Spring 2023  MAT2021VT01  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: 01252023 to 05032023
Last day to drop without a grade: 02122023  Refund Policy
Last day to withdraw (W grade): 03262023  Refund Policy
This course has started, please contact the offering academic center about registration
Faculty
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 2122 and later:
Mathematics
Note
 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.
 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 twotailed 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/computerrecommendations/
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.
Methods
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:
 Identify at least one part of the reading you found unclear or confusing.
 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 realworld 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 straightfaced petitions for extra credit or makeup work from students who are unsatisfied with their midterm 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:
 High  Low 
A+  100  98 
A  Less than 98  93 
A  Less than 93  90 
B+  Less than 90  88 
B  Less than 88  83 
B  Less than 83  80 
C+  Less than 80  78 
C  Less than 78  73 
C  Less than 73  70 
D+  Less than 70  68 
D  Less than 68  63 
D  Less than 63  60 
F  Less than 60  
P  100  60 
NP  Less than 60  0 
Weekly Schedule
Week/Module  Topic   Readings   Assignments 

1  Introduction to Data     

2  Summarizing Data     

3  Probability     

4  Distributions of Random Variables     

5  Foundations for Inference     

6  Inference for Categorical Data     

7  Inference for Categorical Data     

8  Inference for Numerical Data     

9  Inference for Numerical Data     

10  Introduction to Linear Regression     

11  To be determined     

12  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 nonsatisfactory 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 onground 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

Provide disability documentation to the Accessibility Coordinator at your academic center. https://ccv.edu/discoverresources/studentswithdisabilities/

Request an appointment to meet with accessibility coordinator to discuss your request and create an accommodation plan.

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.
