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JUPYTERLAB_GUIDE.md

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+# 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.
+
+
+
+

lecture-learning-outcomes.md

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+# Learning Outcomes by Week
+
+### Week 1: Introduction to Statistical Inference and Sampling
+From this section, students are expected to be able to:
+1.	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.