Attendance is required for scheduled class meetings. Each week there will be a set number of questions that must be turned in by the end of the class period. Unexcused absences for class may result in a 25% deduction for that week's assignment.
If you are working in the computer lab, you need to be aware that after 90 minutes of inactivity, the lab computers computers will shut down, and delete anything saved on the desktop or in the documents folder! Make certain that you save your work onto something permanent like a flash drive, or some sort of cloudy thingy that you can access.
The assignments are designed to lead you through the concepts behind the calculations, i.e., the process is actually more important than simply getting an answer. Taking shortcuts in Excel that have not been covered in the assignment, or using formulas other than those provided on the webpage (the examples in the textbook sometimes use formulas that are easier to calculate, but hide the purpose of the calculation) will result in a grade of zero for the question on the assignment where these shortcuts were applied. If you are confused about how to apply a particular formula, or conduct an analysis, ASK ME rather than lose points for applying a procedure from the book or a shortcut that you found on YouTube or ChatGPT. Remember...it isn't the right answer that is important, it is understanding how you get there that matters. Also remember...there are 3 ways of doing things: the right way, the wrong way, and my way. Searching for answers on the internet or asking AI may get you one of the first 2, but never the third. Please take a moment to review the AI statement.
Answers submitted without evidence of the work used to derive those answers will not receive credit. Do all of your calculations in your Excel spreadsheets. It's good practice for you, and allows me to assign partial credit if there is a simple error with one of the calculations.
TREAT THE NARRATIVE PROVIDED ON THE WEB PAGES AS A LECTURE! You should be taking notes as you work through the assignment the same way that you would for a regular lecture. While there are no exams per se for this class, once a skill or concept has been covered, you will be expected to be competent in that skill or understand that concept as we move through the new material. For example, once we move into inferential statistics, you will be calculating a value known as the sum of squares every week. The means by which we calculate this value is covered in descriptive statistics, and will not be explained again. Having the formula in your notes will save you a lot of confusion and time searching through the webpages. The first 6 lessons develop skills and concepts that we begin to apply in week 7. Historically, week 7 has been a tough week for students...
MAKE YOURSELF A CHART for weeks 7-14 that matches the type of analysis being considered with the analystical methods that are taught. For example, in week 7 you will learn that 2 means can be compared with a t-test, and in week 8 you will learn that 2 or more means can be compared using ANOVA. The final assignment will require you to decide what type of analysis to conduct for different data sets, and having an easy reference will be invaluable.
I will be available by e-mail, or by appointment to provide assistance. Feedback on your assignments will be provided by adding comments directly to your submissions in Blackboard, so be sure to review those once you have received your grade.
Assignments are due (submission via Blackboard) at 5 pm every Tuesday. Late assignments will incur a penalty of 10% for the following week, and will receive a grade of zero if more than 1 week late. This may sound Draconian, but students who fall more than a week behind rarely get caught back up.
Check with me before working ahead. Once an assignment has been posted in Blackboard, you can be certain that it has been updated for this semester, but otherwise there may be changes to assignments before they are posted. If you wish to work ahead, the home page for all of the assignments can be found at THIS LINK. You may want to bookmark this link for future reference.
Cheating, plagiarism, and other activities that can be reasonably considered as dishonest will result in a failing grade
for the course and forwarding of the matter to the University Judicial Board. Plagiarism essentially is the theft of
intellectual property
through an intentional attempt to present another's ideas or work (including AI) as your own, or through an
unintentional omission of credit. This includes sharing the answers to questions with other students, and using answers and/or work given to you
by another student (or anyone else, including a tutor or an AI platform). More than one student
has received a zero for using assignments from students who have taken the course in previous semesters. Should I find evidence that this has occurred,
I will go back through all of your assignments, and treat each instance where I find evidence as a separate instance of academic
dishonesty. If you get assistance from a tutor, be extremely careful about the amount of "help" that you receive. I would prefer that you see me with questions, but I have no issues with you seeking
assistance elsewhere, as long as they are providing you with explanations, and not with answers.
Because of the rampant use of AI in previous semesters, be prepared to explain your answer to me in person to receive credit for that question.
This does not mean that you should not work together, but there is a smart way to do it. Completing the assignment and comparing answers is the smart way to do things. If the answers are similar, then it is likely that the solutions are correct. If the answers differ, then the methodologies can be compared to find the reason for the discrepancy (which is often a simple typographical error on the spreadsheet). On the other hand, relying on someone else's answers, e.g., by dividing up the questions, only increases the probability of me seeing two identically incorrect answers, in which case, I will pursue the implication of academic dishonesty.
Ignorance of these rules will not be taken as an excuse for not following them. Acknowledging that you have read them is part of your first assignment. Send me an e-mail stating that you have read and understood the guidelines before turning in the worksheet and spreadsheet for your first assignment.
Send comments, suggestions, and corrections to: Derek Zelmer