代写STAT 311: Elements of Statistical Methods Spring 2024帮做Python语言

STAT 311: Elements of Statistical Methods

Spring 2024

Course Description

STAT 311 is a modern introduction to the discipline of statistics. Students are immersed in realistic data-driven tasks from the start of the quarter and will learn to navigate their way using a mix of statistics, computer literacy, and last but not least, good old-fashioned common sense.

Course Objectives

At the end of this course, students should be able to:

1. Identify limitations in data collection methods and explain how this limits the scope of in- ference.

2. Use the programming language R to summarize patterns in data visually and numerically.

3. Explain the unifying logic of statistical inference.

4. Apply estimation and testing methods to analyze single variables, and also the relationship between a numerical response and a binary predictor.

5. Model a numerical response using a numerical and categorical predictors. 6. Make data-based decisions.

Required Materials

• Intro to Modern Statistics, 1st edition (available as a free PDF)

• Laptop with reliable internet access

– The Student Loan Tech Programis a great resource in case you do not have access to a laptop.

Course Structure

The course will cover six themed units as detailed below:

• Introduction to data

• Summarizing data

• Linear Regression

• Foundations for inference

• Inference for categorical data

• Inference for numerical data

Roughly speaking, each Wed/Fri class will involve presentation of new material by the instructor followed by guided practice or live coding. The Tue/Thur sections will be run as computer labs facilitated by the TAs. Students are expected to attend class/section so they can meet their peers and get an early start on forming groups for the final project.

Course Communication

Given the large number of students in this class, I ask that questions asking for clarification on the course material/labs be posted on Ed discussion. The TAs and I will monitor the discussion board and will be happy to answer your questions there.

If your question is of a personal, sensitive nature, you may contact theTA in charge of your section or the instructor. Contact information for the TAs is below.

• AA/AB: Jess Phillips ([email protected])

• AC/AD: Alfredo Effendy ([email protected])

• AE/AF: Jerry Wei ([email protected])

Grading

There will be no timed tests in this class. Your grades will instead be determined by your perfor- mance on the lab assignments (70%), in class participation (10%) and a group project (20%). Note that lab assignments will become increasingly open ended as we progress through the quarter, and students will need to stretch their technical skills and creativity in order to earn full credit.

All questions regarding grading of lab assignments should be brought in person to your TA or the instructor within a week after they are graded.

Final grades are determined based on the following benchmarks:  4.0 ≥ 98%, 3.5 ≥ 90%, 3.0 ≥ 85%, 2.0 ≥ 80%, 1.5 ≥ 75%, 0.7 ≥ 68%.  These are minimum guarantees.  Your grade could be higher than the scale suggests. In other words, it is still possible to get a 3.5 even if your percentage is less than 90%, we just can’t make you a guarantee that will happen.

Course Policies

• Attendance Please make every attempt to regularly attend and participate in class and sec- tions. STAT 311 is fast paced, we cover a lot of material and it is easy to fall behind.  I will assign an activity for participation credit once/week during either the Wed or Fri class.  It will be due before the start of Wednesday class in the following week. As a courtesy, I have enabled Lecture Capture in CSE2 G20 so that the Wed and Fri classes should be automati- cally recorded. However, I cannot guarantee the audio or visual quality of these recordings. Sections will not be recorded and the expectation is that you will attend these smaller group meetings in person every week.

• Late assignments will only be accepted provided the instructor is notified in advance of the due date and it is approved. This means that we will not be responding to last minute requests for extensions due to unanticipated coding issues. Please start your work early and get help sooner rather than later.

• Academic Accommodations: Your experience in this class is important to me.  If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course.

• Academic integrity is essential to this course and to your learning.  Violations of the aca- demic integrity policy include but are not limited to:  copying from a peer, collaborating where it is not allowed, copying from an online resource, using a solutions manual, us- ing resources from a previous iteration of the course, and not contributing equally to the group project.  If you are unsure about whether a particular action would be construed as academic misconduct, please ask. Anything found in violation of this policy will beautomat- ically given a score of 0 with no exceptions. If the situation merits, it will also be reported to the Office of Community Standards and Student Conductfor investigation.

• 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 con-science, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy). Accommodations must be requested within the first two weeks of this course using the Reli-gious Accommodations Request form. (https://registrar.washington.edu/students/religious- accommodations-request/)

• Safety and Health Take care of yourself. Do your best to maintain a healthy lifestyle this quarter by eating well, exercising, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. All of us can benefit from support during these times of struggle. You are not alone. Asking for support sooner rather than later is often helpful.

Tentative Course Schedule

Week 01, 03/25 - 03/29: Preliminaries

• Wed: Introductions and welcome!

• Thur: Lab 1: Hello World!

• Fri: Data Basics (whickham-confounding live-coding)

Week 02, 04/01 - 04/05: Introduction to Data

• Tue: Lab 2: Data Basics (arbuthnot)

• Wed: A grammar of data wrangling (gapminder live-coding)

– Diagnostic quiz & Participation 1 is due

• Thur: Keep working on labs 1 and 2

• Fri: More on grammar of data wrangling (back to gapminder live-coding)

Week 03, 04/08 - 04/12: Exploring Data

• Tue: Lab 3: What will you major in? (college_recent_grads)

–  Labs 1 and 2 due by 3:30 PM

• Wed: Visualizing numerical data (ggplot learnr tutorial )

–  Participation 2 is due

• Thur: Lab 4: NYC flights (nycflights)

• Fri: Summarizing numerical data (gapminder learnr tutorial)

Week 04, 04/15 - 04/19: Summarizing Data

• Tue: Lab 5: Airbnb listings in Edinburgh (airbnb)

–  Labs 3 and 4 due by 3:30 PM

• Wed: Considering categorical data (accidents learnr tutorial)

–  Participation 3 is due

• Thur: Lab 6: Building a spam filter (email)

• Fri: Regression modeling: single predictor (paris_painting learnr tutorial)

Week 05, 04/22 - 04/26: Linear regression

• Tue: Lab 7: Human freedom index (hfi)

–  Labs 5 and 6 are due by 3:30

• Wed: Regression modeling: one numerical and one categorical predictor (evals)

–  Participation 4 is due

• Thur: Lab 8: Votes and IMDb ratings (office_data)

• Fri: Regression modeling: multiple predictors

Week 06, 04/29 - 05/03: Linear Regression

• Tue: Group check in for proposal

–  Labs 7 and 8 due by 3:30

• Wed: Regression modeling: multiple predictors (evals)

• Thur: Group check in for proposal

• Fri: Regression modeling: model validation

Week 07, 05/06 - 05/10: Foundations of Inference

• Tue: Lab 9: Zagat ratings (nyc_italian)

–  Project proposal is due by 3:30

• Wed: TBD

–  Participation 5 is due

• Thur: Lab 10: The office revisited

• Fri: Quantifying uncertainty (gss tutorial)

Week 08, 05/13 - 05/17: Foundations of Inference

• Tue: Lab 11: Smoking while pregnant (ncbirths)

–  Labs 9 and 10 due

• Wed: Hypothesis testing with simulation

–  Participation 6 is due

• Thur: Lab 12: TBD

• Fri: Hypothesis testing with randomization

Week 09, 05/20 - 05/24: Statistical Inference

• Tue: Lab 13: Birth weight and smoking (ncbirths2)

–  Labs 11 and 12 due

• Wed: TBD

• Thur: Start work on project

• Fri: No class

Week 10, 05/27 - 05/31: Project week • Mon: UNIV holiday

• Tue:

–  Lab 13 due • Wed:

• Thur: • Fri: