Syllabus          Math 118: Introduction to Statistics       Fall 2018

Margaret Menzin             Office:S209                                                                          Phone: X2704
                                         Email: menzin@simmons.edu                     Home Phone: 781-862-5107

                                         Office Hours:  Mon. 7:15-8:00; and 2:00-6:00
                                                                  Wed. 7:15- 8:00; by appointment 5:00-7:00 p.m.
                                                                  Fri 7:15- 8:;00; and 9:00-1:00 and often after 2:00

                                                    The Mathematics, Statistics and Computer Science Department eats at the Fens on Wednesdays
                                                    at 11:30 -1:00. We hope you will join us.

                                         Note: There will be no class on Monday Sept. 10 or Wednesday Sept. 19. In order to make up for this time,
                                         we will start our class at 7:30 on the other days in those two weeks.


Organization of the course

Text: DeVeaux, Velleman and Bock: Intro Stats 5th (referred to as DVB)

Attendance:This course has three meetings in an integrated classroom and lab each week. Attendance is mandatory. More than two unexcused absences or late arrivals will be penalized.
Attendance needs to be mental as well as physical. You may not use cellphones while in class or use the computers for anything other than classwork.

Statistical Calculations: I am very excited that we will be using R for our statistical calculations.
R is a free (open source) language and we will be using R—Studio with the Mosaic package. This may be used on any computer (PC, Mac, Linux) and is also installed on the Simmons R Server, so you will be able to use it on the Simmons machines and also on your own computers. We hope and expect that this will let you make good use of your knowledge of statistics long after you complete this course.

Grading: There will be in–class exams at the end of chapters 7, 12, and 16. There will be either a final exam or a final project; there may be pop quizzes. Each of these 4 exams or projects counts as 20% of your grade.
The last 20% is from graded homework. HW covers one week of material (MWF). You may ask questions about it the following Monday, it is then collected on Wednesday in class, and returned on Friday. NO LATE HW WILL BE ACCEPTED. In any given HW assignment some problems will be graded and some not. There is no partial credit on graded problems.

It is critical that you do homework as assigned. Problems always look do-able, but when you start to do them, and compare your work with the answers in the back you may uncover gaps in your understanding. You should spend about 3 hours on homework for every hour of class time. I usually ask for questions on homework and the reading at the start of each class, but if I do not ask, please bring up any problems you had trouble with.

Students with Disabilities: Reasonable accommodations will be provided for students with documented physical, sensory, systemic, cognitive, learning, and psychiatric disabilities. If you have a disability and anticipate that you will need a reasonable accommodation in this class, it is important that you contact the Academic Support Center Director at 617.521.2471 early in the semester. Students with disabilities receiving accommodations are also encouraged to contact their instructors within the first 2 sessions of the semester to discuss their individual needs for accommodations."

Title IX and the Simmons College Gender-Based Misconduct Policy: Title IX Federal law states that all students have the right to gain an education free of gender–based discrimination. Some examples of gender-based discrimination, as defined by this law include sexual harassment or exploitation, sexual assault, domestic/dating violence, and stalking. In compliance with Title IX, Simmons College has a "Gender-Based Misconduct Policy" which defines these forms of misconduct, outlines College protocol and procedures for investigating and addressing incidences of gender-based discrimination, highlights interim safety measures, and identifies on and off-campus resources. The policy and a list of resources is located here: http://www.simmons.edu/about-simmons/title-ix/policy

 

Syllabus

You are about to start the study of statistics: a wonderful, beautiful, and extraordinarily useful way to look at the world. Every day, in every discipline, we are presented with data. The data may be numerical ( SAT scores for all seniors at a particular high school, or survival rates for people diagnosed with a particular cancer) or qualitative (choice of major for graduating seniors at Simmons College class of 2020 or numbers of people renting cars at Logan by size of car (subcompact, compact, etc.). It may be one set of data, as in the above examples, or it may be comparative (SAT scores this year vs. 10 years ago, survival rates for cancer with different methods of treatment). The first thing we will look at is how to describe the data, and the relationships among data. We will use charts, tables and pictures, as well as numbers (numbers to describe the strength of a relationship, numbers to describe the central tendency of your data, and numbers to describe how variable your data is). This part of statistics is called descriptive statistics. The material is found in Chapters 1-7 of DVB. There is a test at the end of Chapter 7.

After discussing how to describe data, we will turn to the production of data and design of experiments and sampling distributions. You will learn how to use the natural variability of data to design your experiment so that your results are meaningful. For example, how large a sample do you need for your purposes? What are the theoretical underpinnings for our methods? The material for this section is found in Chapter 8, 10- 12 of DVB and in handouts. There is a test at the end of this material.

The last and major part of the course is inferential statistics. Here we learn how to make inferences about an entire population (e. g. all adult women in the U.S.) based on the information we learn from a random sample of some women (of, say, 100 women). For example, if 37 of our 100 women are left handed what is a reasonable estimate for the proportion of left-handed women in the U.S. (e.g. we are 95% confident that between 34% and 40% are left-handed), and how do we determine how large an interval we need for our estimate. If a random sample of 100 men has 40 lefties, what can we say about the proportion of lefties among men and women? The material is found in Chapters 13-20 of DVB. There is a test about half way thru this material (at the end of Chapter 16).

We will discuss the material in Chapters 17 and 18 and as time allows we explore other topics in the other chapters of DVB. There will be either a cumulative final or a cumulative project.

A more detailed schedule of topics will be found in Mooodle.

I expect you to check your (Simmons) email regularly, as I often send interesting articles and useful comments (e.g. about HW).

While exams in this course are not explicitly cumulative, the material in the course builds on itself.
If you do not understand a topic it behooves you to ask about it so that you can use the material in later parts of the course.

Learning goals for the course

  1. Students will be able to identify the various types of variables, and be able to describe data using appropriate statistical language, quantitative measures, tables and graphs. They should also be able to perform basic manipulations on univariate and bivariate variables in Minitab so as to display and further interpret appropriate characteristics of variables (center, spread, skewness, etc.) and relationships among variables.

  2. Students will be able to find percentiles from the area under a Normal distribution and vice versa, and they will be able to explain, in context, what they have found.

  3. Students will be able to use the Mosaic package of R–Studio to make and examine histograms, box and whisker diagrams and scatterplots; from scatterplots they will know when it is appropriate to use a linear regression model, and will be able to find the regression equation and line, use them for predicitions, and know how to examine and interpret residuals.

  4. Students will understand the importance of randomness in design of experiments, and how a random sample allows us to generalize from a sample to a population. They will understand the difference between association and causation, and they will be able to identify potential confounding variables. Students will be able to distinguish between observations and experiments and be able to design simple experiments with an appropriately sized random sample.

  5. Students will be able to explain the difference between confidence intervals and hypothesis testing and to explain the logic behind each. Students will be able to identify what kind of hypothesis test is appropriate (one-sided vs two-sided, two populations vs. matched pairs, etc.), know when their data is inadequate to merit using basic tests. They will able to perform the tests (when appropriate), compute and interpret p-values and describe their conclusions using proper statistical language. They will understand the difference between Type I and Type II errors and be able to re-phrase them in the context of a specific example.