Faculty: You can include your syllabus here by starting with this template and updating the highlighted items, or making other changes as desired (you can also cut and paste from an existing syllabus). Please delete this informational block when you are ready to share your site with your students. For help working with OpenLab Course sites, visit OpenLab Help.

Course Information

Course Number: MAT 1372

Course Title: Statistics with Probability

Course Outline: Official course outline prepared by the Mathematics Department.

Course Description: Topics include sample spaces and probabilities, discrete probability distributions (Binomial, Hypergeometric), expectation and variance, continuous probability distributions (Normal, Student, Chi-Square), confidence intervals, hypothesis testing, and correlation and regression. Spreadsheets are used throughout the semester.

Credits / Hours: 3 (2 class hours, 2 lab hours)

Section Number: ABCD

Pre- or Corequisite: MAT 1375

Textbooks: There are two textbooks for this course:

  1. Introductory Statistics, Sheldon Ross, 3rd edition, Academic Press
  2. Statistics with Microsoft Excel, Beverly J. Dretzke, 5th edition, Pearson

Online Spaces

  • OpenLab: This website will be the online home for our class. The site contains important information about the course, and will be used in various ways throughout the semester.  Add link to OpenLab Course
  • WeBWorK:  Much of the homework for this class will be completed on the WeBWorK system.  You will be provided with more information in the first week of class.  To go to our class WeBWorK site click here. (update this link with your WeBWorK section info)
  • Add information about any video conferencing tools you will be using.

In-person Location:

  • N81x (update room or delete if not applicable)

Faculty Information

Professor Name:

  • Your Name Here

Office Hours/Information: For information about office hours, visit Contact Info & Communications.

Contact Information

  • Email: youremail@citytech.cuny.edu
  • Phone: 123-456-7890

Learning Outcomes

  1. Collect, organize and graph raw data.
  2. Compute statistical parameters (mean, median, mode, average deviation, variance, and sample standard deviation).
  3. Create grouped frequency distributions, probability distributions, histograms as well as identify bellshaped distributions (normal, t-distribution) and nonbellshaped distributions (Chi-square).
  4. Assign probabilities to events using counting methods, conditional probability and discrete probability distributions.
  5. Determine if the data supports a hypothesis at a given significance level using known distributions.
  6. Use spreadsheet software and other computer technology to assist in creating distributions and testing hypothesis

Gen Ed Learning Outcomes

Students will be able to:

  1. Understand and employ both quantitative and qualitative analysis to solve problems.
  2. Make meaningful and multiple connections between mathematics and other areas of study leading to a major or profession.
  3. Employ scientific reasoning and logical thinking.
  4. Communicate effectively using written and oral means.

Teaching/Learning Methods

Technology Requirements


A detailed schedule of topics can be found on the Schedule page.

Percent/Letter Grade conversion

A = 93.0 — 100
A- = 90.0 — 92.9
B+ = 87.0 — 89.9
B = 83.0 — 86.9
B- = 80.0 — 82.9
C+ = 77.0 — 79.9
C = 70.0 — 76.9
D = 60.0 — 69.9
F = 0 — 59.9
W = withdrawal up to 11/6/20 (WF after 11/6/20)

Grading Policy

The grading policy for the course appears on the Grading Policy page.

Class Etiquette & Netiquette

Add expectations for class etiquette and netiquette.


Add attendance/participation policy. With online instruction the focus is on class participation, which depends on the structure of your course. For example, if you are offering synchronous classroom experiences (Zoom, Blackboard Collaborate, etc.) it would be participation in these meetings. For asynchronous courses, it is participation by the deadlines stated in your syllabus. As technology can be fickle, and life is far from normal, please exercise both compassion and common sense.

Academic Integrity Policy

Students and all others who work with information, ideas, texts, images, music, inventions and other intellectual property owe their audience and sources accuracy and honesty in using, crediting and citation of sources. As a community of intellectual and professional workers, the college recognizes its responsibility for providing instruction in information literacy and academic integrity, offering models of good practice, and responding vigilantly and appropriately to infractions of academic integrity. Accordingly, academic dishonesty is prohibited in The City University of New York and is punishable by penalties, including failing grades, suspension and expulsion. More information about the College’s policy on Academic Integrity may be found in the College Catalog

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