---
tags: stat230, syllabus
---

# STAT 230 Syllabus, Fall 2022
**Instructor**: Adam Loy

## How to use this syllabus

This document contains all the information you need to navigate the course. **This syllabus is meant to be read once, then searched as needed.** If you need to find something, use the table of contents found in the left sidebar of the web version. Click "Expand All" to see the subsections in the table. You can also hit `Control-F` on windows or `Command-F` on a Mac to bring up a search field, and then type in the text you are looking for.

+ When you see blue- or purple-underlined text in the syllabus or any other document, it's a clickable link. For example, [click here for a cat video](https://www.youtube.com/watch?v=aFuUidBR1aQ).
+ Links to all important documents and information can be found on the course website. **Please check the course website daily.**
+ The syllabus is available in electronic form and as a PDF. The electronic version will be consistently updated as needed, with updates ==highlighted yellow like this==. *The PDF version will only be updated occasionally and you should use it only for archival purposes.*

## Key Information

**Meetings:** MWF 2a (i.e., MW 9:50-11am, F 9:40-10:40am) in CMC 102.

**Student drop-in hours:** No appointment is necessary for drop-in hours, just stop by **CMC 307**

- Monday 3:30-4:30 pm
- Tuesday 1:30-2:30 pm
- Wednesday 3:30-4:30 pm
- Friday 12:30-1:30 pm

**Schedule an appointment:** Use my [Calendly page](https://calendly.com/aloy-meetings) to schedule an individual meeting time.

**Contact me:** Slack is preferred for conceptual and logistical questions. For personal question (e.g., grade or illness related) e-mail me ([e-mail]). You can also schedule an appointment or come to drop-in hours. Be sure to read [my availability/response policy](https://hackmd.io/@aloy/S1Pb8X6u9#Instructor-availability-and-message-responses).

**Course calendar:** The official course calendar is in [Appendix B](https://hackmd.io/@aloy/S1Pb8X6u9#Appendix-B-Class-Calendar) and on the course website. *In case of a date conflict on assignments or course documents, the Class Calendar is assumed to be correct.*

**Textbook:** The required textbook is *Practicing Statistics: Guided Investigations for the Second Course* by Kuiper and Sklar.

**Videos:** A playlist of instructional videos for the course is available [on Panopto at this link](https://carleton.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=747649c0-5f92-487c-bd59-aeaf010e0afb).

**Software and technology:**
* We will use the R programming language via the **R Studio** IDE for computational work in the course. You can access Carleton’s RStudio Server at https://maize.mathcs.carleton.edu using your Carleton credentials. If you plan to be off campus, then you will need a VPN to access maize.
* The **Gradescope** grading platform will be used to collect and return most graded assignments. Be sure to check Gradescope for feedback on your submissions.
* We will also use **Slack** for course announcements and as a discussion forum.
* Grades will be summarized on **Moodle**.

**Course webpage:** Our course webpage is at [stat230.github.io/fall2022](https://stat230.github.io/fall2022/). Course materials, including notes, daily prep, study guides, and assigments, will be posted here. Check the course webpage daily.

## About the Course

Our world is awash in data. To make decisions based on evidence, we must be able to make sense of the data around us and communicate our findings. In this course, we will explore how to incorporate multiple predictors into regression models to answer complex questions. In addition to learning how to model continuous response variables, we will also explore models for binomial and Poisson counts. This course will emphasize model building, model validation, and how to communicate results. We will make frequent use of R.

Your success in the course depends on three things:

1. **Your willingness to be actively engaged during class time**. All the available research on learning says that the only way to learn is to be an active participant in the process. Active enagement means everything from paying attention, to asking and answering questions during and between classes, and being actively involved during in-class group work. Students who approach the class with a passive mindset typically struggle. Those who approach it with an active mindset, on the other hand, often surprise themselves with how much and how well they learn.
2. **Your willingness to ask questions**. The material in Stat 230 is challenging, and everyone will be lost, confused, and/or stuck at times. When it happens, don't wait for things to make sense on their own: *Ask questions* of me and your classmates and take action to make sense of the material.
3. **Your ability to stay current and maintain awareness in the course**. It's easy to fall behind quickly in Carleton classes and difficult to catch up. To avoid this, check your messages and the calendar at once daily; get to work immediately on assignments; and above all **stay active, ask questions** and **don't procrastinate**.

If you can commit to these three things, then there is every expectation that you'll succeed in the course.

### What do I expect of you?

I expect that you will

- actively engage with course materials and activities;
- come prepared to, and participate during, class. You should gain some command (though not perfect command) of the material from the readings/videos;
- interact collegially and respectfully with other members of the course;
- complete assignments on time and in accordance with the college academic honesty policy;
- seek out help when you run into difficulty or contact me if there are other obstacles that get in the way of your success in this course; and
- check Slack and the course webpage on a daily basis, Monday-Friday.

### What can you expect of me?

My #1 job as the professor is to make sure you succeed in learning. I promise you that I will work to make STAT 230 a class where you can make mistakes and grow safely and productively. I will
- encourage and support your efforts to learn statistics;
- organize class materials and activities to promote the learning goals for the course;
- be available to help you when difficulties arise;
- provide prompt feedback on assignments, and update you on your overall progress;
- keep information up-to-date on the course webpage and grades up-to-date on Moodle;
- check Slack at least three times a day Monday-Friday, and try to respond within 24 hours. I may not check Slack on weekends (so the 24 hour response time refers mainly to weekends).

## Course Goals

The overall goal of STAT 230 is to **introduce you to the statistical modeling cycle**, which will be the foundation for later coursework such as STAT 285 and STAT 330.

### Course-level learning objectives:

Upon completion of STAT 230, you will be able to:

* Given a research goal, identify the parameter(s) of interest and formulate the goal as measurable statements about parameters in a regression model.
* Fit linear, logistic, and Poisson regression models in R and interpret the output.
* Comment on the adequacy of a model for addressing a given question of interest by assessing the assumptions underlying the model.
* Apply appropriate transformations to the response variable to improve agreement with the model assumptions.
* Communicate the results of a regression analysis effectively in written format.

### Course module structure

The course content is split up into four modules:

- **Module 1: From intro stats to modeling**. Expressing two-sample t-tests and ANOVA F tests from intro statistics as linear models
- **Module 2: Linear regression**. Extending the regression model for quantitative responses to include multiple predictors
- **Module 3: Logistic regression**. Building regression models for binary and count data
- **Module 4: Poisson regression**. Building regression models for counts over space or time

The basic skills that you'll learn in the course are encapsulated in a list of **24 Learning Targets**. [You can find that list in Appendix A](https://hackmd.io/@aloy/S1Pb8X6u9#Appendix-Learning-Targets), and it's linked elsewhere on our various course sites.

## STAT 230 Workflow

**Each class meeting has activities for you to do *before*, *during* and *after* the class.** For details on specific assignment types, see the next section.

* **Before class:** You'll be asked to complete a **Daily Prep** assignment that will involve you in reading a section of the textbook and/or watching some of the videos on [our playlist](https://carleton.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=747649c0-5f92-487c-bd59-aeaf010e0afb). You will complete a small set of basic questions and exercises on a Google Form. This way, you'll come to class ready to work, and we can skip the lecturing in class unless it's really needed.
* **During class:** Class meetings will be focused on *answering questions* and *doing active work*, which are intended to help you make sense of the material and ask questions. We'll also use time in class for assessment and for completing other assignments, so that a lot of the "homework" for the class is done during class time.
* **After class:** In addition to getting started on the next Daily Prep, you should expect to spend significant time in between classes doing practice on concepts you don't fully understand yet, and asking questions. There will occasionally be **Case studies** to work on as well. But, much of the work you do outside of class will be on Daily Prep.

## Assessments and Grades

You can only learn by *doing* statistics. The things you will do to learn include many things that are not assessed or graded, along with some items that are formally assessed and graded. These are:

* **Daily Prep assignments**: These lead you through an active reading of portions of the textbook and some of [the videos](). You will complete a short form with basic exercises and questions to assess your preparation.
* **Learning Target completions**: Learning Targets are the basic skills that you should learn in the course. You will be asked to "complete" as many Learning Targets as possible, done mainly by taking short quizzes on them at regular intervals. See "Learning Targets" below for more.
* **Case studies and a final project**: Case studies allow you to apply the skills found in the Learning Targets in a complete analysis cycle. These will not only require you to construct statistical models to solve a problem, but also to communicate your reasoning clearly to an appropriate audience. In a case study, I will provide you with the data, or instructions for data collection. The final project is similar to a case study, but requires that you find your own data set and devise your own research questions.
* **Homework**: You'll have regular homework assignments to provide you with a low-stakes environment to practice key course concepts and to receive feedback.

### How each assignment is graded

| Assignment | How it's graded | What's recorded in gradebook |
| :---- | :------------ | :-------------- |
| Daily Prep | Completeness and effort only | `1` for complete, good-faith effort; `0` otherwise |
| Learning Targets | Completeness and correctness | 0-4 (see below) |
| Case Study and Project Components | Completeness, effort, correctness, and communication quality | 0-4 |
| Homework Problems | Completeness and correctness | 0-4|

[The "Standards for Assessments in STAT 230" document](https://hackmd.io/@rtalbert235/Bys_sKSBc) contains details on the quality standards for each kind of assessment in the course. Please read this carefully and review before each submission you make.

### 4-point scale

Instead of a 100-point scale, we will use a 4-point scale in this course. This may seem weird, but this scale is more accurate and the scores can be viewed as marks (i.e., meaningful labels) rather than some arbitrary percentage of total points. For more information, check out *Grading for Equity* by Feldman.

Each homework question, exam question, and case study/project component will be assessed using the following 4-point scale:


Daily prep assignments will be graded for completeness and effort, resulting in either a 1 or 0. Your daily prep marks will then be converted to the 4-point scale based on the proportion of 1's based on the below thresholds:

Mark | Threshold
:-----:|:--------:
4 | 85%
3 | 70%
2 | 55%
1 | 40%

A 0 will be given if you do not meet the 40% threshold.

### Learning Targets

Throughout the course, you will demonstrate your learning on each [Learning Target](https://hackmd.io/@aloy/S1Pb8X6u9#Appendix-Learning-Targets). The primary way you'll demonstrate your understanding is through **Learning Target quizzes**, given roughly every other week. Each quiz will contain several problems, and each problem covers one of the Learning Targets.

Each quiz will contain problems for each *new* Learning Target along with *new versions* of quiz problems for earlier Learning Targets. **You do not need to do all the problems on each quiz;** just attempt the problems for Learning Targets that you haven't completed yet and feel ready to try. Questions for each learning target will appear on at most two quizzes.

Your work on Learning Target quizzes will be done by hand, then I will scan and upload them to Gradescope for quicker feedback.

**Alternative way of demonstrating skill on Learning Targets:** You can also demonstrate your skill on a Learning Target in the following ways other than quizzes (but will need to spend a token to do so):

- *An oral quiz in appointment hours*. In this option, you schedule time with me via Calendly. During this meeting I will give you a new problem and you work it out "live".

Details on using these alternative assessment methods are found in the [Instructions for alternative Learning Target assessments](https://hackmd.io/@aloy/S1GKxpPlj) document. Other ways of demonstrating skill may be introduced later, especially for Learning Targets appearing near the end of the course.

<!--
### Revising a case study

You will have opportunities to revise your case studies. This will allow you to learn from the feedback given on these assignments and be rewarded for your efforts. This also acknowledges that feedback is important for learning.

You may revise each case study once to improve your score based on the feedback received. You can only increase your score on each component up to a 3 (so you will not receive a score higher than a 3 on any revised case study component).
-->

## Earning a Course Grade

Your grade in the course will be determined by taking a weighted average of the above components and mapping it to a letter grade.

Here are the component weights:

| Course component | Weight |
|:------------------|-------:|
| Daily prep | 5% |
| Homework | 10% |
| Learning targets | 55% |
| Case studies | 15% |
| Final project | 15% |

Your weighted score will be a number between 0 and 4, and will map to a letter grade based on the following thresholds:

Grade | Threshold
------|------:
A |3.8
A- | 3.45
B+ | 3.2
B | 2.85
B- | 2.6
C+ | 2.3
C | 2.0
C- | 1.7
D+ | 1.3
D | 0.9
D- | 0.4

## Academic integrity

You are expected to follow the [policies regarding academic integrity](https://apps.carleton.edu/handbook/academics/?policy_id=21359) established by Carleton College. Any work you submit for a grade should be your own, not a facsimile of a classmate's work or an online solution, which would constitute academic dishonesty. To check if your homework meets this standard, imagine I asked you to explain your reasoning for each problem---you should be able to do so with ease using language similar to your submission. Finally, cell phones are prohibited during learning target quizzes.

Cases of academic dishonesty will be dealt with strictly. Each such case will be referred to the Academic Standing Committee via the Associate Dean of Students or the Associate Provost. A formal finding of responsibility can result in disciplinary sanctions ranging from a censure and a warning to permanent dismissal in the case of repeated and serious offenses.

Here are the specific parameters for how much and with whom you may collaborate on each piece of graded work in the course:

| Assessment | Collaboration allowed |
| :-------- | :--------------------- |
| Daily Prep | You are encouraged to collaborate with others via questions and discussion threads. On the response forms, your work must be your own and reflect your own understanding. |
| Learning Targets | **No collaboration is allowed at all** with other people (including online forums such as Stack Exchange or Reddit). You are allowed to use resources from the class such as the textbook, your notes, and your past quiz work; but getting answers or significant parts of solutions from outside print or electronic resources is not allowed. |
| Case Studies | Each case study will be comprised of group and individual components. You must adhere to the guidelines given for each case study. |
| Homework | You are encouraged to collaborate on these, and you can use outside sources. But your work must be your own and reflect your own understanding (i.e., it can't be copied). |
| Final Project | You are expected to collaborate with your group, but cannot rely on external sources (people or online sources) to guide the *statistical content* of the project. You are allowed to ask for R help in the Stats Lab or to search R-related tutorials. |

## How to Get Help

As mentioned, you will almost certainly find yourself lost, stuck, or confused on *something* in this course. This is not a defect in your character or intelligence; it's a sign you are being challenged, and you can turn that challenge into real growth by **seeking out help as soon as you need it**.

Make every effort to get yourself unstuck and resolve your questions on your own first. But then:

* **Post a question on Slack.**
* **Attend drop-in hours and ask questions there.**
* [Schedule an appointment through Calendly](https://calendly.com/robert-talbert/) if drop-in hours don't work for you.
* **Work with a classmate** especially on Daily Prep, as long as you're [staying within bounds on academic honesty](https://hackmd.io/TchC8fZORJ29EgxQb1O4lQ?view#Academic-integrity).
* **Go to the Stats Lab (CMC 304).** It's a welcoming place that provides drop-in help with R and/or homework questions, and it also functions as a place to study, and to do homework. I will post the hours on the course webpage.

There are also broader campus resources to be aware of:

**Dean of students.** If your personal situation, due to COVID-19 illness or other circumstances, begins to impact your ability to engage with the course, please contact the Dean of Students Office and myself.

**Campus resources on wellbeing**. Your health and well-being should always be your first priority. At Carleton, we have a wide-array of resources to support students. It is important to recognize stressors you may be facing, which can be personal, emotional, physical, financial, mental, or academic. Sleep, exercise, and connecting with others can be strategies to help you flourish at Carleton. For more information, check out [Student Health and Counseling (SHAC)](https://apps.carleton.edu/studenthealth/), the [Office of Health Promotion](https://www.carleton.edu/health-promotion/), or the [Office of the Chaplain](https://www.carleton.edu/chaplain/about/).

## Course Policies

### Classroom culture

All people in this class deserve to feel safe, respected, and valued. That means that all members of our class community are responsible to each other to make sure that all voices get heard, all comments are considered respectfully, and everyone has a chance at success. Determination, cooperation, and hard work are highly valued in this class; helping your neighbor understand the material is more important than trying to be the first to answer. We flourish as a community when every individual participates and learns.

### Attendance, absences, and participation

**Attendance:** Attenance is expected at all class meetings, as I believe it is critical to your learning, but will not be graded.

**Absences:** If you must be absent, you do not need prior permission or justification, but a heads-up is appreciated. However *please avoid any non-essential absences* such as skipping class, or scheduling a trip during a day where class is scheduled. If you miss, you are solely responsible for catching up.

**Missing Learning Target quizzes:** If you miss a class meeting during which there is a Learning Target quiz and don’t make prior arrangements with me (these are clearly marked on the Class Calendar), then there are no makeups offered. Instead, just attempt the problems you intended to take on the next quiz, or through an oral quiz.

**Participation in class:** You are expected to participate actively during each class meeting, but this will not be graded.

### Deadlines and late work

Daily Prep, Homework, and Case Studies all have deadlines. Those are handled in different ways:

+ **Daily Preps** must be turned in by the deadline (typically 11:59 pm the night before class). This deadline may not be extended because of the time-sensitive nature of the assignment.
+ **Case studies** have "soft" deadlines --- suggested deadlines that give you something to put on the calendar and motivate you to complete it. If you intend to turn in a Case Study but don't think you can finish by the deadline, **you can ask for an extension** by filling out the [due date change form](https://forms.gle/HQje5fCvF2bxLvWL6). Tell me what the issue is, and tell me when you plan to turn in the work. Extension **requests must be received before the original deadline, and I have the right to refuse or give you a different extended deadline if the situation warrants**.
+ **Homework** assignments have a deadline posted on Gradescope. **You can request a 48-hour extension for a homework assignment by spending a token**. Just fill out the [Google form](https://forms.gle/HQje5fCvF2bxLvWL6) **before** the deadline and the extension will be granted if you have tokens remaining. *Note:* the extended deadline will be set to 48 hours after the *original* deadline.
+ The **Final project** must be turned in by the deadline according to Carleton policy. No late projects can be accepted unless an extension is approved by the Dean of Students.

### Tokens

Each student starts the term with 4 "tokens" that can be used as follows:
- A 48-hour extension on a homework assignment (the request must be submitted before the deadline)
- An alternative Learning Target question format (e.g., an oral question)

I will track the number of tokens in a Moodle gradebook column.

### COVID-19 related policies

#### What are the health and safety protocols?

Carleton's culture of accountability and respect remains essential as we move into the 2022-23 academic year. We have an obligation to protect one another and the members of the Northfield community, and we all must continue to take that responsibility seriously. To ensure the well-being of each other and the broader community, we will

- follow the College policies on testing, quarantine, and isolation.
- [report positive COVID-19 test results](https://docs.google.com/forms/d/e/1FAIpQLSf-W0_2mei7QQ7pWK4f0kAYTIBB-fHbwnkvdyE54KeRcPWCgQ/viewform) to the College as soon as possible.
- stay home when sick. (Even if you don’t have COVID-19, you should stay home if you aren’t feeling well.)
- wear a mask if you were a close contact, regardless of whether you have symptoms.
- follow the College mask-wearing policy.
- not eat in class while masking is required.

#### What happens if you (the instructor) cannot be in class?

While I am vaccinated, breakthrough infections can still occur. There is a possibility that I will need to miss class this term either due to COVID symptoms or to care for a family member. If I cannot be physically present for a class meeting, then you will receive a notification through both Slack and email on or before that day. In most cases, class will still be held, but in a synchronous online format using Zoom. Please check Slack and email daily.

#### What happens if I (the student) cannot be in class?

Similarly, it is likely some folks will need to miss class for illness throughout the term. Please reach out to me as soon as possible if you need to miss class because of COVID-19 symptoms, the need to quarantine, or the need to isolate so that we can make arrangements for your continued engagement in the course. While each person's needs may be unique, there are a few guiding principles we'll use to help you remain engaged in the course.

- All materials (videos, readings, slides, handouts, etc.), assignments, and announcements will be posted on the course webpage.
- There are options for virtual student hours. In addition, you can set up an appointment via Calendly.
- Attempt the group-work for that day and ask questions as neeeded.

### Instructor availability and message responses

You can ask a question at any time. You can use Slack (preferred), email ([e-mail]), drop-in hours (either in person or virtual), talk to me after class, or make an appointment. Slack is usually the best venue for conceptual questions since someone likely has the same question.

Please note that I do not always respond immediately to messages. I do not typically check email or Slack messages between 9 pm and 7 am on weekdays, and I check them infrequently over the weekend in order to devote time to family, rest, and finding balance. Messages received during these times will receive attention once I am back online. Otherwise you can expect to receive a response to your message within a few hours, often much sooner. If you post questions to Slack, you are likely to receive responses faster.

### Accommodations and assistive technology

Carleton College is committed to providing equitable access to learning opportunities for all students. The Office of Accessibility Resources (Henry House, 107 Union Street) is the campus office that collaborates with students who have disabilities to provide and/or arrange reasonable accommodations. If you have, or think you may have, a disability (e.g., mental health, attentional, learning, autism spectrum disorders, chronic health, traumatic brain injury and concussions, vision, hearing, mobility, or speech impairments), please contact [e-mail] or call Sam Thayer (’10), Director of the Office of Accessibility Resources (x4464), to arrange a confidential discussion regarding equitable access and reasonable accommodations.

The Assistive Technologies program brings together academic and technological resources to complement student classroom and computing needs, particularly in support of students with physical or learning disabilities. Accessibility features include text-to-speech (Kurzweil), speech-to-text (Dragon) software, and audio recording Smartpens. If you would like to know more, contact [e-mail] or visit go.carleton.edu/aztech

### Course materials assistance

I recognize the potential financial burden of textbook costs. If you are in need of assistance to cover course expenses, please speak with me by the end of Week 1.

## Appendix A: Learning Targets

There are 24 Learning Targets in the course overall. These represent the basic concepts that you can learn in the course.

### Connections from intro

- C.1: I can write down a model (two-sample, regression, or ANOVA) to compare group means.
- C.2: I can interpret the model parameters for a model used to compare group means.
- C.3: I can draw inferences for the coefficients of my model and report the results in context.
- C.4: I can check the necessary model assumptions and discuss the implications of a model deficiency.

### Linear regression

- LINEAR.1: I can express a linear regression model using proper notation.
- LINEAR.2: I can interpret the coefficients of a linear regression model in context.
- LINEAR.3: I can check necessary conditions for linear regression and discuss the implications of a model deficiency.
- LINEAR.4: I can run a hypothesis test to draw inference about a single regression coefficient.
- LINEAR.5: I can run a hypothesis test to compare nested regression models.
- LINEAR.6: I can construct and interpret confidence intervals for a single regression coefficient.
- LINEAR.7: I can use a regression model to make predictions about group means and individual observations and communicate the uncertainty of these predictions through an interval.
- LINEAR.8: I can interpret the coefficients of a linear regression model when transformations have been used on the response and/or the explanatory variables.

### Logistic regression

- LOGIT.1: I can express a logistic regression model for binary or binomial data using proper notation.
- LOGIT.2: I can interpret the coefficients of a logistic regression model in context.
- LOGIT.3: I can check necessary model conditions for logistic regression and discuss the implications of a model deficiency.
- LOGIT.4: I can run a hypothesis test to draw inference about a single logistic regression coefficient.
- LOGIT.5: I can run a hypothesis test to compare nested logistic regression models.
- LOGIT.6: I can construct and interpret confidence intervals for a single coefficient in a logistic regression model.

### Poisson regression

- POIS.1: I can express a Poisson regression model for count or rate data using proper notation.
- POIS.2: I can interpret the coefficients of a Poisson regression model in context.
- POIS.3: I can check necessary model conditions for Poisson regression and discuss the implications of a model deficiency.
- POIS.4: I can run a hypothesis test to draw inference about a single Poisson regression coefficient.
- POIS.5: I can run a hypothesis test to compare nested Poisson regression models.
- POIS.6: I can construct and interpret confidence intervals for a single coefficient in a Poisson regression model.

## Appendix B: Class Calendar

The calendar can be accessed directly [at this link](https://calendar.google.com/calendar/u/0?cid=Y19kczI1aDB2dDAycHA0ZWxsNWhvYWRlZWE2b0Bncm91cC5jYWxlbmRhci5nb29nbGUuY29t); it is also available on the course webpage.

<iframe src="https://calendar.google.com/calendar/embed?src=c_ds25h0vt02pp4ell5hoadeea6o%40group.calendar.google.com&ctz=America%2FChicago" style="border: 0" width="800" height="600" frameborder="0" scrolling="no"></iframe>