Stat 360 (formerly Math 360) section 1: Statistical Methods

Prof. Andrew Ross

Winter Semester 2020

Eastern Michigan University Creed

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We believe that the RELATIONSHIPS we have and those we continue to develop will support us as we learn and grow together as a community.

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Basic Information


This version posted on: 2020-01-06

General Description

In this course you will learn to: This is an introductory but ambitious statistics course, often taken by students from math-affiliated disciplines. We aim to keep our eye on the big ideas of statistics: Distribution, Inference, Model, Sample, and Variation. There are some skills that most statistics and computer science people should pick up, but that this course doesn't have room for. In particular, the database language SQL is important. So is the ability to use a professional statistics package like "R". This course will, as often optional material, pay particular attention to the need of future math teachers (math-secondary-education majors), as well as math minors and computer science majors. The K-12 Common Core State Standards (CCSS) require much more statistical thinking than previous standards have included.

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

A comprehensive overview of statistical methods and analysis with applications. Topics include descriptive statistics, probability theory, random variables and probability distributions, sampling distributions, estimation and testing hypotheses, correlation and regression, introduction to computer-assisted statistical analysis.

Prerequisites

Math 105 (College Algebra)

Follow-up courses

Some of these might have a Calculus prerequisite
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

In-person, not hybrid or online.
 
Tue/Thu 2:00-3:15 Stat 360-1, Pray-Harrold 304, CRN 23138


Detailed Schedule:
Stat 360-01  Prof. Andrew Ross; Tue/Thu 2:00-3:15, Pray-H 304CRN 23138     
Class#2020dayunitTopicRequired Additional ReadingHW AssignedHW DueBonus Tech Material after classR goalsData Set
11/7Tue1Intro; randomization example; car-insurance advertising; population vs sample, types of datam360-ch01-data-types.docxCh 1 preview* = deviation from usual 7-day delaytext-to-columnsimporting filesLock5stat Employed ACS (Income by Gender) ; car-insurance activity; TN STAR; Amer.Community Survey; HELPrct; CS data set?
21/9Thu1;2Discrete vs Continuous; PivotTables, Bar charts, Dotplots; Ch 2 Bias Ch 1 Pivot TablesPivot Table in Rsurvey data for pivoting; CS-sorting, randomizing order vs memory leak
31/14Tue2Random vs Stratified Samples, etc; Random Rectangles activitym360-ch02.2-2.3-powerpoint.pptxCh 2a; 2bCh 1*left/mid/right and =DATEhandling datesRandom Rectangle data or graphs; TN STAR, what was its design?
41/16Thu3Graphical Methods for Describing Data Ch 3Ch 2a*Kernel Density Estimates (KDEs)Histogram and KDE in Rhistogram data; processing times of sorting same data repeatedly; HELPrct drinks/day, previousrehabs; looks&personality scatter; logistic scatter?
51/21Tue4Center, Variability, Boxplots, Empirical Rule, z-scores, Percentiles & Plotsm360-ch04-notes.docxCh 4a and 4bCh 2bMarked ScatterplotsMarked Scatterplotsincome; also, above Histogram data
61/23Thu5Correlation; Regression Ch 5aCh 3plot the percentile curve; dotplot-histogram-crf-etcfit a linear model; see coeff,r,R^2existing spreadsheets
71/28Tue5Assessing fit; Nonlinear Relationships and Transformations 5b previewCh 4a and 4bvlookupplot residuals; fit log-transformed modelsdoctor lifetime estimates (artificial&real)
81/30Thu55 wrapup Ch 5bCh 5aSolver for nonlinear regressionfit polynomials; nonlinear fits; matrix of scatterplotsrisk estimation survey data
92/4Tue6Definition and Properties of Prob; Conditional Probability; independence, PIE, Bayes, Prob via Simulation"m360-ch06a-powerpoint.pptx
and
m360-ch06-bayes-table-handout.docx"Ch 6 ambulance travel distance simulationambulance travel simulation in R 
102/6Thu7Random Variables; Discrete and Continuous Distributions; Mean and StdDev; linear functions and sumsm360-ch07a-notes.docxCh 7aCh 5bsumproducttable joins: merge, dplyr, data.table"rainy days/year; tornadoes/year; true binomial example data?
shifting&scaling, E[nonlinear], adding blood bank demand"
112/11Tue7Binomial, Geometric; Normal; Checking and Transformations for Normality; Binom~Normal; QQm360-ch07b-notes.docxCh 7bCh 6dotplot-histogram-crf-qqpbinom, rbinom, pnorm, qnorm, rnorm; Q-Qincome and Log(income)
122/13Thu8Statistics and Sampling Variability; Sampling Distribution of a Meanexample Proposals and Reports8 previewCh 7aWhat-If Data Tables, 1-dimrandomization with replacementBillionaire incomes and ages
132/18Tue8Central Limit Theorem; Sampling Distribution of a Proportion Ch 8Ch 7bWhat-If Data Tables, 2-dimfor-loops 
142/20Thu9Point Estimation; Confidence Interval for a Proportion Ch 9aCh 8conditional formatting North Carolina birth records
 2/25Tue break week      
 2/27Thu break week      
153/3Tue9Confidence Interval for a Mean (incl. t-distrib) Ch 9b sparklinesCIs for groups, plottingestimators-dotplot-histo-crf-qq ; baseball win/loss by game, or weather precip/not; fake data showing trend or dependence; pivot&errorbars
163/5Thumidtermmidterm      
173/10Tue10Hypotheses and Test Procedures; Errors in Hypothesis Testing; Proportionm360-ch10a-powerpoint.pptxCh 10aCh 9aparallel axis plotsparallel axis plotsDetroit Tigers data
183/12Thu10Hypothesis Tests for Population Mean; Power and Probability of Type II error Ch 10b; midterm correctionsCh 9bcountif, sumif, averageifgenerating artificial data 
193/17Tue112-sample t-test for means (indep); 2-sample t-test for means (paired); skipping 2-proportions Ch 11Ch 10agenerating random numberst.test (low-priority)don't choose tail after; don't test max or min to see if "significant" or outlier.
203/19Thu12Categorical Association part ahandoutCh 12a; ProposalCh 10bPivot Tablescontingency table from data; mosaic plotsurvey like in univ101
213/24Tue12Categorical Association part bhandoutCh 12bCh 11; midterm corrections chisq.test 
223/26Thu12Categorical Association part chandoutCh 12cCh 12a; ProposalPasting into Word/ ppt: live or dead copies?Rmarkdown?Entering freshmen by gender (? race?) vs graduating seniors, perhaps just CS or STEM or MathEd
233/31Tue13Linear Regression and Correlation: Inferential Methodsm360-ch13-notes.docxCh 13Ch 12bExcel Regression ToolCI on slopePA school district data
244/2Thu Multiple Testing; Regression to the Mean; Covariance; PDF/CDF methodsm360-ch99-calculus-supplement Ch 12cLiveRegression Detroit 911 or Bank call center; WiFi; earthquake; shootings; emich webserver
254/7Tue PDF/CDF-based methods; Poisson Processes ch99calc or alternateCh 13What-If Goal Seekdexp, pexp, dpareto, ppareto, etc 
264/9Thu PDF/CDF, Poisson; presentation tipsexample Presentationsch999datafestProject ReportSQL  
274/14Tuepresent.Presentations  Project Presentation and ch99calc or alternate   
284/16Thupresent.Presentations      
 4/21Tue 1:30-3:00 final exam, HALF-HOUR EARLY!  ch999datafest  

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.

I expect that you will work on Stat 360 for 6 to 10 hours per week outside of class.

Instructor information

Professor Andrew Ross
Pray-Harrold 515m
andrew.ross@emich.edu
http://people.emich.edu/aross15/
(734) 487-1658, but I strongly prefer e-mail instead of phone contact.
Math department main office: Pray-Harrold 515, (734) 487-1444

Office Hours and other help

Here is my complete schedule.
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.

Another resource on campus is the Holman Success Center, formerly the Holman Learning Center.

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

You would probably enjoy these books:

Course Content

Course Goals

The objective of this course is to give students an elementary overview of sampling and data analysis using graphical methods, basic probability theory, discrete and continuous random variables, sampling distribution, point and interval estimation, and hypothesis testing. Exposure to computer software, for example, SAS, R or Excel is recommended for statistical analysis purposes.

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.

I tend to print most handouts and exams in a small font to save paper. If you would like a larger font, or a dyslexia-friendly font, please let me know and I will be happy to work with you. I am also happy to work with other learning-difference needs.

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:

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.

Your final score will be computed as follows: Final letter grades will be computed using:
  
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
though if absolutely necessary, a curve might be applied.

General Caveat

The instructor reserves the right to make changes to this syllabus throughout the semester. Notification will be given in class or by e-mail or both. If you miss class, it is your responsibility to find out about syllabus and schedule changes, especially the due dates and times of projects, assignments, or presentations.

Advice from My Other Students

In past years, I've asked my upper-level students to give advice to you, future students, based on their experiences in my courses. Here are some of the highlights:

Land Acknowledgement

The campus of Eastern Michigan University is located on the traditional territory (ceded in the 1807 Treaty of Detroit) of the Anishinaabeg, which refers collectively to the Ojibwe, Odawa, and Potawatomi (also known as the People of the Three Fires), and was also home to the Wendat/Wyandot people. This acknowledgement is included here to honor the elders and stewards of these heritages.

University Writing Center

The University Writing Center (115 Halle Library; 487-0694) offers one-to-one writing consulting for both undergraduate and graduate students. The UWC also has several college and program satellite locations across campus. The locations and hours for the other satellites can be found on the UWC web site: http://www.emich.edu/ccw/writing-center/contact.php Students seeking writing support at any UWC location should bring a draft of their writing (along with any relevant instructions or rubrics) to work on during the consultation.

Standard University Policies

In addition to the articulated course specific policies and expectation, students are responsible for understanding all applicable university guidelines, policies, and procedures. The EMU Student Handbook is the primary resource provided to students to ensure that they have access to all university policies, support resources, and student's rights and responsibilities. Changes may be made to the EMU Student Handbook whenever necessary, and shall be effective immediately, and/or as of the date on which a policy is formally adopted, and/or the date specified in the amendment. Electing not to access the link provided below does not absolve a student of responsibility. For questions about any university policy, procedure, practice, or resource, please contact the Office of the Ombuds: 248 Student Center, 734.487.0074, emu_ombuds@emich.edu, or visit the website at www.emich.edu/ombuds . CLICK HERE to access the University Course Policies

Food Pantry

Swoop's Pantry (104 Pierce Hall, emich.edu/swoopspantry, 734 487 4173) offers food assistance to all EMU students who could benefit. Students are able to visit twice per month to receive perishable and non-perishable food items, personal hygiene items, baby items, and more. Students can visit our website for hours of operation and more information. If you are in a position to donate to Swoop's, I encourage you to do so!