This course introduces the students to both descriptive and inferential statistics. Emphasis is placed on the practical use of statistics in the collecting, organizing, analyzing and interpreting of data. Students will learn standard topics such as sampling, bias, organization of data, measures of central tendency and dispersion, correlation and regression, probability, normal and standard normal distributions, confidence intervals and hypothesis testing.
By the end of this course, you will be able to:
On the course start date, students will have access to orientation. This must be completed to be able to gain access to the first module in the course. Students must complete the first module to gain access to the next one. We recommend that students spend about 15 hours per week to complete a course in 16 weeks. When trying to complete the course in less than 16 weeks, we typically see students do this successfully within 12-14 weeks. Instructors will be timely in grading and feedback, but it will not be instant.
There is a highly recommended Practice Proctored Exam available to all students. The first attempt is free. This exam does not cover course material and is not included in your overall course grade. It prepares test takers for what the testing environment will be like, what forms of identification are needed, and provides a chance to test your external webcam with a live proctor. This is a great way to become familiar with and prepare for your exam!
Weeks 1 – 15 each have a 30-point homework assignment. You will have one attempt.
There are 4 discussions (weeks 3, 7, 10, and 14), and each discussion is worth 30 points.
There is a midpoint assessment in Week 9, worth 160 points. This is a proctored and timed assessment. You will have one attempt. You will have 2.5 hours to complete the assessment. This exam must be taken through ProctorU. See UNE’s Online ProctorU Site for information about signing up and scheduling your exam. The official UNE webcam is required (see the Course Materials section, above, for more information).
There is a course project that is broken up into 3 parts. Each part is worth 80 points, for a total of 240 points (Part 1 is due in Week 6, Part 2 is due in Week 12 and Part 3 is due in Week 16).
There is a course reflection essay that is worth 30 points, due in Week 16.
Your grade in this course will be determined by the following criteria:
Assignment Category | Points | Percent |
---|---|---|
Homework | 450 | 45% |
Discussions | 120 | 12% |
Midpoint Assessment Exam | 160 | 16% |
Statistics Project Part 1 | 80 | 8% |
Statistics Project Part 2 | 80 | 8% |
Statistics Project Part 3 | 80 | 8% |
Course Reflection Essay | 30 | 3% |
Total | 1000 points | 100% |
Grade | Points Grade | Point Average (GPA) |
A | 94 – 100% | 4.00 |
A- | 90 – 93% | 3.75 |
B+ | 87 – 89% | 3.50 |
B | 84 – 86% | 3.00 |
B- | 80 – 83% | 2.75 |
C+ | 77 – 79% | 2.50 |
C | 74 – 76% | 2.00 |
C- | 70 – 73% | 1.75 |
D | 64 – 69% | 1.00 |
F | 00 – 63% | 0.00 |
Week |
Topic |
Readings |
Assignments |
1 |
Introduction to Statistics |
Textbook: Chapter 1—Introduction, 1.1, 1.2 |
Homework (30 pts) |
2 |
Levels of Measurement |
Textbook: Chapter 1— 1.3, 1.4, Review |
Homework (30 pts) |
3 |
Organizing Data |
Textbook: Chapter 2—Introduction, 2.1, 2.2 |
Discussion (30 pts) Homework (30 pts) |
4 |
Organizing Data |
Textbook: Chapter 2—2.3, 2.4 |
Homework (30 pts) |
5 |
Measures of Center |
Textbook: Chapter 2—2.5, 2.6 |
Homework (30 pts) |
6 |
Measures of Variation |
Textbook: Chapter 2—2.7, Review |
Homework (30 pts) Project Part 1 (80 pts) |
7 |
Correlation |
Textbook: Chapter 12—Introduction, 12.1, 12.2 |
Discussion (30 pts) Homework (30 pts) |
8 |
Regression |
Textbook: Chapter 12—12.3, 12.5, 12.6, Review |
Homework (30 pts) |
9 |
Probability |
Textbook: Chapter 3—Introduction, 3.1, 3.2, Review Irma Shifting Forecasts: It’s All a Matter of Probability Probability |
Homework (30 pts) Midpoint Assessment (160 pts) |
10 |
Normal Distribution |
Textbook: Chapter 6— Introduction, 6.1, 6.2, 6.3, Review Normal Table |
Discussion (30 pts) Homework (30 pts) |
11 |
Central Limit Theorem |
Textbook: Chapter 7— Introduction, 7.1., 7.3, Review Normal Table |
Homework (30 pts) |
12 |
Confidence Intervals |
Textbook: Chapter 8—Introduction, 8.1 |
Homework (30 pts) Project Part 2 (80 pts) |
13 |
Confidence Intervals |
Textbook: Chapter 8—8.2, 8.3, Review Student t table |
Homework (30 pts) |
14 |
Hypothesis Testing |
Textbook: Chapter 9—Introduction, 9.1, 9.2, 9.3 |
Discussion (30 pts) Homework (30 pts) |
15 |
Hypothesis Testing |
Textbook: Chapter 9—9.4, 9.5, Review |
Homework (30 pts) |
16 |
Course Wrap Up |
None |
Project Part 3 (80 pts) Reflection Essay (30 pts) |
Your Student Support Specialist is a resource for you - they will monitor course progression and provide assistance or guidance when needed. Please don’t hesitate to contact them for assistance, including, but not limited to course planning, course materials, billing, current problems or issues in a course, technology concerns, or personal emergencies.
Questions? Visit the Student Support Science Prerequisites page
Togetherall is a 24/7 communication and emotional support platform monitored by trained clinicians. It’s a safe place online to get things off your chest, have conversations, express yourself creatively, and learn how to manage your mental health. If sharing isn’t your thing, Togetherall has other tools and courses to help you look after yourself with plenty of resources to explore. Whether you’re struggling to cope, feeling low, or just need a place to talk, Togetherall can help you explore your feelings in a safe supportive environment. You can join Togetherall using your UNE email address.
Check Brightspace for specific instructor and support specialist contact information.
The Student Lounge Discussion Forum is a designated support forum in which students may engage with each other and grapple with course content. Feel free to post questions, seek clarification, and support each other, but be mindful of UNE's Academic Integrity Policy.
Your instructor will monitor this forum. However, if you are seeking specific and timely answers to questions about course content or your personal grades, please contact your instructor via course messages. For questions about course materials, program policy, and how to navigate and proceed through the course, please contact your Student Service Advisor through the Student Portal.
Your course may have proctored exams. The University of New England has contracted with ProctorU to provide students with the most convenient online exam proctoring system. This system provides a simple, no cost to the student, secure, online proctor for exams and allows the student to take all the exams at home and on their own schedule.
Upon enrollment into the course, each student will register with ProctorU and establish a login name and password. This will give the student access to all of ProctorU's services. When ready, students will schedule each of their proctored exams with ProctorU. Exams must be scheduled at least 72 hours in advance to avoid fees. Prior to taking their exams, students must be sure that they have downloaded any required additional software. They must also be sure their testing site's connection meets the minimum requirements by using ProctorU's "Test It Out" utility.
Upon the exam day and hour, students will log in to ProctorU and click on "exams". After following the procedures outlined at ProctorU's website, the student will log in to Brightspace and locate their correct exam. The proctor will then allow student access to that exam.
Students must follow all proctoring requirements for their exams to be credited. Please contact your instructor for specific feedback.
Students will receive two attempts at all proctored examinations. The higher score of the two attempts will be calculated into the final grade. Students can schedule their second attempt by following the same ProctorU instructions as with the original exam.
All students are encouraged to utilize a second attempt on their exams in order to improve their overall performance in the course.
Discussion topics cover events or materials related to this course that contribute to a deeper understanding of key concepts and allow you to interact with your classmates and the instructor. Each discussion topic may require you to conduct internet research, read additional materials, visit a specific webpage, AND/OR view a short video before writing a response following the specific guidelines in the discussion topic prompt.
To earn full credit you will need to post a response to the discussion topic, respond to the original posts of other students, and then contribute meaningfully to an ongoing discussion. You may need to post your initial response before you will see any posts from your classmates. For special cases where one or two students are accelerating faster through the course, the instructor will participate in the discussion so that everyone has the opportunity to interact.
Please see Brightspace for a full description, along with specific guidelines, for each discussion topic. Discussion board assignments should be completed, along with all other assignments in the course, in the order that they appear. Due to the course design, you may be unable to take a proctored exam if you do not complete all assignments that appear prior to that exam.
Please also refer to the Grading Policy/Grade Breakdown section of the syllabus to learn the percentage of your grade that each discussion is worth.
Please review the technical requirements for UNE Online Programs: Technical Requirements
A schedule of lectures and assignments is included in this syllabus. This is, however a self-paced course and you can complete the course in less time.
Please visit the enrollment page to review the withdrawal and refund policies.
Students are expected to attempt and complete all graded assignments and proctored exams by the end date of the course. View the incomplete grade policy..
Due to the Family Educational Rights and Privacy Act, only the student may request official transcripts. This may be done online by going to the University of New England Registrar website and following the directions on the page.
The University of New England values academic integrity in all aspects of the educational experience. Academic dishonesty in any form undermines this standard and devalues the original contributions of others. It is the responsibility of all members of the University community to actively uphold the integrity of the academy; failure to act, for any reason, is not acceptable.
Academic dishonesty includes, but is not limited to the following:
Charges of academic dishonesty will be reviewed by the Program Director. Penalties for students found responsible for violations may depend upon the seriousness and circumstances of the violation, the degree of premeditation involved, and/or the student’s previous record of violations. Appeal of a decision may be made to the Dean whose decision will be final. Student appeals will take place through the grievance process outlined in the student handbook.
Generative AI (GenAI) applications (like ChatGPT) have proven to be powerful and effective tools, and students are encouraged to become familiar with and use them. However, as with any tool, students must use GenAI in ways that support learning, not replace it. Learning to use AI responsibly and ethically is an important skill in today’s society.
In their courses, students are not allowed to use advanced automated tools, such as generative AI tools, on assignments unless explicitly directed to do so. Each student is expected to complete each assignment, including labs and quizzes as applicable, without substantive assistance from others, including automated tools.
Using AI-content generators to complete assignments without proper attribution violates academic integrity. By submitting assignments in UNE courses, you pledge to affirm that they are your own work and you attribute use of any and all tools and sources.
Unauthorized use of AI is treated as a violation of academic integrity.
If permitted, students should indicate and cite any use of AI tools.
Instructors should clearly reiterate, using UNE Online’s Policy, how students can use AI tools in their courses, and communicate this policy to students at the beginning of the semester.
Students must follow the academic integrity policy of the University of New England.