This version posted on: 2016-08-30

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

Follow-up courses:

MATH 419W - Introduction to Stochastic Mathematical Modeling (Gen Ed Area I, W) ECON 415 - Introduction to Econometrics 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.

MW 12:30-1:45 Stat 360-0, PH 305, CRN 16762 TR 12:30-1:45 Stat 360-1, PH 305, CRN 16764

A | B | C | D | E | F | G | H | I | |
---|---|---|---|---|---|---|---|---|---|

1 | Stat 360-0 | Prof. Andrew Ross; MW 12:30-1:45 ; Pray-Harrold 305 | CRN 16762 | ||||||

2 | Class# | Date 2016 | day | unit | Topic | Required Additional Reading | HW Assigned | HW Due | Bonus Tech Material after class |

3 | 1 | 9/7 | Wed | 1 | Intro; randomization example; car-insurance advertising; population vs sample, types of data | m360-ch01-data-types.docx | Ch 1 preview | * = deviation from usual 7-day delay | text-to-columns |

4 | 2 | 9/12 | Mon | 1;2 | Discrete vs Continuous; PivotTables, Bar charts, Dotplots; Ch 2 Bias | Ch 1 | Pivot Tables | ||

5 | 3 | 9/14 | Wed | 2 | Random vs Stratified Samples, etc; Random Rectangles activity | m360-ch02.2-2.3-powerpoint.pptx | Ch 2a; 2b | Ch 1* | left/mid/right and =DATE |

6 | 4 | 9/19 | Mon | 3 | Graphical Methods for Describing Data | Ch 3 | Ch 2a* | Kernel Density Estimates (KDEs) | |

7 | 5 | 9/21 | Wed | 4 | Center, Variability, Boxplots, Empirical Rule, z-scores, Percentiles & Plots | m360-ch04-notes.docx | Ch 4a and 4b | Ch 2b | Marked Scatterplots |

8 | 6 | 9/26 | Mon | 5 | Correlation; Regression | Ch 5a | Ch 3 | plot the percentile curve; dotplot-histogram-crf-etc | |

9 | 7 | 9/28 | Wed | 5 | Assessing fit; Nonlinear Relationships and Transformations | 5b preview | Ch 4a and 4b | vlookup | |

10 | 8 | 10/3 | Mon | 5 | 5 wrapup | Ch 5b | Ch 5a | Solver for nonlinear regression | |

11 | 9 | 10/5 | Wed | 6 | Definition 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 simulation | |

12 | 10 | 10/10 | Mon | 7 | Random Variables; Discrete and Continuous Distributions; Mean and StdDev; linear functions and sums | m360-ch07a-notes.docx | Ch 7a | Ch 5b | sumproduct |

13 | 11 | 10/12 | Wed | 7 | Binomial, Geometric; Normal; Checking and Transformations for Normality; Binom~Normal; QQ | m360-ch07b-notes.docx | Ch 7b | Ch 6 | dotplot-histogram-crf-qq |

14 | 12 | 10/17 | Mon | 8 | Statistics and Sampling Variability; Sampling Distribution of a Mean | 8 preview | Ch 7a | What-If Data Tables, 1-dim | |

15 | 13 | 10/19 | Wed | 8 | Central Limit Theorem; Sampling Distribution of a Proportion | Ch 8 | Ch 7b | What-If Data Tables, 2-dim | |

16 | 14 | 10/24 | Mon | 9 | Point Estimation; Confidence Interval for a Proportion | Ch 9a | conditional formatting | ||

17 | 15 | 10/26 | Wed | 9 | Confidence Interval for a Mean (incl. t-distrib) | Ch 9b | Ch 8 | sparklines | |

18 | 16 | 10/31 | Mon | midterm | midterm | Ch 9a | parallel axis plots | ||

19 | 17 | 11/2 | Wed | 10 | Hypotheses and Test Procedures; Errors in Hypothesis Testing; Proportion | m360-ch10a-powerpoint.pptx | Ch 10a | Ch 9b | countif, sumif, averageif |

20 | 18 | 11/7 | Mon | 10 | Hypothesis Tests for Population Mean; Power and Probability of Type II error | Ch 10b; midterm corrections | |||

21 | 19 | 11/9 | Wed | 11 | 2-sample t-test for means (indep); 2-sample t-test for means (paired); skipping 2-proportions | Ch 11 | Ch 10a | ||

22 | 20 | 11/14 | Mon | 12 | Categorical Association part a | handout | Ch 12a; Proposal | Ch 10b | Pivot Tables |

23 | 21 | 11/16 | Wed | 12 | Categorical Association part b | handout | Ch 12b | Ch 11; midterm corrections | |

24 | 22 | 11/21 | Mon | 12 | Categorical Association part c | handout | Ch 12c | Ch 12a; Proposal | Pasting into Word/ ppt: live or dead copies? |

25 | 23 | 11/28 | Mon | 13 | Linear Regression and Correlation: Inferential Methods | m360-ch13-notes.docx | Ch 13 | Ch 12b | Excel Regression Tool |

26 | 24 | 11/30 | Wed | calc | Multiple Testing; Regression to the Mean; Covariance; calculus-based methods | m360-ch99-calculus-supplement-v2.docx | Ch 12c | LiveRegression | |

27 | 25 | 12/5 | Mon | calc | Calculus-based methods; Poisson Processes | ch99calc | Ch 13 | What-If Goal Seek | |

28 | 26 | 12/7 | Wed | Review Day | ch999datafest | Final Report | |||

29 | 27 | 12/12 | Mon | present. | Presentations | Presentation | |||

30 | 28 | 12/14 | Wed | Final | 360-0 final: Wed Dec 14, 11:30 (AN HOUR EARLY) | ch99calc and ch999datafest | |||

31 | 29 | 12/19 | Mon | no class--other classes having finals |

A | B | C | D | E | F | G | H | I | |
---|---|---|---|---|---|---|---|---|---|

1 | Stat 360-1 | Prof. Andrew Ross; TR 12:30-1:45 PH 305 | CRN 16764 | ||||||

2 | Class# | Date 2016 | day | unit | Topic | Required Additional Reading | HW Assigned | HW Due | Bonus Tech Material after class |

3 | 1 | 9/8 | Thu | 1 | Intro; randomization example; car-insurance advertising; population vs sample, types of data | m360-ch01-data-types.docx | Ch 1 preview | * = deviation from usual 7-day delay | text-to-columns |

4 | 2 | 9/13 | Tue | 1;2 | Discrete vs Continuous; PivotTables, Bar charts, Dotplots; Ch 2 Bias | Ch 1 | Pivot Tables | ||

5 | 3 | 9/15 | Thu | 2 | Random vs Stratified Samples, etc; Random Rectangles activity | m360-ch02.2-2.3-powerpoint.pptx | Ch 2a; 2b | Ch 1* | left/mid/right and =DATE |

6 | 4 | 9/20 | Tue | 3 | Graphical Methods for Describing Data | Ch 3 | Ch 2a* | Kernel Density Estimates (KDEs) | |

7 | 5 | 9/22 | Thu | 4 | Center, Variability, Boxplots, Empirical Rule, z-scores, Percentiles & Plots | m360-ch04-notes.docx | Ch 4a and 4b | Ch 2b | Marked Scatterplots |

8 | 6 | 9/27 | Tue | 5 | Correlation; Regression | Ch 5a | Ch 3 | plot the percentile curve; dotplot-histogram-crf-etc | |

9 | 7 | 9/29 | Thu | 5 | Assessing fit; Nonlinear Relationships and Transformations | 5b preview | Ch 4a and 4b | vlookup | |

10 | 8 | 10/4 | Tue | 5 | 5 wrapup | Ch 5b | Ch 5a | Solver for nonlinear regression | |

11 | 9 | 10/6 | Thu | 6 | Definition 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 simulation | |

12 | 10 | 10/11 | Tue | 7 | Random Variables; Discrete and Continuous Distributions; Mean and StdDev; linear functions and sums | m360-ch07a-notes.docx | Ch 7a | Ch 5b | sumproduct |

13 | 11 | 10/13 | Thu | 7 | Binomial, Geometric; Normal; Checking and Transformations for Normality; Binom~Normal; QQ | m360-ch07b-notes.docx | Ch 7b | Ch 6 | dotplot-histogram-crf-qq |

14 | 12 | 10/18 | Tue | 8 | Statistics and Sampling Variability; Sampling Distribution of a Mean | 8 preview | Ch 7a | What-If Data Tables, 1-dim | |

15 | 13 | 10/20 | Thu | 8 | Central Limit Theorem; Sampling Distribution of a Proportion | Ch 8 | Ch 7b | What-If Data Tables, 2-dim | |

16 | 14 | 10/25 | Tue | 9 | Point Estimation; Confidence Interval for a Proportion | Ch 9a | conditional formatting | ||

17 | 15 | 10/27 | Thu | 9 | Confidence Interval for a Mean (incl. t-distrib) | Ch 9b | Ch 8 | sparklines | |

18 | 16 | 11/1 | Tue | midterm | midterm | Ch 9a | parallel axis plots | ||

19 | 17 | 11/3 | Thu | 10 | Hypotheses and Test Procedures; Errors in Hypothesis Testing; Proportion | m360-ch10a-powerpoint.pptx | Ch 10a | Ch 9b | countif, sumif, averageif |

20 | 18 | 11/8 | Tue | 10 | Hypothesis Tests for Population Mean; Power and Probability of Type II error | Ch 10b; midterm corrections | |||

21 | 19 | 11/10 | Thu | 11 | 2-sample t-test for means (indep); 2-sample t-test for means (paired); skipping 2-proportions | example Proposals and Reports | Ch 11 | Ch 10a | |

22 | 20 | 11/15 | Tue | 12 | Categorical Association part a | handout | Ch 12a; Proposal | Ch 10b | Pivot Tables |

23 | 21 | 11/17 | Thu | 12 | Categorical Association part b | handout | Ch 12b | Ch 11; midterm corrections | |

24 | 22 | 11/22 | Tue | 12 | Categorical Association part c | handout | Ch 12c | Ch 12a; Proposal | Pasting into Word/ ppt: live or dead copies? |

25 | 23 | 11/29 | Tue | 13 | Linear Regression and Correlation: Inferential Methods | m360-ch13-notes.docx | Ch 13 | Ch 12b | Excel Regression Tool |

26 | 24 | 12/1 | Thu | calc | Multiple Testing; Regression to the Mean; Covariance; calculus-based methods | m360-ch99-calculus-supplement-v2.docx | Ch 12c | LiveRegression | |

27 | 25 | 12/6 | Tue | calc | Calculus-based methods; Poisson Processes | ch99calc | Ch 13 | What-If Goal Seek | |

28 | 26 | 12/8 | Thu | Review Day; presentation tips | example Presentations | ch999datafest | Final Report | ||

29 | 27 | 12/13 | Tue | present. | Presentations (T/R class has presentations first) | Presentation | |||

30 | 28 | 12/15 | Thu | no class--other classes having final exams | ch99calc | ||||

31 | 29 | 12/20 | Tue | Final | 360-1 final: Tue Dec 20, 11:30 (AN 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.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

Mon/Wed 10:00-11:00 Office Hours 11:00-12:15 Math 319, PH 521 12:30-1:45 Stat 360-0, PH 305 1:45-2:45 Office Hours 3:30- 4:30 (Wed) faculty research meeting 4:30- 5:00 (Wed) student research meeting Tue/Thu 11:30-12:30 Office Hours 12:30-1:45 Stat 360-1, PH 305 1:45-2:45 Office Hours 4:30-5:30 Office Hours 5:30-6:45 Math 560, PH 503 Fri: no schedule--I'm often on campus, though. I have various meetings to go to. Send e-mail to make an appointment.

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 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.

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 Fall 2016 and Winter 2017 we will still use the 4th edition; do not buy the 5th edition even if you see it.

This textbook is not calculus-based, but our course is a calculus-based course. So, we will use a calculus-based supplement to the textbook that I have written.

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.

I will post data files, homework assignment files, etc. on my home page and sometimes 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.

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

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 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 will be assigned about twice per week, usually 2 assignments per chapter. All homework should be typed and 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.

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

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 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.

There is no systematic grade-dropping system 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 and consult with me during office hours to ensure you know the material.

Your final score will be computed as follows:- 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 <48 F 48 to <52 D- 52 to <56 D 56 to <60 D+ 60 to <64 C- 64 to <68 C 68 to <72 C+ 72 to <76 B- 76 to <80 B 80 to <84 B+ 84 to <88 A- 88 to <100 Athough if absolutely necessary, a curve might be applied.

- * 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.

The University Writing Center (115 Halle Library; 487-0694) offers one-to-one writing consulting for both undergraduate and graduate students. Students can make appointments or drop in between the hours of 10 a.m. and 6 p.m. Mondays through Thursdays and from 11 a.m. to 4 p.m. on Fridays. Students should bring a draft of what they're working on and their assignment sheet. The UWC opens for the Winter 2013 semester on Monday, January 14 and will close on Friday, April 19.

The UWC also offers small group workshops on various topics related to writing (e.g., Organizing Your Writing; Incorporating Evidence; Revising Your Writing; Conquering Commas; Finding and Fixing Errors). Workshops are offered at different times in the UWC. Visit the UWC page ( http://www.emich.edu/english/writing-center ) to see our workshop calendar. To register for a workshop, click the link from the UWC page for the type of workshop you wish to attend.

The UWC also has several satellite sites across campus. These satellites provide writing support to students within the various colleges. For more information about our satellite locations and hours, visit the UWC web site: http://www.emich.edu/english/writing-center .

The Academic Projects Center (116 Halle Library) also offers one-to-one writing consulting for students, in addition to consulting on research and technology-related issues. The APC is open 11 a.m. to 5 p.m. Mondays through Thursdays for drop-in consultations . Additional information about the APC can be found at http://www.emich.edu/apc . Students visiting the Academic Projects Center or any of the satellites of the University Writing Center should also bring with them a draft of what they're working on and their assignment sheet.