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
MATH 419W - Introduction to Stochastic Mathematical Modeling (Gen Ed Area I, W) ECON 415 - Introduction to Econometrics MATH 460/576 Applied Survey Sampling MATH 461/575 Linear Regression Analysis MATH 462/572 Design and Analysis of Experiments MATH 468 - Introduction to Biostatistics MATH 469 - Introduction to Categorical Data Analysis MATH 474W/574 - Applied Statistics (Gen Ed Area I, W) MATH 571 Mathematical Statistics I: Probability Theory MATH 573 Statistical Data Analysis MATH 577 Applied Multivariate Statistics MATH 578 Nonparametric Statistics.
Class meetings will be mostly interactive lectures, with some time to work on problems in class, and some time to go over problems from the homework.I expect that you will work on Math 360 for 6 to 10 hours per week outside of class.
Mon/Wed: 9:30-10:45 Math 360, Pray-Harrold 520 10:45-11:30 office hours 1:00- 2:00 office hours 2:00- 3:15 Math 110 Pray-Harrold 418 3:15- 4:00 office hours Tue/Thu: 1:00- 2:00 office hours 2:00- 3:15 Math 110 Pray-Harrold 323 3:15- 4:00 office hours Fri: No official office hours, but I'm often on campus. E-mail me to make an appointment, or drop by.
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:
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.
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 419 when I teach them. I also enjoy teaching about cost-minimizing/profit-maximizing methods called Non-Linear Programming (NLP) in Math 560, Optimization Theory.
Textbook: Probability & Statistics for Engineers & Scientists by Walpole, Myers, Myers, and Ye, 9th edition
A lot of our work will be done on computers. If you had been waiting for a good reason to buy a laptop, this is it.
I will post data files, homework assignment files, etc. on my home page.
We will use an on-line gradebook via EMU-Online 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.
Unit Two: Probability Topics include sample space, events, and probability rules.
Unit Three: Random Variables and Probability Distributions Topics cover discrete, continuous and joint probability distributions (discrete case only).
Unit Four: Mathematical Expectation Key concepts in this unit include mean, variance and covariance of random variables, mean and variance of linear combinations of random variables.
Unit Five: Discrete and Continuous Probability Distributions Topics include probability distributions of various discrete random variables (uniform, binomial, multinomial, hypergeometric, negative Binomial, geometric, and Poisson) and various continuous random variables (Normal distribution and its application, exponential, gamma and chi-squared distributions).
Unit Six: Sampling Distributions Topics cover sample mean and sample standard deviation, sampling distribution of sample mean.
Unit Seven: One- and Two-Sample Estimation Problems This unit covers point and interval estimations of mean, proportion and variance based on a single sample, point and interval estimation of differences between two means, proportions and ratio of two variances based on two samples.
Unit Eight: One- and Two-Sample Tests of Hypotheses Topics include one-and two-sample tests of hypotheses concerning means, proportions and variances.
Some variations in this outline are to be expected.
An example outline from Walpole, Myers, Myers, and Ye would be
CH 2 Sample space Events Counting sample points Probability of an event Additive Rules Conditional probability Multiplicative rules Bayes' rule (sensitivity, specificity, positive predictive value, etc) CH 3 Concept of a random variable Discrete probability distributions Continuous probability distributions CH 4 Mean of random variables Variance of random variables CH 5 Discrete uniform distribution Binomial distribution Poisson distribution CH 6 Continuous uniform distribution Normal distribution Exponential distribution CH 8 Random sampling Sampling distributions of means The Central Limit Theorem (statement) Sampling distributions of variances Student's t distribution Ch 9 Statistical inference Estimating the mean Standard error Estimating the difference of means Paired observations Estimating single sample proportion Estimating difference of proportions Estimating the variance Ch 10 Statistical hypothesis Type 1 and type 2 errors Tests for the mean (standard deviation known and unknown) Goodness of Fit test Categorical Tests of Independence/Homogeneity Ch 11 Introduction to linear regression The simple linear regression model Least squares and the fitted model Choice of a regression model Correlation
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.
My lectures and discussions mostly use the chalkboard, 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 will be assigned about once or twice per week. It will sometimes be a small problem set designed to help you understand the behavior of math models. Other times, it will involve writing up a little paper on an assigned topic.
Homework 01: Chapter 1.3 Measures of Center, plus the GEICO simulation, Bloom's Taxonomy, and 8 Standards for Mathematical Practice Homework 02: Chapter 1.4 (#1.7 through #1.12) Homework 03: Types of Data; Stem-and-Leaf Homework 04: Effects of Shifting and Scaling (on-line multiple-choice quiz) Homework 05: Histograms (based on a scan from another textbook, Watkins/Schaeffer/Cobb) Homework 06: Chapter 2.1 & 2.2, 2.3: Sets, Combinatorics Homework 07: Combinatorics, divided for teachers/nonteachers. Homework 08: Chapter 2.5 - 2.7: Conditional probability, Bayes' Rule Homework 09: Chapter 3.1-3.4: PDFs, CDFs, calculus-based problems Homework 10: Chapter 4: expected value, calculus-based problems Homework 11: Chapter 5: binomial, geometric distribution Homework 12: Chapter 6: normal approximation to binomial (skip continuity correction) Homework 13: Chapter 8: sampling distributions Homework 14: Chapter 9: part 1 Homework 15: Chapter 9: part 2, confidence intervals Homework 16: Chapter 10: hypothesis tests Homework 17: Chapter 10.11: Goodness-of-Fit test Homework 18: emu-online version of photocopied hypothesis-test concept test Homework 19: Chapter 10.12 Workshop Statistics stapled photocopies from class Homework 20: Chapter 10.12/13 homework for all Homework 21: Chapter 10.12/13 for teachers Homework 22: Chapter 10.12/13 for nonteachers Homework 23: Chapter 11 part 1 for all Homework 24: Chapter 11 part 1 for teachers Homework 25: Chapter 11 part 1 for nonteachers Homework 26: Chapter 11 part 2 for all Homework 27: Chapter 11 part 2 for teachers Homework 28: Chapter 11 part 2 for nonteachers
There will be 3 exams plus a final exam.
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. The grade breakdown is:
On average, students should spend a total of about 30 minutes in office hours discussing the project. Plan for this in advance!
No scores will be dropped, unless a valid medical excuse with evidence is given. In the unfortunate event of a medical 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 and consult with me during office hours to ensure you know the material.Your final score will be computed as follows:
Current University policy recognizes the rights of students to observe religious holidays without penalty to the student. Students will provide advance notice to the instructor in order to make up work, including examinations, they miss as a result of their absence from class due to observance of religious holidays. If satisfactory arrangements cannot be made with the instructor, the student may appeal to the school director or head(s) of department(s) in which the course(s) is / are offered.
Academic dishonesty, including all forms of cheating, falsification, and/or plagiarism, will not be tolerated in this course. Penalties for an act of academic dishonesty may range from receiving a failing grade for a particular assignment to receiving a failing grade for the entire course. In addition, you may be referred to the Office of Student Conduct and Community Standards for discipline that can result in either a suspension or permanent dismissal. The Student Conduct Code contains detailed definitions of what constitutes academic dishonesty but if you are not sure about whether something you are doing would be considered academic dishonesty, consult with the course instructor. You may access the Code online at: www.emich.edu/studentconduct/
Students are expected to abide by the Student Conduct Code and assist in creating an environment that is conducive to learning and protects the rights of all members of the University Community. Incivility and disruptive behavior will not be tolerated and may result in a request to leave class and referral to the Office of Student Conduct and Community Standards (SJS) for discipline. Examples of inappropriate classroom conduct include repeatedly arriving late to class, using a mobile/cellular phone while in the class session, or talking while others are speaking. You may access the Code online at www.emich.edu/studentconduct/
When we aren't in a computer lab, if ever, those who use laptops during class should sit in the back row if possible, to avoid distracting others with what is on their screens.
If you wish to be accommodated for your disability, EMU Board of Regents Policy 8.3 requires that you first register with the Disability Resource Center (DRC) in 240K EMU Student Center. You may contact DRC by telephone (734.487.2470). Students with disabilities are encouraged to register with the DRC promptly as you will only be accommodated from the date you register with them forward. No retroactive accommodations are possible.