressly prohibited, including providing materials to commercial platforms such as Chegg or CourseHero. Doing so may constitute a violation of U.S. copyright law as well as MSU’s Code of Student Conduct.

Policy on the use of AI language models

In this course, you may utilize generative AI language models, including ChatGPT, as a resource to support your work except during in-class exams. AI language models are powerful tools developed to generate text based on the input provided. While the AI language models can help refine your writing and coding, it is important to remember that it is an AI system and not a substitute for your critical thinking and creativity. If you choose to use this tool, apply it as a supplement and do not rely solely on its suggestions. Ultimately, you are responsible for the content and quality of your work. Therefore, you should critically evaluate ChatGPT outputs for accuracy, potential bias, and relevancy. When utilizing AI language models, it is essential to ensure that your writing and coding remains original and properly attributed, including citing outputs or text generated by ChatGPT. If you choose to use AI language models to assist you on labs or assignments, you must cite the source used. Failure to do so will result in earning a 0 on all problems in which AI language models usage has been detected.

Please see the How to cite ChatGPT in MLA Style resource. I encourage you to use AI language models to enhance your writing and coding skills, experiment with its capabilities, and learn from its suggestions. If you have any questions or concerns regarding using AI language models for assignments, please discuss them with us.