Web Schedules

Fall 2023
Spring 2023
Summer 2023

One Credit Courses

Fall 2023
Spring 2023
Summer 2023

No Cost Textbook/Resources Courses

Fall 2023
Spring 2023
Summer 2023

Low Cost Textbook/Resources Courses

Fall 2023
Spring 2023
Summer 2023

Course Planning by Program


Essential Objectives

Course Syllabus

Revision Date: 18-Dec-22

Spring 2023 | MAT-2021-VT01 - Statistics

In Person Class

Standard courses meet in person at CCV centers, typically once each week for the duration of the semester.

Location: Brattleboro
Credits: 3 (45 hours)
Day/Times: Wednesday, 06:00P - 08:45P
Semester Dates: 01-25-2023 to 05-03-2023
Last day to drop without a grade: 02-12-2023 - Refund Policy
Last day to withdraw (W grade): 03-26-2023 - Refund Policy
This course has started, please contact the offering academic center about registration


John Woodward
View Faculty Credentials

Hiring Coordinator for this course: Debra Grant

General Education Requirements

This section meets the following VSC General Education Requirement(s) for Catalog Year 21-22 and later:
  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 catalog year page, and your academic advisor.
  2. Courses may only be used to meet one General Education Requirement.

Course Description

This course is 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. Prerequisite: Math & Algebra for College or equivalent skills.

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.
12. Apply mathematical reasoning to analyze social justice problems in a variety of different contexts and consider whether these approaches are just and equitable.

Required Technology

More information on general computer and internet recommendations is available on the CCV IT Support page. https://support.ccv.edu/general/computer-recommendations/

Please see CCV's Digital Equity Statement (pg. 45) to learn more about CCV's commitment to supporting all students access the technology they need to successfully finish their courses.

Required Textbooks and Resources

This course only uses free Open Educational Resources (OER) and/or library materials. For details, see the Canvas Site for this class.


Readings and exercises for this course will come in four flavors:

  • Primary readings and problem sets (weekly)
  • Supplemental readings (as assigned, likely weekly)
  • Written reflections to primary/supplemental readings (weekly)
  • Case study project (due at end of term)

Primary Readings and Problem Sets

Our primary text for this class will be OpenIntro Statistics (4th edition), available for free online in PDF format here. This text and some of its companion material (slides, videos, etc.) will be the source of your weekly reading and problem set assignments. We will also directly interact with various selections of the OpenIntro text in class together, including, as much as possible, the datasets behind its many examples, which are also available online.

Supplemental Readings

Other readings may be assigned periodically to reinforce, extend, or even challenge the primary text material. These supplemental readings could include:

  • Data journalism stories
  • Data visualization examples
  • Popular press stories about statistical findings
  • Academic journal articles about statistical studies
  • Online datasets
  • Survey questionnaires and results
  • Research reports
  • Cat videos
  • Etc.

Written Reflections

Statistics is a complex and abstract subject. The material we cover in class will inevitably cause you some level of confusion and frustration (very possibly more so the closer attention you pay!). This is as it should be. Confusion is a natural and important stage of any learning process.

In my experience, one of the most valuable ways to begin working through confusion is to attempt to write down, in your own words, what precisely you feel you are not yet understanding. Taking the time to find the words to articulate why something doesn’t make sense can sometimes reveal the missing link, or at least the path toward understanding.

In that spirit, each week, in addition to your problem set solutions, you will submit a short, written reflection to at least one of the assigned readings—your choice of either the primary text or a supplemental source. There are only two guidelines for these weekly written reflection assignments:

  1. Identify at least one part of the reading you found unclear or confusing.
  2. Try to explain why this material didn’t make sense to you. Be as specific as possible.

Your reflections will help me understand where I could improve or expand my presentation of course material. They will also give me a wider view of how you are digesting the lessons, beyond getting the right or wrong answers on the problem sets.

Case Study Project

Statistics, in its purest application, is really just a set of tools that helps us make the best possible guess about the nature of phenomena that we can’t directly observe. The question that statistics tries to answer is not, “What is true?” but instead, “How sure can we be about what we think is true?”

Of course, this is not necessarily how the findings of statistical studies are always understood by a lay audience of policy makers, journalists, and busy, working people. Even expert practitioners of statistics can overlook or downplay the inherent uncertainty of their "statistically significant" conclusions.

The primary text provides us with many illustrations of how a responsible application of statistics can reliably improve and expand our collective understanding of how the world works. Throughout the class we will also discuss and read about different real-world examples of statistical studies and findings being misused or misrepresented by various actors.

For the case study project, you will pick one of these examples to analyze in more detail outside of class, either individually or as part of a small group. There will be several format options for packaging what you’ve learned from your analysis into a final work product, e.g., written report, slide deck, verbal presentation, etc.

Regardless of the format you choose, the overall goal will be the same: to explain how the claims made about or by the statistical study in question are (or are not) supported by the evidence and methods used by the study.

Evaluation Criteria

Grades will be based on your performance in each of the four categories listed in the table below. The weighting factor will determine how much each category counts toward your overall grade.

Extra Credit

I will entertain any and all straight-faced petitions for extra credit or makeup work from students who are unsatisfied with their mid-term evaluation. It will be up to the student to initiate this conversation, but if you do, I will be ready and willing to brainstorm appropriate extra credit or makeup assignments together.

Grading Criteria

CCV Letter Grades as outlined in the Evaluation System Policy are assigned according to the following chart:

A Less than 9893
A-Less than 9390
B+Less than 9088
B Less than 8883
B-Less than 8380
C+Less than 8078
C Less than 7873
C-Less than 7370
D+Less than 7068
D Less than 6863
D-Less than 6360
FLess than 60 
NPLess than 600

Weekly Schedule

Week/ModuleTopic  Readings  Assignments


Introduction to Data



Summarizing Data






Distributions of Random Variables



Foundations for Inference



Inference for Categorical Data



Inference for Categorical Data



Inference for Numerical Data



Inference for Numerical Data



Introduction to Linear Regression



To be determined



To be determined


Attendance Policy

Regular attendance and participation in classes are essential for success in and are completion requirements for courses at CCV. A student's failure to meet attendance requirements as specified in course descriptions will normally result in a non-satisfactory grade.

  • In general, missing more than 20% of a course due to absences, lateness or early departures may jeopardize a student's ability to earn a satisfactory final grade.
  • Attending an on-ground or synchronous course means a student appeared in the live classroom for at least a meaningful portion of a given class meeting. Attending an online course means a student posted a discussion forum response, completed a quiz or attempted some other academically required activity. Simply viewing a course item or module does not count as attendance.
  • Meeting the minimum attendance requirement for a course does not mean a student has satisfied the academic requirements for participation, which require students to go above and beyond simply attending a portion of the class. Faculty members will individually determine what constitutes participation in each course they teach and explain in their course descriptions how participation factors into a student's final grade.

Missing & Late Work Policy

Class sessions are held on Wednesday evenings at 6:00 PM.

Written reflections will be due on the Tuesday following each Wednesday class session. This gives you five full days (plus change) to complete the reading and produce your written response.

Weekly problem set answers will be due on the second Friday after each Wednesday class session. This gives you eight full days (plus change) to complete the assigned work. It also provides us an opportunity to use class time to address difficulties you may be having before the submission deadline.

Late submissions for both the written reflections and the problem set solutions will be accepted with a penalty. Any problem set assignment that is turned in on time can be resubmitted with corrections at any time. Late problem set assignments will not be given this option.

The purpose of these deadlines and late penalties is not so much to punish tardiness as it is to ensure I can review your work and develop meaningful feedback in the hours I've set aside to do so, which will generally be on weekends. That said, I do not intend to penalize you for things you cannot control. But I do ask that you let me know as soon as possible if extenuating circumstances are preventing you from meeting a deadline.

Accessibility Services for Students with Disabilities:

CCV strives to mitigate barriers to course access for students with documented disabilities. To request accommodations, please
  1. Provide disability documentation to the Accessibility Coordinator at your academic center. https://ccv.edu/discover-resources/students-with-disabilities/
  2. Request an appointment to meet with accessibility coordinator to discuss your request and create an accommodation plan.
  3. Once created, students will share the accommodation plan with faculty. Please note, faculty cannot make disability accommodations outside of this process.

Academic Integrity

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.