Eastern Michigan University Creed

Basic Information

General Description

• summarize and analyze data, in tables and visualizations;
• make predictions, by fitting trendlines and judging how good they are;
• make inferences: quantify how uncertain we are about what's going on; and
• use theoretical distributions to model what's going on

This course alone will not be enough to prepare you to teach AP Statistics. From an MET draft document: "it is clear that extensive additional preparation in statistics is required to teach AP Statistics. Several graduate courses in statistics are desirable (chosen in individual consultation with faculty in a graduate statistics program). The minimum preparation would be a good lower-level introductory statistics course, based on the sort of textbooks mentioned above, followed by either a second undergraduate statistics course or a graduate statistics course designed for teachers (see the MET Professional Development website for details about such a course)." http://cbmsweb.org/MET_Document/index.htm

Course Catalog Entry

Prerequisites

Follow-up courses

MATH 419W - Introduction to Stochastic Mathematical Modeling (Gen Ed Area I, W--writing intensive)
ECON 415 - Introduction to Econometrics
STAT 370 - Probability (prerequisite is Calc 2)
STAT 460/576 Applied Survey Sampling
STAT 461/575 Linear Regression Analysis
STAT 462/572 Design and Analysis of Experiments
STAT 468 - Introduction to Biostatistics
STAT 469 - Introduction to Categorical Data Analysis
STAT 474W/574 - Applied Statistics (Gen Ed Area I, W)

STAT 571 Mathematical Statistics I: Probability Theory
STAT 573 Statistical Data Analysis
STAT 577 Applied Multivariate Statistics
STAT 578 Nonparametric Statistics. 

Class Format and Meetings

 
Tue/Thu 2:00-3:15 Stat 360-1, Pray-Harrold 304, CRN 23138

3 credit hours.

Class meetings will be mostly interactive lectures, with some time to work on problems in class, but hardly ever time to go over problems from the homework; that is best done in office hours or by email before the HW is due.

Instructor information

Office Hours and other help

Mon/Wed:
	11:00-12:00 grant meeting (Wednesdays)
	 1:00- 2:00 office hours
	 2:00- 3:15 Math 319, room TBA
	 3:15- 4:30 office hours
Tue/Thu:
	10:30-11:00 office hours
	11:00-12:15 Math 110, Pray-Harrold 406
	1:00- 2:00 office hours
	2:00- 3:15 Stat 360, Pray-Harrold 304
	3:15- 4:30 office hours
Fri:
	No official office hours, but I'm often on campus.
	E-mail me to make an appointment, or drop by.
	11:00-12:00 department colloquium (once a month)
	12:30-2:30 department meeting (once a month)
	2:30-3:30 research meeting

I am also happy to make appointments if you cannot come to the general office hours. Please send me e-mail to arrange an appointment. However, I am not available when I am teaching other classes (see above).

The Mathematics Student Services Center (or "Math Lab") is also here to help you, in Pray-Harrold 411 Their hours are posted here. Please give them a call at 734-487-0983 or just drop by.

Some assignments in this course will be in the form of papers, which I want to be well written. Please consult with The Writing Center for help in tuning up your writing.

Teaching philosophy, interests

I am a very applied mathematician. Applied, applied, applied. Not pure. Impure. I try to focus on real-world problems, rather than artificial drill problems (though I do recognize the need for some drill). My classes spend much more time on formulating problems (going from the real world to math notation and back) than on proving theorems. If you want the theoretical basis for anything we are discussing, please ask!

My general math interests are in Industrial Engineering and Operations Research (IEOR). In particular, I do research in applied probability and queueing theory, the mathematics of predicting how long it takes to wait in line for service. You can learn more about this in Math 319 and 419W when I teach them. I also enjoy teaching about cost-minimizing/profit-maximizing methods called Non-Linear Programming (NLP) in Math 560, Optimization Theory.

Required materials

Textbook: Introduction to Statistics & Data Analysis, 4th edition, by Peck, Olsen, and Devore amazon link. We do actually use the textbook, fairly heavily in fact. For Winter 2020 we will still use the 4th edition; if it's hard to find a cheap (under $40) used copy online, email me ASAP.

A lot of our work will be done on computers, usually in Excel or a similar spreadsheet. If you had been waiting for a good reason to buy a laptop, this is it. Spreadsheets other than Excel (such as OpenOffice/LibreOffice, Google Docs, etc.) work reasonably well for most things in the class, but some things really don't work well without name-brand Excel. Fortunately, it's available free to EMU students (as of Fall 2016). Email me to ask for details.

In the long run, most math/stats/compsci/data-science people should learn either Python or R or both. If we have time this semester, we will try to introduce some Python (via Notebooks) or R/Rstudio and Rmarkdown. These might be particularly helpful for projects. The good news is that these programs are all free!

Course Web Pages

Some course files are posted on my home page, but most files (homeworks, homework solution keys, some handouts) are only in Canvas

We will use on-line homework submission and gradebook via EMU Canvas to keep track of grades. You are expected to keep an eye on your scores using the system, and get extra help if your scores indicate the need.

Supplementary Materials

• The Cartoon Guide to Statistics
• The Manga Guide to Statistics
• What is a p-value anyway, by Andrew J. Vickers

Course Content

Course Goals

The department's list of Student Learning Outcomes for this course are as follows: Students will be able to

1. Apply various well known discrete and continuous probability distributions for modeling.
2. Distinguish between underlying population and sampling distributions.
3. Derive the parameters for the sampling distributions of proportions and means for one-sample and two-sample problems.
4. Understand the principles and methods of estimation and hypothesis testing.
5. Apply exploratory and the inferential methods to analyze data.
6. Understand the assumptions and limitations of statistical procedures and the scope of conclusions.
7. (Use linear regression and appropriate data transformations.)

Grading Policies

Attendance

Regular attendance is strongly recommended. There will be material presented in class that is not in the textbook, yet will be very useful. Similarly, there are things in the textbook that are might not be covered in class, but are still very useful. If you must miss a class, arrange to get a copy of the notes from someone, and arrange for someone to ask your questions for you. If you are stuck on occasion without your usual child care, you may bring your child to class, and need not even get advanced permission (this is my personal policy--I don't know if EMU has a policy). Please be considerate to your classmates if your child becomes disruptive.

My lectures and discussions mostly use the document camera, along with demonstrations in Excel and other mathematical software. I do not usually have PowerPoint-like presentations, and thus cannot hand out copies of slides.

Homework

Homework will be assigned about twice per week, usually 2 assignments per chapter. All homework should be submitted via the Canvas dropbox. The policy is: if it isn't in Canvas, it doesn't exist for grading purposes. Any assignments emailed to me will be treated as drafts, and I will try to respond to them with helpful advice. Most homework will be graded simply on completion, and solution keys released (in Canvas) the day after the homework is due. It is your responsibility to check your answers against the solution keys and ask questions about things you do not yet understand (email about that is welcome!).

I am open to doing contract honors for this class for students in the Honors College. Please contact me if you are interested in doing so.

Exams

There will be a midterm exam and a final exam. Quizzes might also occur, announced or not, during the semester.

Project

You will do a project where you create a question, decide how to study it, design a data collection method, collect data, and analyze it. You will write a project proposal so I can be sure you are on the right track, and a final report, which is usually about 5 to 10 pages long (could be as short as 3 pages, if aiming to submit it to a national competition). The grade breakdown for the project is roughly:

• 5 pct: proposal
• 80 pct: work and written report
• 15 pct: presentation file (PowerPoint, usually)

On average, students should spend a total of about 30 minutes in office hours discussing the project. Plan for this in advance! Teams of 2 are allowed/encouraged, but no team bigger than 2 is allowed (with rare pre-approved exceptions).

Overall Grades

There is no systematic grade-dropping method like "lowest 2 scores will be dropped". In the unfortunate event of a need, the appropriate grade or grades might (at the instructor's discretion) be dropped entirely, rather than giving a make-up. You are highly encouraged to still complete the relevant assignments that were dropped, and consult with me during office hours to ensure you know the material. If a student falls hopelessly behind in the homeworks (aside from the project), they may request a grand make-up assignment (which might be done at home or in the math testing room, at the instructor's discretion). This request might or might not be granted, at the instructor's discretion. Note that the students who relied on this option recently did very poorly on the grand make-up.

• 50 percent for all the homework, possible quizzes, and the project together,
• 20 percent for the midterm exam
• 30 percent for the final exam.

  
0	to	<52:	F
52	to	<56:	D-
56	to	<60:	D
60	to	<64:	D+
64	to	<68:	C-
68	to	<72:	C
72	to	<76:	C+
76	to	<80:	B-
80	to	<84:	B
84	to	<88:	B+
88	to	<92:	A-
92	to	infinity:	A

General Caveat

Advice from My Other Students

• * work in groups * start the first day assignment is given * don't take too many credits w/ this class * ask a lot of questions * utilize Dr. Ross
• Do go to his office hours more than you normally would; if you have a question ask don't wait.
• See Prof. Ross in office hours and don't be afraid to email him. He is usually very helpful and approachable.
• Plan on visiting Prof. Ross during office hours in order to do well in the class. You will learn a lot in the end, but be ready to work.
• attend the office hours Prof Ross is really good at explaining & helping out with the homework
• WORK TOGETHER!
• Take notes during the computer days and send yourself the excel sheets.
• Go to class. The computer days help even if you know excel well.
• Go to class. Go to office hours and pick project that you're energized about and interested in even if they're harder. It will make this math class the best one you've ever taken.
• Don't drop the class! It sounds impossible in the beginning, but stick with it.
• Don't procrastinate.
• Start projects ASAP.
• Ask questions!!! The professor will guide you along the way like Yoda.
• Talking to anyone about your projects or the homework, be it Prof. Ross or other students, is a really, really good idea.
• Never be afraid to ask for help.
• If project falls through, have backups.

Land Acknowledgement

University Writing Center

Standard University Policies

Food Pantry