Reading Assignments

Instructions

These instructions are a little long, but please read them carefully at least once. It is your responsibility to know what’s written here to ensure that you receive credit!

Reading Assignments (RAs) are labeled by a week number and a day; for example, the first one is labeled “w1wed,” which is short for “Week 1 Wednesday.” Each RA is due by 7am PT of the day indicated on its label. Each RA has three parts:

1. Read the indicated reading material.

   • The reading material will usually be from DVB (Stats: Data and Models, 5th edition, by De Veaux, Velleman, and Bock) or OS (OpenIntro Statistics, 4th edition, by Diez, Çetinkaya-Rundel, and Barr). Occasionally, you will be asked to read something different (either in addition or instead).

   • We will discuss the content of the reading in class on the day indicated by the label. In particular, this means you will be submitting RAs before we’ve discussed this material together in class. Until then, feel free to chat with your classmates and to supplement your reading with any other resources you find. Doing all of that is part of the point of the RAs!

2. Submit the indicated Comprehension Check (CC) exercises through Gradescope.

   • You’ll get credit for sincere attempts. You don’t need to get answers correct! If you find yourself very stuck, you can still demonstrate a sincere attempt by writing a few sentences explaining what you’ve tried, where you got stuck, etc.

   • Conversely, you won’t get credit if your work doesn’t demonstrate a sincere attempt: leaving things blank or just drawing a giant question mark, transcribing solutions verbatim from the back of the book, etc.

   • The exercise numbers below always always refer to the end-of-section “Exercises,” in both OS and DVB (in particular, they do not refer to the “Guided Practice” problems in OS).

   • There are also some Further Practice (FP) exercises listed below. These are optional and do not need to be submitted. They are recommended for additional practice, and exam problems might resemble some of these problems.

3. Post a “Reading Question” (RQ) to our class Zulip stream.

   • Your RQ should be something that shows sincere engagement with the content of the reading material. Here is a non-exhaustive list of examples of such things:

     • Asking a question about something you found confusing or intriguing in the indicated sections.
     • Asking a question about a CC exercise from the indicated sections that you struggled with.
     • Responding to a question that someone else posted about the indicated sections.
     • Sharing something that you found confusing at first about the indicated sections but then managed to figure out.
     • Sharing a list of what you thought were the salient points from the reading.

     If your RQ is a question, formulate as precise a question as you can. For example, if you’re asking about an exercise, say more than just “I don’t know how to do this” or something like that; share something specific about what you’ve tried and what went wrong. Note that you can typeset math, share code, and upload images on Zulip.

   • To indicate that your Zulip post is intended to be an RQ, the “topic” for the post must contain the assignment label in square brackets, and should also include something to indicate about what your RQ is about. If your RQ is starting a new topic, make sure to follow these rules!

     For example, if your RQ for Week 5 Monday is a question about the normal approximation for the binomial distribution, you might set the topic to something like [w5mon] normal approx for binomial.

     If your RQ is a response to a topic started by someone else which already contains a valid square-bracket label, no further action is required on your part: your response will automatically count under the same label also.

   • As mentioned earlier, your RQ must be something that shows engagement with the content of the current reading. You’re certainly welcome to ask questions and make comments of other sorts on Zulip (eg, questions about class or exam logistics, or questions about previous readings), but please do not label them as RQs. If me or a TA reacts to your proposed RQ with an octopus emoji, it means your proposed RQ doesn’t demonstrate sincere engagement with the content of the current reading and it won’t count for credit.

   • A bot will be used to tabulate credit for RQs. If you don’t follow the topic labeling instructions above precisely, the bot might not recognize your post as an RQ and you won’t get credit for it. Also, the bot will mark anything that comes in after the deadline as late, even if it comes in just one second after the deadline, so make sure to get things in on time! Instructions for checking your RQ score with the bot are available on our Zulip stream.

   • Note that, if you start digging through the settings, you’ll notice that Zulip gives you flexible privacy options regarding who can view your email address. You must make your email address visible at least to “Administrators” in order for us to be able to match your scores to you using your email address at the end of the quarter!

   • You don’t need to post an RQ every time in order to get full credit on this portion of your grade. See the syllabus for details.

   • For more information about Zulip, see the opening post of our class stream.

w1wed

Topic: Data Basics

Read: VM03: “Statistics and Ethics” by Vardeman and Morris.

• This article is now about 20 years old, and it is directed primarily at early graduate students in statistics. Nonetheless, it’s worth reading and giving some thought to. Even if there are parts that don’t feel directly relevant to you and your plans in life, might there be some underlying principles that would be relevant for you? Bring some thoughts along these lines to class with you!

Read: (DVB) Chapters 1 and 10–11; or, (OS) Chapter 1.

CC: (OS, Chapter 1) 1.1, 7, 13, 17, 29

FP:

• (OS, Chapter 1) 1.3, 5, 9, 11, 12, 19, 21, 22, 27, 28, 33, 34, 36, 40, 43, 44
• (DVB, Chapter 1) 1.3, 9, 11, 13, 27–40; (Chapter 10) 10.13, 15, 23, 31, 45; (Chapter 11) 11.15, 17, 21-33 odds, 61

w1fri

Topic: Summarizing Data 1

Read: (DVB) Chapters 2–4; or, (OS) Sections 2.1–2

CC: (OS, Chapter 2) 2.1, 5, 9, 23

FP:

• (OS, Chapter 2) 2.3, 6, 10–13, 17, 19, 20, 24
• (DVB, Chapter 2) 2.33, 35, 40, 41, 47, 49, 63, 65; (Chapter 3) 3.11, 19, 21, 24, 29, 49; (Chapter 4) 13, 17, 23, 31, 35, 45, 51

w2mon

Topic: Summarizing Data 2

Read: (DVB) Chapters 6–8; or, (OS) Sections 8.1–3

CC: (OS, Chapter 8) 8.1, 9, 17, 27

FP:

• (OS, Chapter 8) 8.3, 5, 7, 11, 13, 19, 21, 23, 28, 29
• (DVB, Chapter 6) 6.11, 13, 15, 19, 21, 25, 31, 37; (Chapter 7) 7.15-23 odds, 27, 28, 43, 44, 49, 51, 67, 77; (Chapter 8) 8.21–33 odds, 41, 49, 63, 65

w2wed

Topic: Probability Basics

Read: (DVB) Chapter 12 and Section 13.1; or, (OS) Section 3.1

CC: (OS, Chapter 3) 3.1, 3, 5, 7

FP:

• (OS, Chapter 3) 3.8, 9, 10, 11, 39, 40
• (DVB, Chapter 12) 12.9, 11, 15, 21, 37, 49; (Chapter 13) 13.1, 2, 15, 17, 19, 29

w2fri

Topic: Conditional Probability

Read: (DVB) Sections 13.2–4; or, (OS) Section 3.2 through 3.2.6

CC: (OS, Chapter 3) 3.13, 14, 15

FP:

• (OS, Chapter 3) 3.16, 17, 18
• (DVB, Chapter 13) 13.3, 5, 7, 9, 11, 23, 33, 39, 43, 51

w3mon

Topic: Bayes’ Rule

Read: (DVB) Section 13.5; or, (OS) Sections 3.2.7–8

CC: (OS, Chapter 3) 3.19, 20

FP:

• (OS, Chapter 3) 3.21, 22, 41, 42
• (DVB, Chapter 13) 13.13, 14, 55, 57, 59, 61

w3wed

Topic: Random Variables

Read: (DVB) Chapter 14; or, (OS) Sections 3.4–5

CC: (OS, Chapter 3) 3.29, 31, 37, 43

FP:

• (OS, Chapter 3) 3.3, 8, 10, 11, 15, 17, 33, 35, 36, 38, 39, 41, 45, 46, 47
• (DVB, Chapter 14) 14.9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39

w3fri

Topic: Bernoulli, Geometric, and Poisson Distributions

Read: (DVB) Sections 15.1, 15.2, and 15.6; or, (OS) Sections 4.2 and 4.5

CC: (OS, Chapter 4) 4.11, 13, 31, 33

FP:

• (OS, Chapter 4) 4.12, 14, 15, 16, 32, 34, 35
• (DVB, Chapter 15) 15.1, 2, 3, 4, 17, 19, 21, 47, 48, 49(b), 50(b)

w4fri

Topic: Normal Distribution

Read: (DVB) Chapter 5; or, (OS) Section 4.1

CC: (OS, Chapter 4) 4.1, 3

FP:

• (OS, Chapter 4) 4.2, 4, 5, 7, 9, 10
• (DVB, Chapter 5) 5.3, 9, 11, 13, 15, 25, 35, 49, 55

w5mon

Topic: Binomial Distribution

Read: (DVB) Sections 15.3–4; or, (OS) Section 4.3

CC: (OS, Chapter 4) 4.17, 19, 21

FP:

• (OS, Chapter 4) 4.18, 20, 22, 23, 24, 26
• (DVB, Chapter 15) 15.5, 6, 33–40

w5wed

Topic: Uniform and Exponential Distributions

Read: (DVB) Section 15.7 (see here on Canvas)

CC: (DVB, Chapter 15) 9, 10, 11, 12 (see here on Canvas, and you can check your solutions to odd-numbered exercises here)

FP: (DVB, Chapter 15) 59, 60, 61, 62 (see here on Canvas)

w5fri

Topic: Confidence Intervals for 1 Binary Variable

Read: (DVB) Chapter 16; or, (OS) Sections 5.1–2

CC: (OS, Chapter 5) 5.1, 3, 7, 9

FP:

• (OS, Chapter 5) 5.2, 5, 8, 10, 11, 13, 45
• (DVB, Chapter 16) 15.21, 23, 25, 27, 35, 37, 45, 47, 53

w6mon

Topic: Hypothesis Testing for 1 Binary Variable

Read: (DVB) Chapter 18; or, (OS) Sections 2.3 and 5.3–6.1

CC: (OS, Chapter 2) 2.25; (Chapter 5) 5.15, 17; (Chapter 6) 6.1, 3

FP:

• (OS, Chapter 5) 5.16, 19, 21, 23, 24, 26, 27, 28, 32, 37; (Chapter 6) 6.2, 4, 5, 9, 10, 11, 12, 14, 15, 16, 49
• (DVB, Chapter 18) 15, 17, 19, 25, 35, 37, 41, 43

w6wed

Topic: Being Careful about Statistical Inference

Read: AGM19: “Scientists rise up against statistical significance” by Amrhein, Greenland, and McShane. (see also here on Canvas)

Read: Was16: “The ASA Statement on p-Values” by Wasserstein.

CC: Synthesize the two articles and submit a list of 3 salient points that you thought were particularly important.

• Do not submit an RQ on Zulip for w6wed. It won’t count for credit. Do the CC above instead.

• Please read the articles carefully: the articles make some subtle but very important points. This might be the most important reading you do this quarter!

• Bring your list of 3 salient points to class with you. We will discuss this together. This might be the most important in-class discussion we have this quarter, so please make sure to do the reading and come to class!

w6fri

Topic: Confidence Intervals for 1 Numerical Variable

Read: (DVB) Chapter 17; or, (OS) Section 7.1 through section 7.1.4

CC: (OS, Chapter 7) 7.1, 3, 5

FP:

• (OS, Chapter 7) 7.2, 6, 10, 13, 14
• (DVB, Chapter 17) 17.23, 25, 33, 39, 41, 47, 49, 57

w7mon

Topic: Hypothesis Testing for 1 Numerical Variable

Read: (DVB) Section 18.4 and Chapter 21; or, (OS) Sections 7.1.5 and 7.2

CC: (OS, Chapter 7) 7.7, 9, 11, 15, 17

FP:

• (OS, Chapter 7) 7.4, 16, 18, 19, 21
  
• (DVB, Chapter 18) 18.9, 10, 11, 12, 27; (Chapter 21) 9, 11, 13, 17, 23, 27

w7wed

Topic: Inference for 1 Categorical Variable

Read: (DVB) Sections 22.1–3; or, (OS) Section 6.3

CC: (OS, Chapter 6) 6.31, 33

FP:

• (OS, Chapter 6) 6.32, 34
• (DVB, Chapter 21) 1, 3, 5, 7, 13, 15, 17, 21

w7fri

Topic: Inference for 2 Binary Variables

Read: (DVB) Sections 20.1–3; or, (OS) Section 6.2

CC: (OS, Chapter 6) 6.19, 21, 23

FP:

• (OS, Chapter 6) 6.18, 20, 23, 25, 29
• (DVB, Chapter 20) 1, 3, 5, 7, 9, 21, 23, 25, 27, 31, 33, 35, 37, 39, 41

w9wed

Topic: Inference for 1 Numerical and 1 Binary Variable

Read: (DVB) Sections 20.4–5; or, (OS) Section 7.3

CC: (OS, Chapter 7) 7.23, 25

FP:

• (OS, Chapter 7) 7.27, 29, 31
• (DVB, Chapter 20) 20.11, 13, 15, 51, 53, 55, 57, 59

w9fri

Topic: Inference for 2 Categorical Variables

Read: (DVB) Section 22.4; or, (OS) Section 6.4

CC: (OS, Chapter 6) 6.35, 37

FP:

• (OS, Chapter 6) 6.38, 40, 41, 50
• (DVB, Chapter 22) 22.9, 11, 12, 23, 25, 27, 29, 31, 33, 35

w10mon

Topic: Inference for 2 Numerical Variables

Read: (DVB) Sections 23.1–4; or, (OS) Section 8.4

CC: (OS, Chapter 8) 8.31, 33, 34

FP:

• (OS, Chapter 8) 8.37, 38, 39, 42
• (DVB, Chapter 8) 23.1, 3, 19, 21, 23, 25