Course Information

Prof. J Reitz
NYC College of Technology, Spring 2013

This course provides an introduction to statistical methods and statistical inference. Topics include descriptive statistics, random variables, distributions, sampling, estimation and inference, t-tests, chi-square tests and correlation.

Course and Section:  Math 1272 Statistics, Section 5195
Class Meets:
T/Th 2:30 – 3:45, Namm 403
 Larson & Farber, Elementary Statistics, 5th Ed, Prentice Hall.
A scientific calculator is required. We recommend the TI-30X IIS or similar.
Namm N707
Office Hours:  
Tuesday 3:45 – 4:45, Thursday 11:30 – 12:30
The class website will be on the OpenLab ( The site contains important information about the course, and will be used in various ways throughout the semester.  The address for the class website is:
Homework for this class will be completed on the WeBWorK website.  You will be provided with more information in the first week of class.  The address is:

Grading (percent / letter grade correspondence):

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 4/18/12
WF = withdrawal after 4/18/12 (WF = F)
NOTE: Withdraw before 4/18/12 to avoid an F or WF

Grading (how your grade is calculated):
Homework (20%):
You must complete assignments on WebWork to earn homework points. Homework will be assigned for each topic through the WebWork website and must be completed by the due date (usually about 1 week from the day the assignment is given). Over the course of the semester, you will only need to complete 80% of the assigned problems to earn the required points (the total required points will be provided near the end of the semester, when all assignments have been created). Any additional points earned will count as bonus credit (50% value of required points).
OpenLab participation (15%):
You will be participating in the OpenLab (website) by posting and making comments each week in response to assigned readings, homework problems, and so on. Your first assignment is to register for the OpenLab and join this class (go to the course website for instructions). Further assignments will be posted on the OpenLab.
In-Class Exams (40%):
There will be 3 exams during the semester (not including the final).  No makeup exams will be given.  If you miss an exam for a valid reason, your final exam score will take the place of the missing exam.
Final Exam (25%):
A final exam is given on the last day of class covering all topics. The final exam must be taken to pass the course.

Attendance:  Absence is permitted only with a valid reason. Anything in excess of 10% of the total number of class meetings is considered excessive absence (more than 3 absences).
Two latenesses count as one absence.
Records should be kept by every student of all grades received, exam papers, other work completed and any absences.

Learning Outcomes

  1. Students will be able to collect, organize and graph raw data.
  2. Students will be able to compute statistical parameters (mean, median, mode, average deviation, variance, and sample standard deviation).
  3. Students will be able to identify the binomial distribution and bell-shaped distributions (normal, t-distribution).
  4. Students will be able to do simple counting arguments and apply simple probabilities to events.
  5. Students will be able to determine if the data supports a hypothesis to a given level of significance.
  6. Students will be able to determine and apply correlation between two characteristics.

Gen Ed Learning Outcomes
Students will be able to:

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

Academic Integrity: The New York City College of Technology Policy on Academic Integrity: 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 citing 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 at New York City College of Technology and is punishable by penalties, including failing grades, suspension, and expulsion. The complete text of the College policy on Academic Integrity may be found on p. 64 of the catalog.


Leave a Reply

Your email address will not be published. Required fields are marked *