From a57f9efd3d4ae33a3957f73b8b35fe463a7a3753 Mon Sep 17 00:00:00 2001 From: Marie Auger-Methe Date: Wed, 4 Sep 2024 10:31:34 -0700 Subject: [PATCH] Update website --- Code-of-Conduct.html | 284 +++++++++++++++++++++++++++++++++++ JUPYTERLAB_GUIDE.md | 203 +++++++++++++++++++++++++ images/image1.png | Bin 0 -> 73229 bytes images/image2.png | Bin 0 -> 38841 bytes images/image3.png | Bin 0 -> 11371 bytes images/image4.png | Bin 0 -> 10110 bytes images/image5.png | Bin 0 -> 120968 bytes images/image6.png | Bin 0 -> 37071 bytes images/image7.png | Bin 0 -> 87405 bytes lecture-learning-outcomes.md | 93 ++++++++++++ 10 files changed, 580 insertions(+) create mode 100644 Code-of-Conduct.html create mode 100644 JUPYTERLAB_GUIDE.md create mode 100644 images/image1.png create mode 100644 images/image2.png create mode 100644 images/image3.png create mode 100644 images/image4.png create mode 100644 images/image5.png create mode 100644 images/image6.png create mode 100644 images/image7.png create mode 100644 lecture-learning-outcomes.md diff --git a/Code-of-Conduct.html b/Code-of-Conduct.html new file mode 100644 index 0000000..ebaf72d --- /dev/null +++ b/Code-of-Conduct.html @@ -0,0 +1,284 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Introduction

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For STAT 201, we are committed to creating a friendly and respectful place for learning, teaching and contributing. All participants in our course and communications are expected to show respect and courtesy to others.

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To make clear what is expected, everyone participating in STAT 201 activities is required to conform to the Code of Conduct. This Code of Conduct applies to all spaces managed by STAT 201 including, but not limited to, class meetings, email communication, and online forums such as Piazza and GitHub. The STAT 201 teaching team are expected to assist with the enforcement of the Code of Conduct.

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The STAT 201 Instructor(s) is responsible for enforcing the Code of Conduct. All reports will be reviewed by the Instructor(s) and will be kept confidential.

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The STAT 201 Code of Conduct

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For STAT 201, we are dedicated to providing a welcoming and supportive environment for all people, regardless of background or identity. As such, we do not tolerate behaviour that is disrespectful to our teachers or learners or that excludes, intimidates, or causes discomfort to others. We do not tolerate discrimination or harassment based on characteristics that include, but are not limited to, gender identity and expression, sexual orientation, disability, physical appearance, body size, citizenship, nationality, ethnic or social origin, pregnancy, familial status, veteran status, genetic information, religion or belief (or lack thereof), membership of a national minority, property, age, education, socio-economic status, technical choices, and experience level.

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Everyone who participates in STAT 201 activities is required to conform to this Code of Conduct. It applies to all spaces managed by STAT 201 including, but not limited to, class meetings, email communication, and online forums such as Piazza and GitHub. The STAT 201 teaching team are expected to assist with the enforcement of the Code of Conduct.

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Expected Behavior

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All participants in our events and communications are expected to show respect and courtesy to others. All interactions should be professional regardless of platform: either online or in-person. In order to foster a positive and professional learning environment we encourage the following kinds of behaviours in all STAT 201 events and platforms:

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  • Use welcoming and inclusive language
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  • Be respectful of different viewpoints and experiences
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  • Gracefully accept constructive criticism
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  • Focus on what is best for the community
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  • Show courtesy and respect towards other community members
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Note: See the four social rules for further recommendations.

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Unacceptable Behavior

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Examples of unacceptable behavior by participants at any STAT 201 event/platform include:

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  • written or verbal comments which have the effect of excluding people on the basis of membership of any specific group
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  • causing someone to fear for their safety, such as through stalking, following, or intimidation
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  • violent threats or language directed against another person
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  • the display of sexual or violent images
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  • unwelcome sexual attention
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  • nonconsensual or unwelcome physical contact
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  • sustained disruption of talks, events or communications
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  • insults or put downs
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  • sexist, racist, homophobic, transphobic, ableist, or exclusionary jokes
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  • excessive swearing
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  • incitement to violence, suicide, or self-harm
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  • continuing to initiate interaction (including photography or recording) with someone after being asked to stop publication of private communication without consent
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Consequences of Unacceptable Behavior

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Participants who are asked to stop any inappropriate behavior are expected to comply immediately. This applies to any STAT 201 events and platforms, either online or in-person. If a participant engages in behavior that violates this code of conduct, the teaching team may warn the offender and or engage the STAT 201 Instructor to investigate the Code of Conduct violation and impose appropriate sanctions.

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Incident Reporting Guidelines

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If someone violates the Code of Conduct during a STAT 201 event or in an online space outside of organised events, we ask that you report it to the Instructor(s). The current instructors of STAT 201 are Rodolfo Lourenzutti and Vincenzo Coia. An email to lourenzutti@stat.ubc.ca will be seen by Rodolfo and an email to vcoia@stat.ubc.ca will be seen by Vincenzo. To report a STAT 201 code of conduct violation that will not be seen by Rodolfo or Vincenzo, an alternative contact person is Tiffany Timbers, Professor of Teaching in the Department of Statistics at UBC. Her email is tiffany.timbers@stat.ubc.ca.

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Attribution

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+ + + + + + + + + + + + + + + + diff --git a/JUPYTERLAB_GUIDE.md b/JUPYTERLAB_GUIDE.md new file mode 100644 index 0000000..62e2f42 --- /dev/null +++ b/JUPYTERLAB_GUIDE.md @@ -0,0 +1,203 @@ +# JupyterLab Guide + +This guide contains tips and FAQs which help to avoid common issues that arise when using JupyterLab. + +# Table of Contents +### Tips +1. [Open multiple notebook tabs](#open-multiple-notebook-tabs) +2. [Check solutions or feedback](#check-solutions-or-feedback) +3. [Check memory usage](#check-memory-usage) +4. [Get a fresh copy of a notebook](#get-a-fresh-copy-of-a-notebook) +5. [View hidden files](#view-hidden-files) +6. [Clone a GitHub repo](#clone-a-github-repo) +7. [Get a copy of everything](#get-a-copy-of-everything) + +### FAQs +1. [Why did I get a zero/low-mark when I submitted everything on time?](#why-did-i-get-a-zerolow-mark-when-i-submitted-everything-on-time) +2. [Why are none of my cells running?](#why-are-none-of-my-cells-running) +3. [Why does my kernel keep dying?](#why-does-my-kernel-keep-dying) +4. [Why did I get a zero on a question when all the tests passed?](#why-did-i-get-a-zero-on-a-question-when-all-the-tests-passed) +5. [Why did I get a low mark on a question when all the tests passed?](#why-did-i-get-a-low-mark-on-a-question-when-all-the-tests-passed) +6. [I made a typo in my code, can I still get marks for the question?](#i-made-a-typo-in-my-code-can-i-still-get-marks-for-the-question) +7. [I am getting a 400 (Bad Request) or 404 (Not Found) error, how do I fix it?](#i-am-getting-a-400-bad-request-or-404-not-found-error-how-do-i-fix-it) +8. [How do I get a blank worksheet or tutorial for practice?](#how-do-i-get-a-blank-worksheet-or-tutorial-for-practice) + +## Tips + +## Open multiple notebook tabs + +When working on a notebook, you might want to have multiple tabs of the same notebook open to easily reference previous questions. + +**DO NOT** open another browser tab, create another instance of JupyterLab, and open the same notebook. Doing so may overwrite the notebook you are working on with an empty or half-completed version. + +Instead, use JupyterLab's built in tab functionality to create another view for the notebook. + +To do so in JupyterLab, right click the tab that contains the notebook in question. + +On the dropdown menu, click **New View for Notebook**: + +![Alt text](images/image1.png) + +This will create a split screen, showing a copy of the notebook on half of the screen. + +## Check solutions or feedback + +Solutions and feedback are uploaded to your home directory. This is a different directory to where assignments are stored. + +To get to your home directory, open JupyterLab and then click the small grey folder icon on top of the menu to the left that shows files and folders. + +![Alt text](images/image2.png) + +Then that same menu should show assignment solutions and feedback. Solutions are typically released a few days after the due date, feedback is released after assignments are graded (this may take a couple weeks as tutorials are manually graded). + +## Check memory usage + +Student JupyterLab instances are limited to 2GB (2048MB) of memory usage, if that limit is reached by running a notebook then the kernel will be killed and cells will stop running. + +To check how much memory JupyterLab is using, there is a indicator on the bottom left of the window: + +![Alt text](images/image3.png) + +The maximum amount of memory usage a notebook should use is around 1GB (1024MB). If you find that JupyterLab is hitting the limit very often, here are some steps you can take: + +### Restart your JupyterLab server + +On the top left, go to **File -> Hub Control Panel** and it will open a window with a red button with text **Stop My Server**, click the button and then click the **Log Out** button on the top right. + +Close the browser tab and then open JupyterLab again from Canvas. + +### Check your code + +If restarting your server does not work, then there may be an issue with your code. + +Run your notebook one cell at a time. If you hit a cell that causes memory usage to hit the 2GB limit, examine the code in the cell to make sure there aren't any errors e.g. running a function on unfiltered data. + +## Get a fresh copy of a notebook + +If you need a new copy of a notebook, for example if you accidentally deleted a grading cell or want one for practice, first rename the notebook you have in your account currently. + +For example if you need a new copy of a `worksheet_wrangling.ipynb`, right-click it in the file menu and choose the **Rename** option and rename it to `worksheet_wrangling_old.ipynb`. + +Then close JupyterLab and then open the assignment again on Canvas. A new copy of the assignment should be created in place of the renamed assignment. + +## View hidden files + +*Warning*: Only delete hidden files that you have created, other hidden files may be used by the server + +To view hidden files, on the top left go to **View** and on the dropdown click **Show Hidden Files** + +## Clone a GitHub repo + +To clone a GitHub repo for collaborative work, follow these steps: + +1. Find the HTTPS repo link from GitHub and copy it to the clipboard + +![alt text](images/image5.png) + +2. Go to your JupyterLab and make sure you're in the home directory by clicking the small grey folder icon on the top left of the menu that shows your files and folders + +![Alt text](images/image2.png) + +3. Go to the JupyterLab git extension menu by clicking its tab on the left of JupyterLab: + +![alt text](images/image6.png) + +4. Click "Clone a Repository" and then paste the repo link from step 1 into the box and click Clone. + +![alt text](images/image7.png) + +The repo should now be in your home directory in a folder titled with the name of your repo. + +To get to your cloned repo, remember to navigate to the home directory as shown in step 2. + +## Get a copy of everything + +To get a zipped copy of all of your work at the end of term: + +1. Open a terminal by clicking the terminal icon when you first open JupyterLab from the Canvas homepage or in a new JupyterLab tab. + +![Alt text](images/image4.png) + +2. Enter the following command into the terminal: +``` +cd /home/jovyan/work/ && zip -r backupjupyterlab.zip * +``` +If you received a `Disk quota exceeded` error, then there are too many files. Delete any files or folders that you don't need (especially large .csv files) and try the command again. + +3. Go to your home folder [as described here](#check-solutions-or-feedback) and there should be a zip file. Right-click the file and click Download to download a copy onto your device. + +## FAQs + +### Why did I get a zero/low-mark when I submitted everything on time? + +If you are aware of the above, then make sure that you are opening another notebook tab [in the correct way](#open-multiple-notebook-tabs). If you opened another browser tab with JupyterLab, then there is a chance your notebook was overwritten. + +Lastly, do not rename the assignment notebook file or any folders. The autograder relies on those names to collect submissions. + +### Why are none of my cells running? + +Before anything else, try restarting JupyterHub as described [here](#restart-your-jupyterlab-server). + +Make sure you're using a compatible browser. We recommend Chrome, Edge, or Firefox. Also update your browser to the latest version. + +Update your device's operating system to the latest version and reboot your device. + +Disable any extensions to check if they are affecting JupyterLab. + +Try opening JupyterLab in an incognito browser session. + +### Why does my kernel keep dying? + +Please read [Check memory usage](#check-memory-usage). + +### Why did I get a zero on a question when all the tests passed? + +You may have deleted a grading cell accidentally (grading cells are cells that contain test functions e.g. `test_3.2()`). + +The autograder relies on these cells to give students marks. Creating a new cell and then copying the test function into that cell will not fix the issue. + +If you do accidentally delete a grading cell, you should [get a fresh copy of the assignment notebook](#get-a-fresh-copy-of-a-notebook). Then, copy your work into the fresh notebook. + +### Why did I get a low mark on a question when all the tests passed? + +Some questions have hidden tests that are not shown in the student version of the assignment. + +Your answer may have passed the visible tests but failed the hidden tests when it was being marked. + +Examine the feedback for the question carefully to see if hidden tests were run on your code. Instructions for accessing feedback are [here](#check-solutions-or-feedback). + +### I made a typo in my code, can I still get marks for the question? + +We will not be making exceptions for typos in autograded coding questions. + +Read the questions carefully as some questions request answers of a certain type (e.g. String). + +The questions include tests that give instant feedback on your code. Run those tests frequently to ensure that you haven't made any typos. + +### I am getting a 400 (Bad Request) or 404 (Not Found) error, how do I fix it? + +Before continuing with the instructions below, try JupyterLab again after restarting your browser. + +The errors could be due to your browser's cache and it may need to be cleared. + +Instructions: + +[For Edge](https://www.microsoft.com/en-us/edge/learning-center/how-to-manage-and-clear-your-cache-and-cookies?form=MA13I2) + +[For Chrome](https://support.google.com/accounts/answer/32050?hl=en&co=GENIE.Platform%3DDesktop) + +[For Firefox](https://support.mozilla.org/en-US/kb/how-clear-firefox-cache) + +After clearing your browser's cache, restart the browser and try opening JupyterLab again. + +If it still has the same error, try using Incognito or Private Browsing on your browser. + +Lastly, disable any browser extensions as they could interfere with JupyterLab. Also check that your browser is updated to its latest version. + +### How do I get a blank worksheet or tutorial for practice? + +Follow the instructions [here](#get-a-fresh-copy-of-a-notebook) to get a blank copy of a tutorial or worksheet. + + + + diff --git a/images/image1.png b/images/image1.png new file mode 100644 index 0000000000000000000000000000000000000000..3d141d29f001239fb44af6d3cfe454d273448fdd GIT binary patch literal 73229 zcmb5WbyyVL|33_fuu6;4ogyVA(p^$2(kYE}H%KVm(%nid-7O8$B`w`uvefUacYN;e z^WWojaqT)gvomw{#H-#D^jbj*;~~*Q1Ox;O8ENsi2na|H2ndMH5EQV5=s58sctNy( zDfEbLoKAw9@bf_z`xZcrU3lHw}_^6E8> zOn9_cG)*s$i~<$U)AvCrkrX1w*Ee{RKiw||cX#)H?9L_Huh)%ztKS}YTNCv+2KK~%HcrBuVn?_AtH}Ra2;BzRcNpW4n4;i6CoGN-#;%`i`TXWT z?L+Vj+yn71ZsB+TX-Exn9}0(JZ;Wh||J}&*Rvd^ZcdPZsuU`K_lU+$ z91^@@vTV6I-`df=gFWB)5=I@t&)#$k+u2fWfhkXONybfS*~`~&7Zi>z-k#TRc9YoW z$iJAT8Q?o;q#nQTDl$oz>M+WA)PcKk8i*b(a5k=#Z`(ykFK}}@W_EKnrPxj8Zb8Iu z*aJBs8GGP$ceR**)J-l_Ts5iD{?%^QvL?6X_N?U64bwj3{mM#&(4bjL%lV8sH%ZvQ 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Describe real-world examples of questions that can be answered with the statistical inference methods presented in this course (e.g., estimation, hypothesis testing). +2. Name common population parameters (mean, proportion, median, variance, standard deviation, and correlation) that are often estimated using sample data, and write computer scripts to calculate estimates of these parameters. +3. Define the following terms in relation to statistical inference: population, sample, population parameters, estimate, sampling distribution, sample distribution. +4. Write an R script to draw random samples from a finite population (e.g., census data). +5. Write an R script to reveal a sampling distribution from a finite population. + +### Week 2: Populations and Sampling +From this section, students are expected to be able to: +1. Compare and contrast quantitative and categorical variables. +2. Explain random and representative sampling and how this can influence estimation. +3. Define random variables and explain how they relate to sampling. +4. Define standard error and explain its purpose. +5. Compare and contrast population distribution, sample distribution and an estimator's sampling distribution. +6. Explain what a sampling distribution is, list its properties, and its purpose in statistical inference. + +### Week 3: Bootstrapping and its Relationship to the Sampling Distribution +From this section, students are expected to be able to: +1. Explain why we don’t know/have a sampling distribution in practice/real life. +2. Define bootstrapping. +3. Write a computer script to create a bootstrap distribution to approximate a sampling distribution. +4. Contrast the bootstrap sampling distribution with an assumed sampling distribution. +5. Estimate the standard error of an estimator using bootstrapping. + +### Week 4: Confidence Intervals via Bootstrapping +From this section, students are expected to be able to: +1. Define and calculate sample quantiles. +1. Define what a confidence interval is, and why we want to generate one. +2. Explain how the bootstrap sampling distribution can be used to create confidence intervals. +3. Write a computer script to calculate confidence intervals for a population parameter using bootstrapping. +4. Effectively visualize point estimates and confidence intervals. +5. Interpret and explain results from confidence intervals. +6. Discuss the potential limitations of these methods. + +### Week 5: Mid-term #1 and Preparation for Projects +In this week, students will write a mid-term exam, and begin working on their projects. +Also from this section, students are expected to be able to +1. Propose parameters that are useful, given the type of data. +2. Propose parameters that are useful, given a question. +3. Choose an appropriate way to present estimator uncertainty, given a question, by comparing and contrasting the usefulness of confidence intervals vs. standard error. + +### Week 6: Hypothesis Testing via Simulation/Randomization +From this section, students are expected to be able to: +1. Give an example of a question you could answer with a hypothesis test. +2. Differentiate composite vs. simple hypotheses. +3. Given an inferential question, formulate null and alternative hypotheses to be used in a hypothesis test. +5. Identify the steps and components of a basic hypothesis test ("there is only one hypothesis test"). +6. Write computer scripts to perform hypothesis testing via simulation, randomization and bootstrapping approaches, as well as interpret the output. +8. Describe the relationship between confidence intervals and hypothesis testing. +9. Discuss the potential limitations of this simulation approach to hypothesis testing. + +### Week 7: Confidence Intervals (of means and proportions) Based on the Assumption of Normality or the Central Limit Theorem +From this section, students are expected to be able to: +1. Describe the Law of Large Numbers. +2. Describe a normal distribution. +3. Explain the Central Limit Theorem and its role in constructing confidence intervals. +4. Write a computer script to calculate confidence intervals based on the assumption of normality / the Central Limit Theorem. +5. Discuss the potential limitations of these methods. +6. Decide whether to use asymptotic theory or bootstrapping to compute estimator uncertainty. + +### Week 8: Classical Tests Based on Normal and t- Distributions +From this section, students are expected to be able to: +1. Describe a t-distribution and its relationship with the normal distribution. +2. Use results from the assumption of normality or the Central Limit Theorem to perform estimation and hypothesis testing. +3. Compare and contrast the parts of estimation and hypothesis testing that differ between simulation- and resampling-based approaches with the assumption of normality or the Central Limit Theorem- based approaches. +4. Write a computer script to perform hypothesis testing based on results from the assumption of normality or the Central Limit Theorem. +5. Discuss the potential limitations of these methods. + + +### Week 9: Mid-term #2 and Project Work + + +### Week 10: Errors in Inference +From this section, students are expected to be able to: +1. Define type I & II errors. +2. Describe responsible use and reporting of p-values from hypothesis tests. +3. Discuss how these errors are linked to a "reproducibility crisis". +4. Measure how these errors amplify when performing multiple hypothesis testing, in the context of multiple comparisons. + + +### Week 11: Beyond two-group comparisons +From this section, students are expected to be able to: +1. Run a simple one-way ANOVA, without knowing the details of the test. +2. Apply FDR or Bonferroni correction to control the errors when performing multiple hypothesis testing. +3. The value of presenting an entire distribution as a prediction. +4. Estimate a population distribution using simulation (if there's time). Example: wedding planning: https://www.tomasbeuzen.com/post/party-planning-probability/ +5. Calculate, interpret, and visualize prediction intervals. + +### Week 12: Project Week +This week is designed as independent studying where the students will be working on a project that aims at answering an inferential question with the material they have learned from weeks 1-11.