Course Description

This course introduces the use of statistical methods from the points of views of both consumers and           producers of statistics, with an emphasis on exploring and applying statistical methods to answer questions based on distinct types of sample data. We begin with definitions and examples to get you thinking about   statistical reasoning and the concept of uncertainty. We follow this by introducing types of data, data          collection methods, methods for data visualization and the use of descriptive statistics. The course then       moves on to probability, probability distributions, and several inferential methods. Specific topics include   graphical displays for qualitative and quantitative data; calculation and interpretation of summary statistics; elementary concepts of probability and sampling; random variables and probability distributions; basic       concepts of hypothesis testing, estimation, and confidence intervals; z-tests and t-tests; and correlation and simple linear regression.

STAT 311 is intended as a first course in statistics for students in many different disciplines. This course is often appropriate for students who need a statistics course as a prerequisite for applying to graduate school, or for individuals who want a better command of elementary statistical methods for use at work.

Core Learning Objectives:  by the end of the course, you will be able to

•    summarize single variable data sets by computing summary statistics and by creating appropriate graphs such as histograms, stem-and-leaf plots, and boxplots;

•     interpret summary statistics and visual displays created either by yourself or others;

•     use computed means and standard deviations to apply normal approximation methods;

•     relate data to a standard normal distribution or percentiles when appropriate;

•     plot points and lines to look at relationships between two variables;

•     use software to compute correlation coefficients and relate the correlation coefficient to the effect of regression;

•     use software to perform simple linear regression analysis (and basic multiple regression), including          finding the equation of the regression line, as well as conducting inference related to the regression slope parameter;

•    distinguish between estimation and prediction with respect to linear regression and be able to create confidence and prediction intervals;

•    explain elementary probability rules, and extend these concepts to setting up correct chance models to make a statistical inference;

•     define the concept of uncertainty;

•     use software to compute standard errors and confidence intervals as measures of reliability for parameter estimation;

•     interpret confidence intervals;

•     use software to set up and interpret hypothesis tests, using z-tests, and t-tests, to make inferences about populations based on information from samples;

•     discuss the assumptions for the various tests and when it is appropriate to use each type of test;

•     use software for an introduction to simulation and some basic nonparametric methods; and,

•     collaborate with others to answer some questions based on the analysis of a data set.

Course Organization

The eight lessons in this course cover much of the material throughout Chapters 1 - 25 of the Introduction to Modern Statistics textbook. See the class schedule or Readings pages of each lesson for specific chapters.    We will also introduce some material not covered in detail in the textbook.

There are eight homework assignments (a mix of R and non-R problems), three online quizzes and an online final. Lectures, as mp4 PowerPoint lectures, are part of Lessons 1 – 8. We cover a new lesson each week      except for the week 8 and the last week of classes. You will have homework due these weeks, but no new     material will be introduced. You will be able to work ahead in this class if you want to as several Lessons     will be released from the start. The homework assignments, however, may not be available until a week or   two before their due dates.

The first lecture in Lesson 1 walks through some key pages for navigating the course in Canvas. After finishing reading through this syllabus, watch this first.

About the Lessons

You can stop and start the online lecture presentations when you need to, and easily find where you left off if you need to close out and return later. Each lesson includes mp4 PowerPoint online lectures recorded by        your instructor. Lessons are designed so that you apply new skills as you learn them to complete the               homework and to prepare for the quizzes and final. Some students may find it helpful to read the assigned      pages from your text, then listen to the lecture.

The lessons emphasize understanding the material and the interpretation of results. Example problems are   presented to illustrate concepts and problem-solving. Lessons follow the text; however, some chapters and  sections are skipped and examples not in the text are presented. Also, for a few topics the lectures provide   more detail than the text. A summary of the content of each lesson is posted on the Introduction page linked under Getting Started on the Home page.

Recommended/Reference Reading

The primary textbook for the class is OpenIntro Introduction to Modern Statistics, 1st Edition, by Çentinkaya-Rundel and Hardin. Goherefor a free online version of the book.

The textbook for Lesson 4 is OpenIntro Statistics, 4th Edition, by Diez, Çentinkaya-Rundel and Barr. Go hereto find a free pdf copy of the book.

You may find the two books linked below useful for getting additional help with R.

•   Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. You can link to the online versionhere.

•   Rfor Data Science, locatedhere.

All books listed above have freely available pdf or online versions. All books are available for purchase as well, but this is not necessary.

The textbooks are meant to supplement the lectures. For some topics, it may be important for you to             carefully read the textbook to gain a more in-depth understanding. Many of the chapters contain examples   other than those worked through in the lectures. Going through examples can help further your                     understanding of the assorted topics. Each lesson will outline the main points for each topic, providing         further explanations and examples for more difficult topics. Each student will need to figure out what works best for their learning style. Some will find it better to listen to the lectures first and then go to the book.      Others may find it better to go through the book and then listen to the lectures. To avoid undue frustration,  listen to the lectures and at least scan through the chapters before attempting the assignments.

Homework [read this section very carefully!]

You will have eight homework assignments, one for each Lesson. Check the posted pdf schedule or Live  schedule on the Syllabus page for the due dates. The due dates will also show up on the Assignments page after assignments are published on Canvas.

Assignment due dates lag the lessons, so you have time to watch the videos before doing the assignments.

•    Use RStudio and special Rmd assignment templates for your assignments, unless otherwise noted. For problems that do not require R, you will simply type your answers in the Rmd file. We will give you a couple of tips if you want to include simple equations. For problems that require R code, you will put  your R code in code chunks (explained elsewhere) and then you will type up answers/explanations  outside and after the code chunks. [do not put answers or interpretations before the R code or in the code chunks; all writing goes after any code chunks]

•    Your name must be on your assignment. Remember to change it in the template.

•    Write all answers in complete sentences with context, even if not explicitly stated in the problem.     We are looking for thoughtful answers/interpretations. More is not always better, so think about what you write. You may lose points for writing something irrelevant that happens to be incorrect.

•    Always show your work, even for simple calculations. Just giving an answer, even when correct, results in at least half off.

•    Always given a brief explanation whenever it makes sense, even if not explicitly stated in the problem.

•    Always include units when applicable, even if not explicitly stated in the problem.

•    Rounding matters. Do not simply copy and paste R output into your explanations and interpretations. Use reasonable rounding! [for most problems, 1 or 2 decimal places are adequate/reasonable. However,   when reporting probabilities, use 4 decimal places]

•    RStudio has a spell checker--use it!

•    You will "knit" your Rmd file to HTML, open the HTML file in a browser and then use the browser print functionality to print to a pdf file. NOTE: if using Mac OS, do not save as pdf, as this creates a 1-       page pdf file for the entire assignment. You will upload the pdf file to Gradescope for grading.

•    Homework assignments are equally weighted. We will drop your two lowest homework scores (as percentages).

•    We assign homework as a way for you to reinforce definitions and practice methods taught in this class. Given the size of the class, we only grade a small fraction of the problems from each assignment for      correctness, with the remaining problems being graded for effort/completeness. We will NOT tell you   which problems we will grade for correctness until after the due date.

•    Grading for effort does not necessarily mean full credit just because you write something down. You must demonstrate effort to complete the problem, even though your answer(s) may be incorrect.

•    After the due date, we will post solutions to Canvas. It is your responsibility to check the solutions         against your assignment. Just because you got full credit for a completeness question does not mean your answer is correct. Be sure to attend office hours or post to Ed Discussion if you have any              questions about homework problems after checking the solutions.

•     No late homework assignments accepted! We allow a two-drop homework policy because we will not

give extensions on homework assignments. If you are ill, have a family emergency, or you just have a

busy week, you can use one of your dropped assignments. This may sound harsh, but since we do not    grade all problems for correctness, we must be able to post solutions in a timely manner so students can check their own assignments.

•     Homework assignments often take more time than you think they will, so plan accordingly. Having

coding issues when you leave your assignment to the last minute is not a valid excuse for a late

assignment. To accommodate technical difficulties or “life happens” situations that might arise during, we will accept assignments until noon Pacific Time the day after the original due date/time. Otherwise, the assignment will be late, and you will not be able to upload it, meaning the assignment will receive a

0.

•    You may discuss homework problems with classmates, but all submitted solutions must be your own   writing and your own Rmd file. We will report suspected cheating to the Student Conduct Office and a

score of 0 will be given when cheating is suspected.

•     Do pay attention to the videos that show you how to upload to Gradescope. You must assign each

problem or part of a problem that is in the outline to the page(s) on which that problem exists. [If you do

not paginate correctly, we do not grade your assignment (or the associated problems if it only applies to one or more problems]

•     Summary:  in addition to answers being incorrect, points will be deducted for poor effort, for not

using complete sentences, not showing any work for computational problems, not including

context, forgetting to include units, not providing brief explanations, unreasonable/no rounding,

improper file type uploads, and incorrect pagination.

•    If you put in the effort, you should be able to get close to 100% on homework—the time you spend working on the assignments and asking questions is in your control!

R/RStudio

We will be using R and RStudio this quarter to introduce the use of statistical software. OpenIntro includes R tutorials for you to work through to introduce the various functions that you will need to complete your R      assignments. We reference recommended R tutorials on the Readings page for some Lessons. Please refer to the Resources link in the top row of the Home Canvas page for more details about R and links for installing the software. We provide instructions/links under “Getting Started with R and RStudio on the Canvas            Modules page.

We present R code in some lectures. For any lessons where this applies, an Rmd code file with the code that was used and a knitted html file for that code is included on the Presentations page. You may copy/paste/edit this code as necessary to complete homework assignments. We also provide an Rmd template for each          assignment that will be done in rmarkdown. This template will include an outline for all problems, with R  code chunks as needed. In the homework templates, we provide the code to read in data files. We also           provide some code for parts of other problems.

R has a bit of a learning curve. The R tutorials will provide examples of the functions you need for the        related assignments. You will, however, need to spend some time to learn how R works. You may find that looking at the output after each call helps you to better understand what the code is doing! We   recognize that there will be a variety of programming backgrounds for students in this course, from never   have used software to having used R or other languages. The TA and I will help you with R--you just need to ask.

Quizzes

There are three quizzes that each emphasize material from the previous quiz. The quizzes may include a mix of R and non-R problems. For each quiz you will have a window from 9 AM PDT on quiz day until 11:30    PM PDT the next day to complete the quiz. Each quiz will have two parts:  1) a timed multiple choice or fill in the blank Canvas quiz. For these timed parts, once you start the timer starts and continues to countdown,  even if you exit the program; and, 2) an untimed short answer problems quiz posted in Gradescope. All parts of quizzes are Stat 311 open notes and lectures, Stat 311 open textbook, and Stat 311 open homework and    solutions.

Each final quiz score will be the sum of the scores from the two parts. We will drop your lowest quiz score (as a percentage). No make-up quizzes will be given. All quizzes must be completed on your own, with no discussion with other students or individuals. We will report suspected cheating to the Student Conduct

Office.

Final Exam

There will be one Stat 311 open note/Stat 311 open textbook/Stat 311 open homework and solutions timed   online final that opens Saturday morning after the last day of classes and will be due by 11:30 PM PDT the   Tuesday of finals week. The format will be multiple choice and true/false, with questions that touch on         material in Lessons 1 – 8. No make-up final will be given. You must take the final to get credit for this   class. The final exam must be completed on your own, with no discussion with other students or individuals. If we suspect cheating, you will be reported to the Student Conduct Office.

Course Announcement/Discussion Forum

Use the Ed Discussion board to post questions or comments about the course, including questions from      readings in the textbook, course materials, learning objectives, or other course content. We have populated the Ed Discussion board with categories--please try to use the most appropriate category based on the         content of your post. For guidelines about effective posting on discussion forums, please see theNetiquette information.

Every week, the TA or I will post announcements that are pertinent for all students under the                   “Announcements” category in Ed Discussion. It is important for you to read all these postings—you are responsible for all information posted in the announcements category. Please do not reply to          announcements as we do not monitor the announcements for comments. If you have a

question/comment based on an announcement, please post under the proper thread on the Course Discussion Forum, email me or a TA, or attend office hours.

While we monitor Ed Discussion throughout the day on weekdays, it may take us up to 24 hours to post responses. We may not answer questions posted after 5 PM until the next day. Questions posted over the weekend may not be answered until Monday.

Grades

Your final course grade will be determined by your overall weighted percentage based on the following categories and weights:

You need to take two of three quizzes and the final to pass this course. You may skip two homework            assignments and still pass. Final grades may be based on curved weighted percentages with grades higher     than indicated in the table below, but final grades will never be lower than the rough grade guidelines shown below.

Religious Accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or          significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available atReligious       Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-       policy/). Accommodations must be requested within the first two weeks of this course using theReligious Accommodations Request form (https://registrar.washington.edu/students/religious-accommodations-      request/).

Mental Health Resources

Any member of the UW community can callSafeCampusanytime to anonymously discuss safety and well-   being concerns for yourself or others. Caring, trained professionals will talk you through options and connect you with additional resources if you want them. Available 24/7 by phone at 206-685-7233, or M-F, 8am-5pm atsafecampus@uw.edu.Crisis Connectionsprovides immediate help to individuals, families, and friends of  people in emotional crisis, dealing with addiction, or struggling to meet basic needs. Anyone in Washington  State can receive support and resource referrals 24/7 through their crisis line at 866-4CRISIS (866-427-4747 or TTY 206-461-3219).