Submitting homework All homework assignments will be submitted on Gradescope, which requires a PDF version of your assignment. You hand write your homework and use a scanner in the libaray or your phone to create a PDF version. Here are Gradescope instructions for homework submission, including how to create a PDF. If you're interested, you can use the Gradescope App Here is a full tutorial on submitting an assignment Format and content I expect your homework submissions to be clearly written and well organized. I elaborate on this below.

Data Collection D.1: (core) The table below shows the first 8 observations from a sample of 200 individuals, who reported their age, race, income, and job satisfaction score (on a scale from 0 to 100). What are the cases?The cases are the 200 respondents. What are the variables recorded in this study? List them and identify each as either categorical or quantitative.Age (Quantitative), Income (Categorical), Race (Categorical), Score (Quantitative) D.2 (core) Below is a brief overview of an experiment published in Archives of General Psychiatry. Over a 4-month period, among 30 people with bipolar disorder, patients who were given a high dose (10 g/day) of omega-3 fats from fish oil improved more than those given a placebo.

This is an outline of material for the concept quizzes in Stat 120 for Spring term 2024. To study, I recommend carefully going through your class notes, homework problems, daily prep, and daily activities. Be sure that you review actively, intermixing reading, thinking, solving problems, and asking questions. I have included bullet points of important topics to study, but I do not guarantee that it is an exhaustive list. There are also example quiz questions included, but this does not mean the quiz questions will of identical in structure. Quiz coverage Each quiz will include all of the topics that we have discussed up to that point in the course, but remember you get multiple attempts on each so only attempt those that you feel prepared for. Below is a table outlining the new topics that will be included on each quiz (but the old topics will have new problems as well if you want to attempt them). Quiz

Stat 230, Case study 2 The worldbank data set consists of 32 variables regarding economic development in African. The information is collected at the country level, and is from 2019. The data set was pulled from the World Banks DataBank of World Development Indicators on October 11, 2022. Below are brief descriptions of each variable included in the data set, but you can find more verbose descriptions on the DataBank cite, and you can certainly do a little background reading to help clarify some of these quantities as necessary. There are some missing values, so be aware of this as you explore the data! country: Name of the country code: Abbreviated country code region: Region of Africa based on the United Nations regions

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. 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

Learning Target Quiz #4 will include questions on learning targets 16-20. In addition, you can reattempt targets that appeared for the first time on Quiz 3. 16. Given a fitted hiearchical model, check whether the fitted model is appropriate/adequate. State the appropriate/available diagnostic tools for assessing the adequacy of a fitted model Given the diagnostics for your model, comment on whether the model assumptions are reasonable, and how wrong the model is. Explore the posterior predictive densities and comment on how reasonable the predictions are Example question:

"Completing" a Learning Target requires successful demonstration of understanding on that Learning Target. The primary way to demonstrate skill is to complete a problem for the target on a Learning Target Quiz. But it's not the only way! You can also demonstrate this understanding through an oral quiz. This document gives instructions on how these alternative methods work. Demonstrating skill through an oral quiz In this method, you'll do the following: Schedule a 15-minute slot during appointment hours and indicate one Learning Target that you'd like to be quizzed on. I will make up a new problem for that Learning Target, similar to one found on a regular quiz. Then you simply drop in to drop-in hours at the time you selected. I will give you the problem to work, and you'll work it out "live" while I listen and observe. You can talk through your work, or work in silence, and use either paper or the board.

Learning Target Quiz #3 will include questions on learning targets 1, 7, and 8. In addition, you can reattempt targets that appeared for the first time on Quiz 2. 9. Given a model specification, explain the steps of the Metropolis algorithm. Clearly outline the key steps of the algorithms Understand how the proposal and acceptance probability work What acceptance rate should we target? Identify situations where MCMC is needed Example question: Suppose that you have a random sample, $x_1, \ldots, x_n$, from a Galenshore distribution with PDF.

Learning Target Quiz #2 will include questions on learning targets 1, 7, and 8. In addition, you can reattempt targets that appeared on Quiz 1. 1. Given your prior belief, specify an appropriate prior distribution for a univariate model. Know what prior elicitation (tuning) is and describe what it means to do elicitation well Define informative prior, weak/diffuse/flat prior, conjugate prior and understand when each might be used Construct a discrete prior distribution to express the plausibility of parameter values before sampling Construct a continuous prior distribution to express the plausibility of parameter values before sampling. The book discusses two primary methods: the prior sample size method and the quantile method. Construct a weakly informative (vague) prior distribution to express the lack of prior information Construct a conjugate prior distribution to simplify calculation of the posterior distribution. The book presents a few examples (e.g., beta-binomial) but you should be able to utilize this idea more generally.

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. 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

Learning Target Quiz #1 will focus on univariate models. It will include questions on learning targets 2-6. 2. Given the prior distribution and data, derive the posterior distribution for a univariate model. Understand how the three steps of inference (prior, likelihood, and posterior) fit together Be able to derive the likelihood function Identify the likelihood through the story of a distribution (i.e., choose a logical distribution to model the data) "The posterior is proportional to prior times likelihood." Derive the posterior distribution using Bayes' rule up to the normalizing constant Given a sampling distribution (likelihood), show that a prior is conjugate

This document contains all the details for what constitutes “successful” or “acceptable” work on each type of assignment in the course. Use the table of contents in the upper-left to navigate to specific assignment types. Learning Targets There are two ways to demonstrate skill on a Learning Target: By working a problem on a Learning Target quiz or by working a problem in an oral quiz. Both written and oral quizzes are graded on the basis of completeness and correctness. A mark of Successful is recorded if the work meets the standards; a mark of Retry is recorded otherwise. The work receives a mark of... If it meets these criteria