• Erin Howard (she/her)
• Email: [email protected] (Include "ST 314" and your lecture time in the subject line)
• Campus Office: Weniger 273

There are two sections of this course. You must attend the lecture time of the section for which you registered. Lectures will take place on Tuesdays and Thursdays at the following times:

• 8:30-9:50 am in Gilbert Hall Rm. 124
• 12-1:20 pm in Gilfillan Auditorium

In-person attendance and participation in class are mandatory. Starting in Week 2, during class, you'll complete activities and assignments based on the pre-lecture material and material discussed in class. Your three lowest in-class assignments will be dropped. This means you are allowed to miss three classes with no penalty. Extensions for in-class assignments beyond the three excused in-class assignments will only be awarded for extenuating circumstances.

All lectures will be recorded and posted to Canvas. Please allow for some time between the end of the lecture and when recordings are posted as the videos can take some time to process.

• Erin - Tuesday 2-3 pm in Weniger 273
• Erin - Thursday 10-11 am in Weniger 273

• Eddie Kingston - [email protected]
• Alex Kuhn - [email protected]
• Falah Alanazi - [email protected]

Required

• Top Hat - You will need to purchase a Top Hat subscription (if you have not already done so) from the Beaver Store. See the Top Hat section later in the syllabus for more details.
• R Statistical Software - We will explore data with statistical software R studio. Homework and Exam questions will assume you can interpret software output. See the R and R Studio page in the Start Here module for more details.
  • R is a free open-source language. Download base R using this link: R Download Website
  • R Studio is a user interface that uses the R language. R Studio is strongly recommended - it is much easier to work in than base R. No cost download for a system with base R
    • R Studio Website
  • Alternatively, you may use an internet-based RStudio interface called RStudio Cloud.
• FREE Textbooks
  • OpenIntro Statistics by David Diez, Mine Cetinkaya-Rundel, and Christopher Barr
    • Open the link above. Under "Getting Started" select FREE -- OpenIntro Statistics PDF.
    • The text is FREE. When downloading the PDF, you will have the option to contribute to the authors. This is optional. To get the text for free, set the sliders to $0.
    • Add the Ebook to your cart and checkout.
    • This will be our primary text throughout the course.
  • OpenStax Introductory
    • The chapters of this text can be read online, without a downloaded PDF.
    • There is an option to download a PDF if you prefer.
    • This will be our secondary text throughout the course.
• Word Processor - Microsoft Office 365 is available to all OSU students and can be accessed on the OSU Office 365 Site. Other word processors are accepted.

Introduction to Statistics for Engineers is a fast-paced introductory course for students majoring in engineering, science, or computer science. The course requires a brief understanding of integral calculus. ST314 is designed to be a dense one-term introduction to probability, inferential statistics, experiments, hypothesis testing, estimation, regression, statistical process control, and statistical coding and interpretation using R. ST314 is worth 3 credit hours. Students should expect to spend up to 9 hours per week on lectures, in-class assignments, homework, and readings.

Prerequisites: MTH 252

The following are expectations of a student after the completion of this course.

• Have an understanding of the role of statistics within the engineering profession.
• Be able to graphically display data in ways meaningful for interpretation.
• Have an understanding of variability in engineering processes and ways of modeling such variability.
• Have a basic understanding of the tools of statistical inference.
• Have an understanding of the principles of experimental design and be able to identify when such principles can be put to use for engineering problems.
• Be able to construct and interpret linear regression models involving one or more independent variables.

• Set theory and Probability, Discrete and Continuous Random Variables and their Probability Models
• Expectation and Variation for Random Variables
• Sampling and Experimental Designs
• Summary Statistics and visual displays for categorical and quantitative variables
• Central Limit Theorem and Sampling Distributions
• Estimation and Hypothesis testing for single sample means
• Estimation and Hypothesis testing for comparing two sample means
• Single Factor ANOVA
• Simple and Multiple Linear Regression, Scatterplots, Correlation, and Residual Analysis
• Statistical Process Control, xbar and S charts, p and c charts
• R software

Canvas will be your hub for course information, assignments, grades, and other material used outside of lectures. You will find assignments, communicate with peers, and review grades in Canvas. The instructor and teaching assistants will monitor the Canvas discussion boards where you are encouraged to post questions. You can access Canvas through an app available for Android and Apple. Log in to Canvas on a regular basis (at least every two days).

Materials and student expectations are listed in the Modules tab for each week of the course. For a clear understanding of expectations, you should read and follow the instructions for the week.

Each week, prior to attending the lectures, you'll participate in a pre-lecture assignment. The pre-lecture assignment will expose you to the concepts, definitions, and some examples that will be necessary for your successful participation in the in-person lecture meetings.

Lecture meetings will consist of a review of the readings and pre-lecture assignments, demonstrated examples, and collaborative group activities.

Starting Week 2, you must have a Top Hat compatible device with you in class.

• Announcements - Please check the Announcements in Canvas regularly to receive tips, updates, and class information. You are responsible for knowing the information that is announced in Canvas. I strongly recommend adjusting your Canvas settings so that all announcements are automatically pushed to your email.
• Discussion Boards - When you have a course material or non-personal question, post it to the weekly “Got a Question? Ask Here” discussion board. This allows all students an opportunity to respond to your question, and benefit from the explanation.
• Office hours - You can attend office hours to meet in person with Erin to ask questions and discuss course materials.
• Email - Use email for personal questions (grades, extension requests, etc.). Do not use email to ask course material-based questions (use Discussion boards instead).

I strive to provide clear feedback and post grades quickly and often. You are responsible for monitoring your grades. Please voice grading concerns within one week of the original due date and prior to finals week. Final grades are calculated based on weighted assignment groups.

Canvas will calculate your grade automatically for you; however, if you would like to calculate your final grade yourself, use the following formula:

Letter grades will be assigned at the end of the term as follows:

There may be unannounced Top Hat extra credit opportunities during class. Extra credit will not be counted towards your final grade until after the final exam grades have been posted to Canvas at the end of the term.

To request an incomplete please review the valid reasons for receiving an I Grade fill out the request form available online or in the Statistics office (Weniger 239). See the Statistics Department policy on incompletes. Please read the instructions for valid reasons to request an Incomplete. Return the completed request form to the Statistics office. You will be notified within a few days of making the request as to whether your request was granted.

Reasons an Incomplete will not be granted:

• Request is prior to the University withdrawal date.
• Student has earned less than 60% of the points available upon request of the incomplete.

Prior to attending each week of lectures, it is expected that you will spend some time familiarizing yourself with the concepts and definitions that will be used to work through examples in class and on the in-class activities. Assigned readings and pre-lecture activity details will be published in the appropriate Canvas module.

Pre-lecture activities will be due on Monday nights at 11:59 pm.

Analyses allow students to explore statistical software, practice data manipulation and discovery, implore a deeper understanding of topics, and receive individualized feedback. Submissions must be typed and submitted in Gradescope. All work should be the personal thoughts and ideas of the individual submitting the assignment. Plagiarism, copying, and cheating will not be tolerated in this course. What is Plagiarism?

Specific due dates are on the Canvas calendar and within each module. The single lowest data analysis score is dropped.

Within one week of the due date, your teaching assistant will grade and provide feedback on your work within Gradescope.

• Submit work on time in Gradescope. - You must select the pages that correspond to each question before hitting submit in Gradescope. Assignments that do not meet this requirement will be subject to a point deduction. - See the Submitting Assignments in Gradescope page for instructions and a video tutorial.
• Label and type answers (exception - only equations and arithmetic work can be written neatly by hand and included along with typed text answers.)
• You need to review feedback and grades. Grading concerns must be made within 1 week of the due date. - Grading concerns can be expressed directly in Gradescope by requesting a regrade.

Data analyses will always be due at 11:59 pm PT. Your individual due dates will be as follows:

• Data Analysis 1 - due 4/7/22
• Data Analysis 2 - due 4/14/22
• Data Analysis 3 - due 4/21/22
• Data Analysis 4 - due 5/5/22
• Data Analysis 5 - due 5/12/22
• Data Analysis 6 - due 5/19/22
• Data Analysis 7 - due 5/26/22
• Data Analysis 8 - due 6/2/22

Top Hat is a responsive classroom interface that will allow you to demonstrate your understanding of the course material and engage with the content covered in this class. Each week during our lecture meetings you will complete an assignment in Top Hat. Your THREE lowest in-class Top Hat Assignment grades will be dropped.

You can purchase a Top Hat subscription from the Beaver Store. A one-year subscription is $25 and can be used for any course at OSU that requires Top Hat. A four-year subscription is $46.

You can visit the OSU Top Hat Support Website. for help setting up your account.

An email invitation will be sent to your OSU email. You can also join this course's Top Hat page by visiting the ST 314D Top Hat page.

For Top Hat support, please contact the Top Hat Support Team directly by way of email ([email protected]) or by calling 1-888-663-5491.

There are two exams in this course - a midterm and a comprehensive final.

• Midterm - The midterm will take place on Thursday of week 5 of the term and will include material from weeks 1-5.
• Final - The final exam is comprehensive. The exam window is scheduled during finals week.
• Both exams will be administered in Gradescope.
• You are allowed to look at notes, previous assignments, lecture recordings, etc. during the exam.
• You are forbidden from speaking with ANYONE about the exam until after the exam windows have closed. Anyone caught committing academic misconduct will receive a zero on the exam and may be subject to formal academic misconduct review with the university.

Academic or Scholarly Dishonesty is defined as an act of deception in which a student seeks to claim credit for the work or effort of another person, or uses unauthorized materials or fabricated information in any academic work or research, either through the Student’s own efforts or the efforts of another. Please read the full text of the Student Code of Conduct here: https://beav.es/codeofconduct.

Please note that assisting, helping another commit an act of academic dishonesty, is itself a violation of the Student Conduct Code.

It is important to avoid even the appearance of dishonesty. Any incidents of academic dishonesty will be dealt with according to the procedure described in the Academic Misconduct Report Form.

Any materials posted to an external site must be your own work. I prohibit my intellectual property from being shared in any manner without explicit or written consent. My intellectual property includes, but is not limited to, the course notes, syllabi, exams, homework and data analysis questions.

Cheating hurts all students. It changes the way a teacher has to provide materials and give assessments. Ultimately, cheating erodes the foundation of a prosperous learning environment. If you witness a violation of academic dishonesty, please report the issue.

University students encounter setbacks from time to time. If you encounter difficulties and need assistance, it’s important to reach out. Consider discussing the situation with an instructor or academic advisor. Learn about resources that assist with wellness and academic success at oregonstate.edu/ReachOut. If you are in immediate crisis, please contact the Crisis Text Line by texting OREGON to 741-741 or call the National Suicide Prevention Lifeline at 1-800-273-TALK (8255).

Accommodations for students with disabilities are determined and approved by Disability Access Services (DAS). If you, as a student, believe you are eligible for accommodations but have not obtained approval please contact DAS immediately. DAS notifies students and faculty members of approved academic accommodations and coordinates implementation of those accommodations. While not required, students and faculty members are encouraged to discuss details of the implementation of individual accommodations.

All students are subject to the registration and refund deadlines as stated in the Academic Calendar: https://registrar.oregonstate.edu/osu-academic-calendar

OSU has twelve established student rights. They include due process in all university disciplinary processes, an equal opportunity to learn, and grading in accordance with the course syllabus: https://asosu.oregonstate.edu/advocacy/rights

• OSU Accounts, Canvas or Computer Issues - IS Service Desk, Milne 201, 541-737-8787 or go to http://is.oregonstate.edu/is-helpdesks-get-help-now
• Technical Help from Ecampus - http://ecampus.oregonstate.edu/services/technical-help.htm
• Top Hat Support - https://learn.oregonstate.edu/classroom-response/top-hat-student-support

I encourage you to engage in the course evaluation process. The evaluation is available toward the end of each term, and you will be sent instructions through ONID. You will log in to “Student Online Services” to respond to the online questionnaire. The results on the form are anonymous and are not tabulated until after grades are posted.

During the term, if you have comments or concerns, I invite you to a respectful and insightful conversation.