代做W25 STATS 250 – Introduction to Statistics and Data Analysis代做留学生Matlab编程
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SECTION A: COURSE OVERVIEW
A1 Course Description
Stats 250 is a one-term, algebra-based introductory statistics course that introduces students to the investigative process of statistics so they may become critical consumers of statistical results and claims. Students will learn to graphically and numerically summarize data and use their understanding of variability to make inferences from a sample to its associated population.
A2 Learning Objectives
After taking Stats 250 you will be able to:
● Think critically about quantitative information in your everyday life, including asking questions about and evaluating the origins of data, analysis of data, and interpretations and real-world decisions based on data.
● Translate research questions into appropriate statistical procedures and use data to address those questions.
● Understand that variability is a natural part of the scientific process and how variability affects data that we analyze in everyday life.
● Effectively communicate statistical ideas to a non-statistical audience.
● Use a statistical programming software R via RStudio to perform basic data analysis and use data to investigate scientific questions.
A4 Required Resources
Required 1: STATS 250 Winter 25 Interactive Course Pack: We do not require students to purchase a publisher-developed textbook but do require students to always bring to class a course pack we have written to integrate into lecture meetings. This class moves quickly and students sometimes express difficulty copying down relevant information from scratch. The course pack provides partially filled-in notes, activities, examples, and supplemental readings, allowing students to focus only on writing the most important concepts. Students must bring the course pack to class. We provide two options for students:
1. Hardcopy ($33.22): We work with a publisher to print the entire course pack at a discounted price, cheaper than if students elected to print out the course pack themselves. You can purchase the course pack and have it shipped to a street address of your choosing atthis link<https://coursepack.allegra.net/category/stats250100-400>.
2. Digital: Students with access to a tablet and stylus can elect to download a PDF of the full course pack from our class Canvas page and annotate the digital course pack with a PDF editor of their choosing.
Required 2: Gradescope ($0 free): All course-related assignments are submitted, evaluated, and returned to students through
Gradescope. To create your Gradescope account for this course, visit the course Canvas site and click on the `Gradescope` link from the left-hand navigation bar. Thereafter, you’ll be auto-enrolled in our course sites (no join code needed).
Required 3: Posit.Cloud Account ($0 free) Refer to the Lab 0 assignment on Gradescope for instructions on creating an RStudio
Cloud account. You can only create an account on the Stats 250 Posit.Cloud workspace by following the steps outlined in Lab 0.
A5 Additional Resources
● ECoach for Stats 250: ECoach is a free, personalized, web-based coaching tool aimed at helping you do your best in this
course. ECoach gives you strategies about the best ways to study, insider tips on course resources, feedback on your scores,
and evidence-based tools to boost your scores. Sign up with your UM email address:https://ecoach.ai.umich.edu/
● Piazza: An online forum where students can submit questions that are reviewed by the instructional team before being
posted for everyone to see. Once a question is posted, students are encouraged to answer and discuss it, helping each other and building a virtual community of learning. The purpose of Piazza is to create a collaborative environment where everyone can contribute to deepening their understanding of the course material. You can link to Piazza through Canvas.
● Office Hours: Office hours are available throughout the week, following the office hours schedule we will post at the start of the 2nd week of the semester. In-person office hours are typically held in Angel Hall G219; we occasionally offer remote office hours via zoom. In special circumstances, your professors can also arrange office hours by appointment. Office hours are open to everyone, whether you have specific questions or not. Make it a habit to attend regularly–get the support you need to succeed in the course!
5.1 Mental Health and Wellbeing
The University of Michigan is committed to advancing the mental health and wellbeing of its students. If you or someone you know needs support, services are available. For help, contact Counseling and Psychological Services (CAPS) at (734) 764-8312 and
https://caps.umich.edu/during and after hours, on weekends and holidays. You may also consult University Health Service (UHS) at
(732) 764-8320 andhttps://www.uhs.umich.edu/mentalhealthsvcs, or for alcohol or drug concerns, see
www.uhs.umich.edu/aodresources.
5.2 Disability Statement
The University of Michigan recognizes disability as an integral part of diversity and is committed to creating an inclusive and
equitable educational environment for students with disabilities. Students who are experiencing a disability-related barrier should
contact Services for Students with Disabilities (SSD)https://ssd.umich.edu/; 734-763-3000 or [email protected]. For students
who are connected with SSD, accommodation requests can be made in Accommodate, a new platform recently adopted on campus. If you have just started working with SSD and plan to get documentation, please email [email protected] as soon as possible.
Due to the time necessary for instructional staff to make appropriate arrangements to ensure accommodations are applied, 3 weeks in advance notice of testing accommodations is requested. If testing accommodations are not received by the course instructors
within 14 days of an exam, we cannot guarantee those accommodations can be facilitated.
In rare cases, we acknowledge the need for a testing accommodation may arise within a shorter turnaround time (example: a
broken wrist). In these cases, the student should contact the SSD office and work with their instructor to determine what is possible, knowing that not all accommodations can be provided on short notice.
If you have any questions or concerns, please contact your SSD Coordinator or visit SSD’s Current Student webpage. SSD considers aspects of the course design, course learning objects, and the individual academic and course barriers experienced by the student. Further conversation with SSD, instructors, and the student may be warranted to ensure an accessible course experience.
A6 Engaging with the Material
● Attend Lectures Regularly: Lectures introduce key concepts essential for your understanding of the course. Here are a list of study strategies that will help you succeed in the learning process:
o Follow along with the interactive lecture notes: Use the provided notes during lectures to actively engage with the material. The notes are designed to keep you on tract with the fast pace of the class.
o Participate in the “Try It” examples: Engage with classmates to work through the “Try It” examples scattered throughout the lectures. These exercises reinforce your learning and provide real-time practice.
o Summarize key ideas: At the end of each lecture, you will find a space to summarize the key ideas covered. Devote a few minutes soon after the lecture to reflect on these key concepts and jot down any questions you still have.
You can post your questions on Piazza or visit office hours to get clarifications. One of the challenges in this course is learning how to learn effectively and discovering study techniques that work best for you. Regularly engaging
with these prompts will help you build skills while reinforcing the material.
● Weekly Labs: Labs are designed to help you apply concepts learned in lecture to real-world data using R. GSIs will guide you through analyzing real-world data sets in R, focusing on applying statistical methods and interpreting results. Your first lab will meet the week of January 27.
o Preparing for Lab: Labs assume you have attended lectures, as they focus on application and analysis rather than re-teaching lecture content. The key to getting the most out of lab is to come prepared by reviewing the relevant lecture material beforehand.
● Ask Questions and Contribute to Discussion: There are several opportunities to ask questions and clarify your
understanding, whether during lectures, in labs, during office hours, or on Piazza. Actively participating by asking questions and engaging in discussion with your peers is key to deepening your understanding of the material.
A7 Tips for Succeeding in Stats 250
The full instructional team is here to help you navigate the course and successfully complete it. Some helpful tips:
● Set a general study schedule for each week. It’s easier to block off time for each class than to juggle your work without a general plan.
● Keep up with lectures. Do not substitute labs for lectures. Labs are meant to provide practice with using some features of R to visualize and analyze real data. Lab instructors will assume students are keeping up with lecture material prior to attending lab.
● Start your homework early and ask questions when you have them. Ask questions when you have them.
● Participate in class by asking and/or answering questions during lectures, lab meetings, office hours, or through piazza.
Contact members of the instructional team (GSI and lecture instructors using email) if you are having difficulties (earlier, rather than later).
SECTION B: GRADING POLICIES
Your overall grade in Stats 250 is determined by the three components: (1) Exams and Lecture Assignments, worth 50% of your overall course grade; (2) Weekly Lab Assignments and Case studies Write-Ups, worth 40% of your overall course grade; and (3) Weekly Homework Assignments, worth 10% of your overall course grade.
B1 Exams and Lecture Work: 50% of your overall course grade.
Together, exams and lecture work total 50% of your overall course grade. Students can choose between two options for precisely how this contribution is tabulated. The ‘Traditional’ approach weights exams at 50% and lecture work at 0%; the ‘Active’ approach weights exams at 40% and lecture work at 10%.
1.1 Exam Information
Exam dates and times are common across all lecture sections; 100, 200, 300, and 400. You will have 80 minutes to complete exams which are proctored in-person, on paper, in a closed-notes format. Calculators are permitted, but not required on exams. NOTE: If
you are entitled to extended time or reduced distraction testing environments, please see our policy on submitting accommodations in section 5.2, above. You must take both Exam 01 and Exam 02 to complete the class.
Exam 01 – Tuesday, February 25th
Exam 01 covers lectures 01 – 11, proctored at 6:00 PM on February 25th. Students can bring a calculator if they would like to but will not need one to be successful on the test. Roughly one week before the exam, the instructional team will post a practice exam and review packet students can use to assist in studying. Additionally, the lab just prior to the exam will be used as an additional review session.
Exam 02 -- Thursday, April 24th
Exam 02 covers lectures 12-24. Although this exam is technically non-cumulative, many concepts in STATS 250 do build off one
another. Like Exam 01, this is a closed note, pen-and-paper exam, proctored at 7:30 PM on April 24th. Review materials will be
provided roughly one week prior to Exam 02. [NOTE: Although the Registrar allocates two hours for final exam proctoring sessions, Exam 02 is only 80 minutes long, just like Exam 01.]
1.2 Lecture Approach Options
Here’s how it works: the semester is divided into two blocks of lectures. At the start of each block, students will be asked to choose between two options outlined below:
● Option 01: Traditional Lecture
o Attendance: Not required. Students can keep up with the lecture material by attending lectures or reviewing recordings.
o Lecture work: Not required. Lecture pre-work and group work are not required.
o Keep up with material: Attend lecture in person or watch the recording at your convenience. Keep in mind that
students should be caught up with lecture-related content before beginning any lab or HW assignments due for a given week. Plan accordingly!
o Grading Impact: Exams are more heavily weighted (each exam is 25% of your overall grade).
● Option 02: Active Learning Lecture
o Attendance: Required in person. Students can keep up with the lecture material by attending lectures and accessing lecture recordings.
o Lecture work: Required. Students complete two low-stakes assignments associated with each lecture.
. Prework Assignments: Students complete a brief 5-10 minute assignment before each lecture. These
assignments are designed to familiarize students with key ideas in an upcoming class meeting or to review key ideas from a recent one.
. Group Work Assignments: During the last 30 minutes of each lecture meeting, students will work with 2-3 of
their peers (i.e., in teams of up to 4) to complete and submit a set of exercises by the end of each lecture.
These exercises are designed to give students immediate practice with concepts and skills learned during a lecture meeting.
o Graduate student instructors and undergraduate instructional assistants will be present in lectures to help facilitate group work.
o Lecture work (pre-work and group work) provides low-stakes opportunities for students to practice recent content; these assignments contribute to the overall course grade.
o Grading Impact: Exams are weighted less heavily (each exam is 20% of your overall grade) and lecture work assignments are factored into your overall grade (1% prework per block; 4% group work per block).
1.3 Choosing your approach
Students will be asked to choose between these two options twice during the semester. Here's how the decision will be factored into the overall course grade:
● Block 01: January 22 – March 11 (11 lectures) - Decide between option 01 and option 02 by Thursday, January 16 at 8 pm (see Gradescope to select)
o If option 01 (traditional lecture) is selected:
. Lecture pre-work and group work for block 01 will be worth 0% of your overall course grade . Exam 01 will be worth 25% of your overall course grade
o If option 02 (active lecture) is selected:
. Lecture pre-work for block 01 will be worth 1% of your overall course grade, and group work for block 01
will be worth 4% of your overall course grade
. Exam 01 will be worth 20% of your overall course grade
Note: After Thursday, January 16, the lecture format option for block 01 will be set and cannot be changed. If no option is selected by January 16, then option 01 (traditional approach) will be used as the default option (this applies to all waitlisted students).
● Block 02: March 12 – April 17 (11 lectures) - Decide between option 01 and option 02 by Tuesday, March 11 at 8 pm (see Gradescope to select)
o If option 01 (traditional lecture) is selected:
. Lecture pre-work and group work for block 02 will be worth 0% of your overall course grade . Exam 02 will be worth 25% of your overall course grade
o If option 02 (active lecture) is selected:
. Lecture pre-work for block 02 will be worth 1% of your overall grade, and group work for block 02 will be worth 5% of your overall course grade
. Exam 02 will be worth 20% of your overall course grade
Note: After Tuesday, March 11, the lecture format option for block 02 will be set and cannot be changed. If no option is selected by March 11, then option 01 (traditional approach) will be used as the default option (not the block 01 lecture format option)
B2 Labs and Case Studies: 40% of your overall course grade.
2.1 Overview of Labs and Case Studies: Exploring Real Data with R
During lab, we will delve into the exciting world of data analysis using R, a powerful tool for statistical computing and graphics.
These lab sessions are designed to complement and enhance your understanding of the lecture material, offering you hands-on
experience with real-world data and practical applications. While lab attendance is not mandatory, we strongly encourage you to actively participate in lab discussions. Attending labs will enhance your grasp of the material and offer practical insights that
contribute to your overall success in the course. Your first lab will meet the week of January 27th.
2.2 Expectations for Lab Preparation
To make the most of your lab session, it is essential to come prepared. Keeping up to date with the lecture material is a vital part of effective lab preparation. The lab assignments will build upon concepts discussed in lectures, and your familiarity with these topics will enable you to fully engage in the lab activities and discussions.
2.3 Purpose of Lab Assignments
Our lab assignments are carefully designed to provide you with opportunities to apply concepts covered in lectures to real data scenarios. By engaging in these lab activities, you will strengthen your analytical skills, gain proficiency in R, and develop deeper comprehension of the subject matter, all while gaining valuable skills that can be applied to future research or professional
endeavors.
Note: All lab assignments are submitted via Gradescope. It is incumbent upon students to verify they have uploaded the intended file in the correct format before the stated deadline. Leniency will not be provided to students who mistakenly upload an incorrect or corrupted file and then request a re-evaluation after an assignment deadline. Submissions will not be accepted via email under any circumstances, barring explicit directions from a member of the instructional team.
2.4 Lab Options
The course includes six labs leading to three major case study assignments. Like with lecture participation, you can elect how you
would like your lab work to be evaluated. You can choose between two lab options, labeled the Traditional Lab and Active Lab
options. The Traditional Lab option benefits from increased flexibility when it comes to attending in-person lab meetings; the Active Lab option benefits from the option to work together and submit in pairs. The options also differ in terms of how lab and case study submissions are weighted.
Lab option 01: Traditional Lab
- Attendance: Not required. Work at your own pace by watching lab videos or attending lab meetings in person.
- Grade impact:
- Lab work: 5% of overall course grade.
- Case Study 01: 8% of overall course grade.
- Case Study 02: 12% of overall course grade.
- Case Study 03: 15% of overall course grade.
- Work submission: all lab and case study assignments must be submitted individually.
- Commitment: This decision is independent of the lecture format you select. Students will need to make a lab format commitment by Thursday, January 30th at 8 pm (see Gradescope to make a selection). Once the lab format selection is made, you cannot switch to the other lab option. Your selection applies for the entire semester.
Lab option 02: Active Lab
- Attendance: Required in person.* (see note below)
- Grade impact:
- Lab work: 10% of overall course grade.
- Case Study 01: 7% of overall course grade.
- Case Study 02: 10% of overall course grade.
- Case Study 03: 13% of overall course grade.
- Work submission: lab and case study assignments can be submitted in pairs. Paired case study submissions (but not labs) are eligible for +2 bonus points.
- Commitment: This decision is independent of the lecture format you select. Students will need to make a lab format
commitment by Thursday, January 30th at 8 pm (see Gradescope to make a selection). Once the lab format selection is made, you cannot switch to the other lab option. Your selection applies for the entire semester.
*Note that, in addition to 1 guaranteed drop throughout the semester, we will be implementing the following attendance policy for students who have selected the Active Lab approach:
Active Lab students are afforded one excused absence per semester. If a student cannot attend lab in a given week, they are welcome to complete and submit the associated lab assignment outside of their lab for full credit evaluation. Any
additional lab absences will result in a 10% penalty on lab/case study assignments submitted that week.
As an example: There are 8 required lab meetings Active Lab students must attend this term. Suppose an Active Lab student misses two of these lab meetings, but still completes and submits the associated lab assignments. The first will be evaluated for full credit, but the second will be evaluated with a 10% penalty. Then, at the end of the semester, the guaranteed drop will be applied to the lowest score across all lab assignments submitted across the entire semester.
Late submissions for lab assignments: We offer a 1-hour late submission window for all lab assignments without any penalty. No submissions are accepted after this 1-hour extension. Students are responsible for ensuring they have uploaded the intended file in the correct format before the deadline.
Late submissions for case study reports: We offer a 1-hour late submission window for all case study assignments without any penalty. Thereafter, we offer an additional 23-hour late submission window at a 10% penalty to the student’s overall grade (e.g., a case study that is submitted 3 hours late cannot earn above 90%). Students are responsible for ensuring they have uploaded the intended file in the correct format before the deadline.