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\begin{document}

\def\course{36-617: Applied Linear Models}
\def\semester{Fall 2022}
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\begin{center}\Large\bf
\course \\
\semester
\end{center}

\begin{center}\Large
\tt              MW     01:25PM--02:45PM         BH A53\\
Zoom Meeting ID: TBA \\  %  \footnotemark: 954 6049 2939 / Passcode: 042583\\
% http://www.stat.cmu.edu/\~{}brian/763
Class Materials: https://canvas.cmu.edu/

\end{center}
%\footnotetext{Class web link: 
% https://cmu.zoom.us/j/95460492939?pwd=dFlYbnBsRDdFVXF5MUh4TzlIbWlndz09}
\begin{center}
``[I]t makes sense to base inferences or conclusions only
on valid models'' {\em -- S.J. Sheather (2007)}

``All models are wrong but some are useful'' {\em -- G.E.P. Box (1978)}
\end{center}


\head{Course Information}

\noindent
\begin{tabular}{lcl}
\hspace*{0.5em}
\begin{tabular}[t]{l}
{\it Instructor:} \\
\hline
Brian Junker, Statistics \& Data Science\\
http://www.stat.cmu.edu/people/faculty/brian \\  
% ~ \\ %% 412) 268-8874 \\
{\tt brian@stat.cmu.edu} \\
132E Baker Hall \\
% ~ \\ %% {\tt http://www.stat.cmu.edu/\~{}brian} \\
\vspace*{\fill} \\
{\it Office Hours:} \\  %% (BH132E)
\hline
% Zoom Meeting ID\footnotemark: 923 9360 1531 \\
% Passcode: 491610\\
% 9:30--10:30am Mon \& Wed, Pittsburgh Time\\
% 3--4pm Mon \& Wed, Pittsburgh Time \\
Noon--1pm, Mon \& Wed\\
{\em (or by appointment)}.
\end{tabular}

& \hspace*{-1em} & %% \hspace*{4em} &

\begin{tabular}[t]{l}
{\em TA:} \\
\hline
Lorenzo Tomaselli
\\
https://bit.ly/3ADrbZW \\
% https://www.cmu.edu/dietrich/statistics-datascience/people/phd/lorenzo-tomaselli.html \\
% ~ \\
{\tt  ltomasel@andrew.cmu.edu} \\
129G Baker Hall\\
% ~ \\ %% {\tt  hhttp://stat.cmu.edu/\~{}bmcveigh/} \\
\vspace*{\fill} \\
{\em Office Hours:} \\
\hline
% Meeting ID\footnotemark: 919 9389 2805\\
% Passcode: 664608\\
% 8--9pm Sunday, Pittsburgh Time\\
% Noon--1pm Monday, Pittsburgh Time\\
11am--Noon, Fridays \\
{\em (or by appointment)}.
\end{tabular}

\end{tabular}
% \footnotetext{BJ Office hours link: 
% https://cmu.zoom.us/j/92393601531?pwd=TE50aDZKRFA5MmFpRnBQQjNUditSUT09}
% \footnotetext{BL Office hours link: 
% https://cmu.zoom.us/j/91993892805?pwd=M2pSbFRiRGJxdFZzK0IwaWFiNThHZz09}


\head{Prerequisites}

\noindent
You must be an MSP student to take this class\footnote{Exceptions require special permission.}.  Beyond that, 
there are no formal prerequisites for this class.  However, {\em I expect
you to be familiar with statistical theory and the statistics of
applied linear regression at a junior or senior undergraduate
level.
% 
You will also be expected to know, or learn quickly, the computational
software used for this course.  That is, primarily {\tt R}.}\footnote{Python is a possibility for some (but not all) work in this class, but the class will be geared to R \& Rstudio.}
%
All homework, take-home tests, and projects will be submitted online as pdf's on Canvas ({\tt https://canvas.cmu.edu})\comment{If you are registered for this course, it will show up when you open the Canvas website.  If not, please see me about whether it is appropriate for you to be in this course.}. There are several ways to prepare pdfs:
\begin{itemize}\tight
\item Write your assignment with pen/pencil and paper, and then scan to pdf
\item Write your assignment in Microsoft Word and save as pdf
\item Write your assignment in \LaTeX, and generate pdf output % (e.g. using {\tt pdflatex})
\item Write your assignment using {\tt rmarkdown} or similar tools in {\tt rstudio}, and knit to pdf
\end{itemize}
{\tt Rmarkdown} is nice for homeworks involving R, but it tends to encourage bad habits when you are writing reports and papers.  So for the project in this course I \ul{\bf strongly recommend} you use \LaTeX{} or MS Word. \\ 

\noindent
Some guidance on Python vs R for this class is provided on page~\pageref{s:computing} below.\\

\noindent
Please feel free to contact me if you have any questions or need
additional information.

\clearpage

% \vspace*{0.25em}
\head{Texts and Course Materials}

\noindent
Almost all of the material for this course are available on-line.  In particular the main texts for the course is available to download as a pdf free of charge to members of the CMU community. In addition, some other reading (especially about writing and communicating in statistics) will be available on-line in Canvas.

\vspace*{0.25em}
\head{Texts }

\noindent
{\em The main texts for this course are }
\begin{description}\tight
\item Sheather, S.J.  (2009).  {\em A Modern Approach to Regression with R}. New York: Springer Science $+$ Business Media LLC.$^*$
\item James, G., Witten, D., Hastie, T., \& Tibshirani, R. (2013). {\em An introduction to statistical learning: with applications in R. }  New York: Springer Science $+$ Business Media LLC.$^*$  See also http://www-bcf.usc.edu/\~{}gareth/ISL/
\end{description}
{\em Later in the course we will also take material from}
\begin{description}\tight
\item Gelman, A. \& Hill, J. (2007). {\em Data Analysis Using Regression and
  Multilevel/Hierarchical Models.}  NY: Cambridge Univ Press. 
\item Lynch, S. M. (2007). {\em Introduction to applied Bayesian statistics and estimation for social scientists.} Springer
Science \& Business Media LLC.$^*$
\end{description}
{\em Other books you may find useful include  }
\begin{description}\tight
\item Weisberg, S. (2013). {\em Applied Linear Regression.} John Wiley \& Sons.  
\item Weisberg, S. (2013). {\em Computing Primer for Applied
Linear Regression, 4th Edition,
Using R}.  Available at {\tt http://www.statpower.net/Content/313/R Stuff/alrprimer.pdf}
\item Berk, R. A. (2016). {\em Statistical learning from a regression perspective, 2$^{nd}$ Ed.}. New York: Springer Science $+$ Business Media LLC.$^*$ ~~~~~~~~(Do not get the 2008 1$^{st}$ edition.)
\end{description}

\blankfoot{$^*$You can buy the physical book many places online, e.g. {\tt smile.amazon.com}, but you can also download the pdf for free at {\tt link.springer.com} if you access through the CMU campus or VPN networks.  I will also put pdf's on Canvas.} 



\vspace*{-0.75em}
\head{Course Description and Course Objectives}

\noindent
Linear regression and its generalizations are the basic modeling toolkit---some would say the {\em whole modeling toolkit}---of applied statisticians and data scientists.  Almost every applied statistics or consulting problem that involves ``inputs'' and ``outputs'' can be solved, or at least profitably explored, using regression techniques.  So it is essential that, as an applied statistician or data scientist, you understand how regression works, and practice using it.  

Using regression in practice usually also involves translating a real-world problem into a techncial form that regression can be applied to, performing regression analysis, and translating back into real-world language, a process I sometimes label ``$ABA^{-1}$''.  
%
The $ABA^{-1}$ process is the first step in a crucial part of the work of statisticians: effective communication.  You will also practice communication of statistical results, using a report format that is useful for empirical scientific research.


By the end of this course you should be able to:
\begin{itemize}\tight

\item Understand the tension between ``valid'' models and ``wrong but useful'' models.

\item Carry out the $ABA^{-1}$ process: translate from real world to quantitative terms, analyze quantitatively, and translate back to real world.

\item Understand the machinery of linear regression well enough to use it intelligently.

\item Build, fit and critically evaluate linear regression models and some generalizations of them, in a variety of messy, real-world settings.

\item Communicate statistical results clearly in writing, from sentence to paragraph to full report, especially using a modified IMRaD format called IDMRaD.

\end{itemize}
This is a lot of material to cover.  I would like to move quickly, but if I start to lose you I will slow down, since a good understanding of a few things is better than a poor understanding of many.

% \clearpage

\vspace*{0.25em}
\head{Computing}\label{s:computing}

\noindent
We'll mostly be working in R with supporting libraries, and as I suggested above, it is good to learn how to use \LaTeX{} to produce documents.
%
You should install this software to run on your own laptop:
\begin{itemize}\tight
%
\item {\tt R}, a statistical analysis and programming environment
  (best to have the most current version).  \\
      See {\tt http://cran.r-project.org/}
      \begin{itemize}\tight
      \item You should also install {\tt RStudio}, from {\tt https://www.rstudio.com/}.  This provides an integrated development envioronment (IDE) for R, and also provides support for {\tt rmarkdown} and other useful tools. I do not use R Studio much in class, but it is a fine tool, especially for documenting larger data analysis and model fitting projects.  The homework solutions I provide will often be in Rstudio.
      \end{itemize}

\item \LaTeX{} is the academic standard technical typsetting system.  Use it, or MS Word, for formal reports, (not {\tt rmarkdown}).  \LaTeX{} is distributed in most \TeX{} systems.  The flavor of \TeX{} that I have always used is called MiK\TeX.  It is available at {\tt https://miktex.org/}, for both Windows and Mac.
\begin{itemize}
\item
Lately I've been doing more and more \LaTeX{} work using Overleaf ({\tt https://www.overleaf.com/}).  Overleaf has lots of online help and a nice user interface within a web browser---without installing \TeX{} on your own computer.
\end{itemize}
%
\end{itemize} 
% 
% There may also be an opportunity to use SAS in this class; I will distribute SAS
%  instructions if needed.
% \\[-.5em]
%
% \noindent
There is lots of help for R, RStudio and \LaTeX{} on the www.  I will post some links on Canvas.\\

\noindent
{\em What about Python?} More and more students come to the MSP program familiar with Python, and you will learn and use Python in other MSP classes.  It is especially useful for manipulating larger data sets, and for ``gluing'' together numerical, graphing and data manipulation routines from numpy, pandas, plotly, seaborn, etc.  (Many of the same capabilities have been built into ggplot and the tidyverse in R.)  I do not plan to use Python in class, but I do not mind if you use Python for some parts of your assignments, project work, etc. --- {\em with two important caveats:}
\begin{itemize}
\item Most of the data sets in this course will be relatively small, and Python conveys no advantages over R for manipulating smaller datasets.
\item I will focus on methods available in R for model fitting, variable selection and diagnostics.  You will likely not be able to complete assignments focusing on these topics using Python alone.
\end{itemize}

\clearpage

\vspace*{0.25em}
\head{Online Resources and Etiquette}

\vspace*{-0.75em}
\begin{description}

\item[Canvas:] Canvas (https://canvas.cmu.edu) has all materials (class notes, handouts, homework assignments and solutions, etc.).  It is also where you will 
\begin{itemize}\tight
\item Take occasional (weekly?) quizzes
\item Turn in weekly homework assignments and take-home tests (in the ``Gradescope'' app within Canvas)
\item Submit and review data analysis papers, and
\item Ask for help outside of office hours (in the ``Piazza'' app within Canvas).  
\end{itemize}
You can also find all of your grades for this course, throughout the semester, in Canvas.

\item[Zoom:]
During the 2020-2021 school year, when all of our classes were remote on Zoom, I recorded my live lectures---especially keeping in mind students in Asia who were not able to travel to Pittsburgh.  It turned out that {\em all} students liked to have the recordings to refer to when reviewing their notes, planning data analyses, etc.  I plan to record live classes again this fall and post the recordings on Canvas, so that you can review a class whenever you like.  If we go ``remote'' at some point due to the pandemic, or if some students are not able to attend class for some reason, we will already have Zoom set up for the class as well.\\ [-1em]

\item[Gradescope:] You will need to upload weekly homework assignments as pdf files on Canvas using the Canvas app ``Gradescope''.  Your homework will also be graded online, and you can see your grades and the TA's comments, all in Gradescope.

\item[Piazza:] If you have questions that cannot be easily asked or answered in class or in office hours, Piazza is a good place to post your questions. You can answer or comment on each others' posts if you wish, or wait for one of us to answer.  The TA and I will especially monitor Piazza during our office hours, and I will also check in on Piazza occasionally throughout each week.  \\[-1em]

Please be kind in your questions, answers and comments: On Piazza, there are no dumb questions, and no dumb mistakes.

\end{description}

\head{Laptops in Class}

\noindent
In this course, it makes sense to bring your laptops to class.  The text is a pdf, most class materials will be online, and there will be in-class activities that require your laptop. 

{\em This places a responsibility on you, however.  As tempting as it is, please do not be distracted by email, online social networks, online shopping, etc.  I want your attention focused on the class.}  

If you get bored and your attention drifts, ask a question in class.  If that doesn't do it for you, let's talk in office hours about how we can help keep you focused in class.

\clearpage

\clearpage

\head{Student Work}

\noindent
Your work for this class will consist of:

\begin{center}
\begin{tabular}{ll}
10-ish Homeworks &    20\% \\    
Monday Quizzes &      10\% \\
Take-Home Midterm & 25\% \\
Final Report Project & 25\% \\
Peer Review       &   10\% \\
Participation     &   10\% \\
\end{tabular}
\end{center}

\begin{itemize}\itemsep=0pt\parsep=0pt
\item \ulem{Homework:}
I intend to give roughly 8--10 assignments.  Homework will provide practice developing and exploring
theoretical material, using software to analyze data, and some writing exercises.  You \ul{\em\bf are} allowed to work with other students on these problems or refer to other
sources if you would like, unless I forbid it on a particular assignment. 
{\bf\em If you work with others, or use 
any other sources, please list you collaborators and other 
sources on your assignment.} 
% See also the section on Academic 
% Integrity above.

\item
\ulem{Monday Quizzes:}
These short quizzes (typically online on Canvas, most Mondays) are intended to help me gauge your understanding of the reading each week. There are no makeups, but you may drop your two lowest scores.

\item 
\ulem{Take-Home Midterm.} This is basically a homework assignment where you are \ul{\em\bf not} allowed to work with others in the class.

\item
\ulem{Final Report Project:}
With this project you will gain experience analyzing data and writing a clear report, in IDMRaD (Introduction--Data--Methods--Results--(and)--Discussion) format.
%
{\em Your work (writing, stastistical analysis, etc.) should be your own}, with three exceptions:\\[-1.5em]
\begin{itemize}
\item You can always talk to the instructor or TA, and you can use any information you learn in class, office hours, or Piazza.
\item You can refer to written sources on the web or in the library (e.g. books, journal articles, websites, blogs, questions asked and answered on quora, stackexchange, etc.) but you are not allowed to pose questions to any individual, group or other entity on line. 
\item Your rough draft will be reviewed by your peers in this class. You can use any information, suggestions, etc, in these peer reviews.
\end{itemize}
~\\[-3em]

\comment{
{\bf\em You must list all sources (people, books, websites, etc.) used in a list of references at the end of your paper.}  See also the section on Academic 
Integrity above.

{\em Prepare your project report as a single pdf and submit it on Canvas.} 
}

\item \ulem{Peer Review:}  You will also be reading each others' project papers and giving feedback to them.  You will be graded on the quality of your feedback.

\item
\ulem{Participation:} Participate in class, office hours, etc., as much as you can.  Your first goal is to get me to remember your name.  Your second goal is to get me to remember how much you participate in class.  If I can't remember your name or I can't remember your participation, you will get a low participation grade.

\end{itemize}

\noindent
{\em Prepare each hw, midterm or project paper as a single pdf and submit it on Canvas.} 

\noindent
In all your work, please label all output, plots, variables, etc., appropriately.
Always be judicious about including computer output and graphs: show
enough that we can clearly see what you are doing, but not so much
that we will get lost or bored leafing through your work!  A good rule
of thumb is to remove any figures, tables, graphs, etc. that you have not written 
something interesting about.

% {\em Because of the number of students in the class, and the variety of work 
% you will be doing, late assignments (homework, projects, etc.) will typically 
% not be accepted.}

\clearpage


\head{Academic Integrity}

\noindent
As members of a top-ranked academic institution, your academic
integrity is assumed and expected.  

Unless I specifically direct otherwise, your work is expected to be your 
own. For all work, if you get ideas or words from a website, journal
article, book, another person (in or out of this class), etc., cite
the source in your writeup, right where you use it.  Then put a
bibliography or list of sources cited at the end of the writeup.  {\bf\em If 
you are not sure what is allowed, or required, please ask me.}

Carnegie Mellon guidelines are listed at
{\tt http://www.cmu.edu/academic-integrity/} (click on the ``Student'' 
link near the top of the page there); however, I expect each of you to behave well above these lower 
bounds.

\head{Disability Resources and Other Concerns}

If you have a documented disability that is preventing you from doing the work in this class, please let me know so that we can take whatever steps are needed to accommodate your needs.  If I am not able to help, or you have other related questions or concerns, please contact your advisor or a trusted mentor, and/or CMU's Disability Resource Office (http://www.cmu.edu/hr/eos/disability/).  

For any other issues or special needs, please  contact me, your advisor 
or a trusted mentor, and/or the Office of the Dean of Student Affairs (http://www.studentaffairs.cmu.edu/dean/).

If at some point during the semester you are unable to attend class because of the pandemic, please let me know so that I can make arrangements for you to participate remotely.

% \clearpage

\head{A Note on Diversity}


\noindent
In this class, I will affirm and promote the inherent worth and dignity of every person, and I expect that every member of the class will do the same.  The University is enhanced by the diversity of its members, in gender, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion and culture: each of you can contribute ideas and perspectives that no one else can.  I will endeavor to present materials that are respectful of and accessible to all of our backgrounds and perspectives.  Please let me know ways to improve the effectiveness of the course for you personally or for other students or student groups. \\[-1em]

\noindent
CMU Resources that may be useful to you include:
\begin{itemize}\tight
\item The Center for Diversity and Inclusion: https://www.cmu.edu/student-diversity/
\item The Intercultural Communication Center: https://www.cmu.edu/icc/
\item The Office of Title IX Initiatives: https://www.cmu.edu/title-ix/
\end{itemize}

\comment{

\noindent
Our aim is that this course is well-served by all backgrounds and perspectives, and we want all students' learning needs be addressed both in and out of class. One of the best things about Carnegie Mellon is that an amazingly diverse group of people come together to learn, work, and achieve their goals. We value that diversity deeply---it is a resource, strength, and benefit of our community. It is our intent that class materials and activities are respectful of this diversity, whether it be in terms of gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and/or culture. Your suggestions are encouraged and appreciated. Please let us know if there are ways that we can improve the effectiveness of the course for you personally or for other students. That said, we will continually work to make our course as welcoming and effective as possible, rather than just wait for students to tell us what we are doing wrong.

% More generally, Zach is also on the IMS Committee on Diversity, Equity, and Inclusion (as well as a comparable committee within the statistics department), and is always open to hearing your thoughts on how statistics as a field can be more welcoming to people from any background.

}

\head{A Note on Life-Work Balance}

\noindent
We care deeply about this course material and are excited to teach it, but we care even more so about your well-being. We are always happy to talk with you about life at CMU and Pittsburgh, your future career and education, or just about anything, really, if it supports your well-being here. There are many resources around you---family, friends, advisors, mentors, etc.---and you should take advantage of them. We also encourage you to be aware of professional resources such as Counseling and Psychological Services (CaPS; 412-268-2922 or {\tt http://www.cmu.edu/counseling/}). However, if you or someone you know is in a life-threatening situation, call the police immediately (8-2323 on campus, 911 off campus).

\comment{

\head{Disability and other Special Needs}

Carnegie Mellon makes great efforts to provide physical and 
programmatic campus access to everyone. Disability Resources 
ensures that qualified individuals receive reasonable 
accommodations and that they further receive the rights 
and protections to equal access programs and services as 
guaranteed by the Americans With Disabilities Act (ADA) 
and Section 504 of the Rehabilitation Act of 1973.

If you have a documented disability, please let me know so that
we can take whatever steps are needed to accomodate your needs.

Please contact CMU's Disability Resources office 
(http://www.cmu.edu/hr/eos/disability/) if
\vspace*{-0.5em}
\begin{itemize}\tight
\item You think you may have a disability and want to document it;
\item You have a documented disability that is not being adequately
accomodated.
\end{itemize}
\vspace*{-0.5em}
For other issues and special needs, please contact me, your advisor 
or another trusted mentor, and/or the Office of the Dean of Student Affairs 
({\tt https://www.cmu.edu/student-affairs/resources.html}).
\vspace*{-0.5em}

}


\clearpage

\head{Tentative Schedule of Topics}

\noindent
The timing of topics and projects is approximate below, but this will give you some idea of how the course will progress.  The dates for the \r{take-home} and \r{project} are tentative and will be updated as needed during the course. G\&H$=${\em Gelman \& Hill}; ISLR$=${\em Introduction to Statistical Learning with R}.\\[-2em]
\begin{center}\spacing{1}
\begin{tabular}{|p{0.75in}|l|p{2.4in}|p{1.7in}|}
\hline
{\bf Week} & {\bf Dates}
           & {\bf Tentative Topics} & {\bf Tentative Sources}\\ \hline \hline
           & Aug 22--26
           & \bf Getting Ready
           & Sheather, Ch 1, 2, 3  % \\
%        & 
%        & \em Project 1 assigned % XXX
%        &
       \\ \hline
Week 1 & Aug 29, 31
       & \bf Intro, Multiple Regression
       & Sheather, Ch 5 \\
       &&& ISLR 3.1, 3.2\\
       &&& G\&H Ch 3  % \\
%        & 
%        & \em Project 1 assigned % XXX
%        &
       \\ \hline
Week 2 & Sep \g{5 (no class$^1$)}, Sep 7
       & \bf Qualitative Predictors
       & ISLR, 3.3.1, 3.3.2, handouts
       \\ \hline
Week 3 & Sep 12, 13
       & \bf Diagnostics \& Transformations
       & Sheather, Ch 6  \\
       &&& ISLR 3.3.3 \\
       &&& G\&H Ch 4 %  \\
%        & 
%        & \em Project 1 Due % XXX
%        &
       \\ \hline
Week 4 & Sep 19, 21
       & \bf Variable Selection
       & Sheather Ch 7   \\
       &&& ISLR Ch 6 \\
       &&& G\&H Ch 4 
       \\ \hline
Week 5 & Sep 26, 28
       & \bf Logistic Regression
       & Sheather Ch 8 \\
       && \r{\em Take-Home Midterm Assigned} & ISLR 4.3 \\
       &&& G\&H Ch 5  \\ \hline
Week 6 & Oct 3, 5 
       & \bf Generalized Linear Models
       & Sheather Ch 8 \\
       && \r{\em Take-Home Midterm Due} & G\&H Ch 6  \\ \hline
Week 7 & Oct 10, 12
       & \bf Nonparametric Regression
       & ISLR Ch 7   \\
       &&& Sheather Appx\\
       &&& handouts 
       \\ \hline
       & Oct 17-21
       & \g{FALL BREAK} % XXX
       &
       \\ \hline
Week 8 & Oct 24, 26
       & \bf Causal Reasoning
       & G\&H Ch 9, 10  \\ \hline
%       &  
%       & Simulation?  Causal inference?
%       &  \\ \hline
Week 9 & Oct 31, Nov 2
       & \bf Multilevel and Mixed Effects Models
       & Sheather, 10.1   \\
       &&& Intercepts: G\&H Ch 12\\
       && \r{\em Project assigned} & Slopes: G\&H Ch 13 % (skip or skim 13.3, 13.6, 13.6) \\
       \\ \hline
%        & \bf Generalized Least Squares
%        & Sheather Ch 9 (\& maybe Ch 4)  \\ \hline
% Residuals, Estimation and Model Selection
% Handouts; stuff from G&H

Week 10& Nov 7, 9
       & \bf Multilevel logistic regression \& GLMs
       & G\&H parts of Ch’s 14 \& 15   \\ \hline
Week 11& Nov 14, 16
       & \bf Residuals, Estimation and Model Selection
       & Handouts; stuff from G\&H  \\ \hline
Week 12& Nov 21, \g{23 (no class)$^2$}
       & \bf Bayes \& Shrinkage 
       & Lynch Ch 3, 4 \\
       &&& G\&H parts of Ch 16 \\ \hline
Week 13& Nov 28, 30
       & \bf STAN and MLM's 
       &  Lynch, Ch 9 \\
       && \r{\em Project due} & G\&H parts of Ch 17 % , handouts  \\
       \\ \hline
Week 14& Dec 5, 7
       & \bf MLM's with STAN
       &  Handouts; maybe parts of G\&H Ch's 18, 21 \\ \hline
%
% Week 15& Dec 7, 9
%        & \bf Special Topics
%        &  To be determined\ldots \\ \hline
%
% \multicolumn{4}{l}{$^*$ BJ absent these days.  Stay tuned for
%   other arrangements.}\\
% \multicolumn{4}{l}{$^{**}$ We will skip, abbreviate, or modify this
%   topic, as time \& interest permits.}
% \multicolumn{4}{l}{$^*$All Chapters from Sheather unless otherwise noted.} \\
\multicolumn{4}{l}{\g{$^1$No Class Sep 5: Labor Day (US Holiday).}} \\
\multicolumn{4}{l}{\g{$^2$No Class Nov 23: Thanksgiving (US Holiday) Nov 24.}} \\
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% \multicolumn{2}{l}{}\\
% \multicolumn{2}{l}{~}&
% \multicolumn{2}{l}{}\\
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\hline The appendices of G\&H contain brief, but {\em very} useful
advice!  If you refer to and follow the advice in appendices A and B,
your work as an applied statistician or data scientist will be much better!\\ \hline
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