Current/Upcoming Items

Final Problem Set (Handed Out Thursday 12/12, Due Electronically by the Start of the Final Presentations)

• J SLOs, E10 and various Hs also available as “reassessment”

Final Project Writeup (Due Electronically by the Start of the Final Presentations – differs by section)

Final Presentation

• I will randomize the order of presenters, unless you have specific constraints (e.g., you are trying to catch a flight the same day)
• Plan to present for about 5 minutes, and take questions for another 2 or so.
• Suggested slide format (you do not have to follow exactly)
  • One slide introducing your topic and the context around it
  • One slide describing your data-collection methods
  • One slide with key visualizations of the data itself, and key summary statistics
  • One slide with your main analyses (don’t show code in the slides; do describe your inference methods and give key results)
  • One slide describing the main takeaways of your findings
  • One slide describing limitations of your data and methods, and what you might do differently if you had more time and resources to conduct the study differently / collect more data
• Send me your slides by noon on the day you are presenting

Past Items

HW0 (Due Friday 9/6 by 5pm)

• Join the class Slack workspace (check email for an invite link)
• Fill out the pre-semester survey here
• Textbook problems: 1.16, 1.30, 1.42, 1.44, 1.46, 1.52, 1.90
• Relevant SLOs (not graded): A1,A2,B1,B2

Quiz 1 (Handed out Monday 9/9, Due Start of Class Tuesday 9/10)

• SLOs A1,A2,B1,B2

HW1 (Due Friday 9/13 by 5pm)

• Review problems (optional): 1.20 (A1-A2), 1.54 (B1-B2), 1.56 (B1-B2)
• Quick Practice (check your own answers in the back of the book; don’t need to turn in): 1.67, 1.69, 1.71, 1.73, 1.75, 1.77, 1.79, 1.81
• Textbook problems: 1.8 and 1.10 (A3), 1.86 (B4), 1.100 (B3-B4)

Lab 1 (Due Friday 9/13 by 5pm)

• Lab 1: see here

Quiz 2 (Handed out Monday 9/16, Due Start of Class Tuesday 9/17)

• SLOs A3,B3,B4

HW2 (Due to RStudioPro server by 5pm on Friday 9/20)

• Submitted filename must begin with hw2 (lowercase, no spaces) and be turned in to ~/stat113/turnin/hw2/ on the server
• Review (optional): 1.92(a-f), 1.94
• Quick Practice (check your own answers in the back of the book; don’t need to turn in): 2.11, 2.17, 2.19, 2.23, 2.43, 2.47, 2.59, 2.73
• Required: 2.16, 2.20 and 2.24 (C2), 2.62, 2.64, 2.66, 2.74 (C1), 2.150 (C4)

Lab2 (Due to RStudioPro server by 5pm on Friday 9/20)

• Started in class Thursday 9/19, finish on your own
• Submitted filename must begin with lab2 (lowercase, no spaces) and be turned in to ~/stat113/turnin/lab2/ on the server

Quiz 3 (Handed out Monday 9/23, Due Start of Class Tuesday 9/24)

• SLOs B3,B4,C1,C2,C4

HW3 (Due Friday 9/27 by 5pm)

• Submitted filename must begin with hw3 (lowercase, no spaces) and be turned in to ~/stat113/turnin/hw3/ on the RStudioPro server
• I encourage (though do not require) you to use an RMarkdown file for regular homework as well; even though most of it will not require any R code, you might find that using code chunks to do whatever arithmetic, etc. that you need to do is smoother than using a calculator and then writing it up. (You will also, in many cases, have access to the dataset referred to in the problem via the Lock5Data package). See the lab2 solution set for an example of how to organize this.
• New SLOs: C3,C5,D1
• Quick Practice (check your own answers in the back of the book; don’t need to turn in): 2.85, 2.87, 2.91, 2.109, 2.113, 2.131, 2.147, 2.164-2.167, 2.169, 2.171, 2.173, 2.201
• Turn in:
  • 2.116, 2.122 and 2.152 (C3)
  • 2.182, 2.186, 2.190(b) and 2.194 (C5)
    • 2.194(c-e) require R, and collectively also earn credit for SLO I2)
  • 2.212 and 2.218 (D1)

Lab3 (Due Friday 9/27 by 5pm)

• Submitted filename must begin with lab3 (lowercase, no spaces) and be turned in to ~/stat113/turnin/lab3/ on the RStudioPro server

Quiz 4 (Handed out Monday 9/30, Due Start of Class Tuesday 10/1)

• SLOs: C3,C5,D1,D2

HW4 (Due Friday 10/4 by 5pm)

• Submitted filename must begin with hw4 (lowercase, no spaces) and be turned in to ~/stat113/turnin/hw4/ on the RStudioPro server
• Textbook problems: 3.2, 3.4, 3.22, 3.12, 3.14

Lab4 (Due Friday 10/4 by 5pm)

• Turn in lab4.Rmd and lab4.html (lowercase, no spaces) to ~/stat113/turnin/lab4/ on the RStudioPro server
• Part (a): Work on in class Tuesday 10/1
• Part (b): Work on in class Thursday 10/3

Lab5 (Due Friday 10/11 by 5pm)

• Turn in lab5.Rmd and lab5.html (lowercase, no spaces) to ~/stat113/turnin/lab5/ on the RStudioPro server

Quiz 5 (Due Tuesday 10/15 by class time)

• SLOs: E1, E3, E4, E5, F1

Term Project Proposal (Due Thursday 10/17 by class time)

• Form a group of 2 or 3, or message me if you want to be assigned to a partner
• See the project specifications for what to include in your proposal.
• Upload your 1-2 page proposal to the RStudio server at ~/stat113/turnin/project/proposal/

HW5 (Due Friday 10/18 by 5pm)

• Quick Practice (check your own answers; don’t need to turn in): 3.45, 3.47, 3.49, 3.51, 3.53, 3.55, 3.75, 3.77, 3.79, 3.105, 4.9, 4.11, 4.13
• Textbook problems: 3.64, 3.72, 3.74, 3.86, 3.102, 4.18, 4.28
• Relevant SLOs: F1, F2, G1, G3

Optional: Quiz 6 (Handed out Monday 10/28; due Tuesday 10/29)

• Chance for reassessment on B4, C5, D2, E1, E3, E4

HW6 (Due Friday 11/01 by 5pm)

• Textbook problems: 4.104, 4.128, 4.130, 4.144, B.36 (from Unit B: Essential Synthesis), 4.134-4.140 (even), 4.176

Lab6 (Due Friday 11/01 by 5pm)

• Turn in lab6.Rmd and lab6.html (lowercase, no spaces) to ~/stat113/turnin/lab6/ on the RStudioPro server

Quiz 7 (Handed out Monday 11/04, due Start of Class Tuesday 11/05)

• SLOs: E3, F2, G2, G3, G4

Lab7 (Due Friday 11/08 by 5pm)

EXAM 2 (Handed out Friday 11/08, due Start of Class Tuesday 11/12)

• E-G SLOs

HW8 (Due Friday 11/15; SLOs H1, H2, H3, I2, I4, I5)

• Unlike in previous homeworks, the SLOs here count toward your grade, since we are now at the point where we are applying analyses end-to-end. The H SLOs are not realistically assessable on quizzes or exams.
• Modified textbook problems: 6.58, 6.126, C.62. For the scenario described in each problem, do the following in a Markdown document:
  • Plot the data or provide a table (as appropriate)
  • Define the parameter of interest with context and using appropriate notation
  • State hypotheses in words and in symbols
  • Check the conditions for using a distributional approximation. But for the sake of practice, proceed with the analysis even if the conditions are not satisfied.
  • Construct a confidence interval without simulation, by first finding the z-scores of the endpoints using an appropriate distribution and then converting them to the scale of the parameter using the appropriate formula for the standard error.
  • Interpret the confidence interval in context
  • Find the appropriate (standardized) test statistic
  • Use the test statistic to find the P-value
  • Interpret the results in context (use alpha = 0.05)

No lab to turn in this week

Project Pilot Analysis (Due Electronically Monday 11/18)

* Upload your .Rmd and Knitted .html files to the turnin folder

Quiz 8: Handed Out Monday 11/18, Due Start of Class Thursday 11/21

• SLOs E6, E7, E9, E10

HW9 (Due Tuesday 11/26; SLOs D1, D2, H4, H5, H6, I2, I4, I5)

• NOTE (11/24) I’ve separated the regression problem into its own handout here, as I didn’t find any of the textbook problems to have the structure I wanted. Include your answers to the questions in this handout as part of your Markdown document.
• As with Lab7 and HW8, the SLOs here count toward your grade, since we are now at the point where we are applying analyses end-to-end. The H SLOs are not realistically assessable on quizzes or exams.
• Modified textbook problems: 6.224, 6.252. For the scenario described in each problem, do the following in a Markdown document:
  • Plot the data or provide a table (as appropriate)
  • Define the parameter of interest with context and using appropriate notation
  • State hypotheses in words and in symbols
  • Check the conditions for using a distributional approximation. But for the sake of practice, proceed with the analysis even if the conditions are not satisfied.
  • Construct a confidence interval without simulation, by first finding the z-scores of the endpoints using an appropriate distribution and then converting them to the scale of the parameter using the appropriate formula for the standard error.
  • Interpret the confidence interval in context
  • Find the appropriate (standardized) test statistic
  • Use the test statistic to find the P-value
  • Interpret the results in context (use alpha = 0.05)
• The regression problem given in this handout

Quiz 9 (Handed out Monday 11/25, due in class Tuesday 11/26)

• SLOs E1, E5, E6, E7, E9, E10, G1, G5, G6

Quiz 10 (Handed out Tuesday 12/3, due Friday 12/5)

• SLOs: E2, E5*, E6*, E8, G6*

Quiz 11 (Handed out Monday 12/9, due Tuesday 12/10)

• SLOs: E1*, E2, E6*, E7*, E8, E10*, F1*, G1*, G4*, G5*

HW10 (due Thursday 12/12)

• SLOs: H7-H9, I2,I4,I5
• Modified textbook problems: Ch 7.30 (H7), 7.48 (H8). For the scenario described in each problem, do the following:
  • State null and alternative hypotheses, in words, and, for 7.30, in terms of parameters (for 7.48 just words is sufficient)
  • Plot the data or provide a table (as appropriate)
  • Check the conditions for using a distributional approximation. But for the sake of practice, proceed with the analysis even if the conditions are not satisfied.
  • Find the appropriate test statistic (using the “built in” function in R is fine)
  • Use the test statistic to find the P-value
  • Interpret the results in context (use alpha = 0.05)
  • New: Use the Pearson residuals (included in the R output) to interpret the nature of the discrepancy from the null hypothesis (if you rejected it)
  • Optional: (H1) For the proportions in 7.30, report 95% confidence intervals for each one, and test whether we have evidence that it is different from the null value.
• Updated: Modified textbook problem: 8.38 (E2*, H4*, H9)
  • State null and alternative hypotheses, in words and (for the null) in terms of parameters of interest.
  • Plot the data, grouping by condition, and report the relevant sample statistics
  • Check whether the conditions for an analytic approximation to the randomization distribution are met
  • For the sake of practice, proceed with the analysis even if the conditions are not satisfied.
  • Find the appropriate test statistic (use the “built-in” function in R)
  • Use the test statistic to find the P-value
  • Interpret the results of the test in context (use alpha = 0.05)
  • (E2) Give and interpret the R-squared value for the association
  • (H4) If you found significant evidence that the population means are different, perform hypothesis tests to test which pairs of population means are different. These are tests of differences in means, with the following small changes: to calculate standard error, (i) use MS(Residuals) from the ANOVA table in place of the squared sample standard deviations (this makes life easier since it’s the same number for all pairs), (ii) and for the df of the t-distribution, use df(Residuals) from the ANOVA table.
  • (H4) Calculate and report 95% confidence intervals for each pairwise difference in means, using the modified standard error and degrees of freedom that you used for the tests.