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2018-19

Web Schedule Summer 2018


MAT-2021-VR01Z - Statistics I


Synonym: 163143
Location: Rutland
Credits: 3 (45 hours)
Accelerated Section: This course has special meeting dates and times. See comments below or consult VSC Web Services - Search for Sections in the VSC portal for specific dates and times. If you have any questions call the site office offering the course.
Semester Dates: 06-25-2018 to 08-09-2018
Last day to drop without a grade: 07-04-2018 - Refund Policy
Last day to withdraw (W grade): 07-22-2018 - Refund Policy
Faculty: David Blankenbaker | View Faculty Credentials
This course has started, please contact the offering academic center about registration
This section meets the following General Education Requirement(s):
Mathematics
    Note
  1. Many degree programs have specific general education recommendations. In order to avoid taking unnecessary classes, please consult with additional resources like your program evaluation, your academic program page, and your academic advisor.
  2. Courses may only be used to meet one General Education Requirement.

Comments: Accelerated course: Meets Mondays and Thursdays, 9am-12pm.

Browse the Moodle Site for this class.

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.

Textbooks:

Summer 2018 textbook data will be available on April 9. On that date a link will be available below that will take you to eCampus, CCV's bookstore. The information provided there will be for this course only. Please see this page for more information regarding the purchase of textbooks.

The last day to use a Financial Aid advance to purchase textbooks is the 3rd Tuesday of the semester. See your financial aid counselor at your academic center if you have any questions.

Contact Faculty:

Email: David Blankenbaker
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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