Syllabus

New York City College of Technology	Mathematics Department Office: N711
The City University of New York	(718) 260-5380 (718) 254-8537 fax

D057 Course Meetings: TTh 10:00 – 11:40AM	D058 Course Meetings: MW 2:00 – 3:40AM
Instructor: Ezra Halleck	Email: ehalleck@citytech.cuny.edu
Office Hours (in N726): T, W noon-1:00PM and by apt

1. OER: openstax (html), the orginal openintro textbook (resources incluing link to “purchase” pdf), passion-driven statistics (html) and course-kata (preview html).
2. Introductory Statistics 3e by Sheldon Ross (textbook from course outline)
3. Statistics with Microsoft Excel 5e by Beverly J. Dretzke (optional textbook from course outline)

Websites: https://openlab.citytech.cuny.edu/mat1372coursehub/

Computer software: R and Excel.

• Installation of Rstudio on your computer locally is a somewhat involved process. Regardless of whether you have a local install, get a free Rstudio Cloud account, to receive and work on labs.
• Install Excel: you may use Free Office 365 for CUNY students: Windows, Mac.
• datacamp’s free intro to R course is required. The R webpage on this site will make references to the various chapters in this course.
• codeacademy’s free intro to R course is much longer and is based on “tidyverse” which may be confusing to you. For the most part, we will be using “base R”.

Course Description: Topics include sample spaces and probabilities, discrete (Binomial, Poisson) and continuous (Normal, Student, Chi-Square) probability distributions, expectation and variance, confidence intervals, hypothesis testing, and correlation and regression.

Co/Prerequisite: MAT1375

Student Learning Outcomes: At the end of the semester, students will be able to:

1. collect, organize and graph raw data.
2. compute numerical statistics (mean, median, mode, average deviation, variance, and standard deviation).
3. create grouped frequency and probability distributions and histograms; identify:
   • bell-shaped distributions (Normal, t-distribution)
   • non-bell-shaped distributions (Uniform, Chi-square).
4. assign probabilities to events using counting methods, conditional probability, and discrete (binomial, hypergeometric and Poisson) and continuous (uniform and other straight line) density functions.
5. find the least squares regression line and visually estimate the correlation
6. using normal, t and Chi-square distributions, use software to display probabilities as areas and determine if the data supports a hypothesis to a given level of significance
7. create a contingency table and determine whether two categorical variables are independent

Attendance: You are expected to attend classes and to arrive on time.

Academic honesty: You are encouraged to work in groups on homework and laboratories, but be able to explain anything you turn in. During an exam, providing someone else your work is cheating; you will be treated in the same way as the person who copies.

Please follow basic guidelines for netiquette: in particular, remember the human.

Set enough time aside each week: You are expected to spend 4-6 hours outside the classroom each week reading the text, watching videos, working on projects, labs, doing homework and preparing for exams.

Time problems? Here is a damage control priority list:

1. Read the section and/or watch the assigned videos prior to the class in which it is covered. They will facilitate your understanding and participation in class and will frequently be part of the daily quiz.
2. Attempt at least some of the homework problems immediately after class, so that you know how much of the class you understood.
3. Take advantage of office hours: If you are unable to attend the scheduled hours, make an appointment.
4. The math dept. often arranges for tutoring. Stay tuned for more info.

Projects: reports (individual, 15%) and presentation (group, 5%)

• I will provide a list of suggested topics/sources, but each group of 2 students is encouraged to find their own topic. You will be assigned a group based on your major & your achievement up to the end of September.
• To ensure an interesting selection of oral presentations, I must approve your second topic.
• For the final project, you should include a scatterplot to compare 2 numerical components, e.g., weight vs. height or hours studied vs. exam score. A best fit line or other curve might be appropriate. How close the data is to the line or curve is correlation.
• No credit will be given for a report if shared. FOR YOUR OWN PROTECTION, do not show your report or draft to your partner. In contrast, code for descriptive statistics and graphs can and should be shared.
• When doing your presentation, make sure that every member of your group speaks for roughly the same amount of time. Minimize what appears on each slide and orally provide narrative and fill in missing info.

Webwork problem sets: Generally due one week after assigned.

Laboratories: These will be disseminated via Rcloud studio. Some work will be done in class, some at home.

The webwork hmwk & laboratories counts for 35% combined (the 15%/20% split shown below is preliminary).

Midterm Exam (20%) A sample exam will be posted one week prior.

Final Exam (25%): A sample exam will be posted one week prior. Comprehensive, but will emphasize statistical inference.

If you miss the final exam and have been failing the course, you will receive a WU. Otherwise, if you have a documented illness or emergency, you will have opportunity to take a makeup final exam (small fee).