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Web Schedule Fall 2016

Revision Date: 30-Aug-16

MAT-2021-VR02 - Statistics I

Synonym: 152523
Location: Rutland
Credits: 3 (45 hours)
Day/Times: Tuesday, 11:45A - 02:30P
Semester Dates: 09-06-2016 to 12-13-2016
Last day to drop without a grade: 09-26-2016
Last day to withdraw (W grade): 11-07-2016
Faculty: Patricia Gordon | View Faculty Credentials

This course has started, please contact the offering academic center about registration

Course Description:

An introduction to the basic ideas and techniques of probability and statistics. Topics may include numerical and graphical descriptive measures, probability, random variables, the normal distribution, sampling theory, estimation, hypothesis testing, correlation and regression. The use of technology may be required. Students must take a math assessment for placement purposes prior to registration.

Essential Objectives:

1. Outline the general development of statistical science, and list a number of common applications of statistical methodology.
2. Distinguish between descriptive and inferential statistics.
3. Create and apply various techniques used to describe data, such as pie charts, bar graphs, frequency tables, and histograms.
4. Define three common measures of central tendency (mean, median, and mode), and demonstrate the ability to calculate each manually from a series of small data sets.
5. Describe common methods of measuring variability, including range, percentiles, variance, and standard deviation, and calculate each from a series of small data sets.
6 Explain the Normal Probability Distribution, techniques of sampling, the Central Limit Theorem, and the concept of standard error, and compute probabilities associated with normally distributed samples.
7. Test hypotheses about the value of the mean assuming the normal distribution and large sample results.
8. Select and perform common statistical tests including one- and two-tailed tests.
9. Define linear regression and correlation and discuss their applications
10. Interpret and evaluate the validity of statistical data and reports.
11. Demonstrate proficiency in understanding, interpreting, evaluating and applying quantitative data and information.

Additional Instructor Pre-Assignments/Notes/Comments:

Students will need a calculator for this course. The instructor will be using a TI 83/84 Graphing Calculator. 


Large Group Discussion

Small Group Discussion


Evaluation Criteria:

  • 7 Problem Sets worth 100 points each.
  • 3 Exams worth 200 points each
  • Attendance/Participation 10 points for each class.
  • Assignments are due at the beginning of class. If the assignment is not received at the start of class it will be considered late. Late assignments are not accepted unless completed with the Instructor.

Grading Criteria:

  • A 90-100 % and at least 14 classes attended.
  • B 80-89 % and at least 13 classes attended.
  • C 70-79 % and at least 13 classes attended.
  • D 60-69 % and at least 13 classes attended.
  • F Below 60 % or 12 or less classes attended


Fall 2016 textbook data will be uploaded on August 4. We strongly suggest that you verify the information below with our online bookseller EdMap before purchasing textbooks from another vendor. If your course is at the Winooski center, check the UVM Bookstore for textbook and pricing information.

No Text Required, ISBN: NTR,    $0.00

Attendance Policy:

Attendance is essential for your success in this class. Missing three classes will result in a failing grade. A pattern of arriving late and leaving early causes disruption and may result in a marked absence. 

Contact Faculty:

Email: Patricia Gordon
Hiring Coordinator for this course: Virginia Gellman

Please note: In order to receive accommodations for disabilities in this course, students must make an appointment to see the Americans with Disabilities Coordinator in their site and bring documentation with them.

Academic Honesty: CCV has a commitment to honesty and excellence in academic work and expects the same from all students. Academic dishonesty, or cheating, can occur whenever you present -as your own work- something that you did not do. You can also be guilty of cheating if you help someone else cheat. Being unaware of what constitutes academic dishonesty (such as knowing what plagiarism is) does not absolve a student of the responsibility to be honest in his/her academic work. Academic dishonesty is taken very seriously and may lead to dismissal from the College.

Course description details subject to change. Please refer to this document frequently.

To check on space availability, choose Search for Classes.

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