Details:

Sections of 587	The different sections of 587 are not interchangeable. Each is essentially a different course. Section 2 focuses on the analysis of data from experimental studies, although we do briefly discuss observational studies. Most examples will be relevant to the target audience (agriculture and biology). Because there is no social science section in Fall 2023, some examples will come from the social sciences. Computing will be your choice of SAS, JMP, or R. Section 1 is for graduate students in the physical sciences, math, and engineering. It includes more mathematical detail.
Student background:	The course requires no prior familarity with statistics. However, some exposure would be helpful because we move pretty quickly through material commonly covered in undergrad introductory statistics courses. As graduate students, I expect you to ask questions when you don't understand something. I use a graduate-level grading scheme (mostly A's and B's) but I reserve the right to give lower grades when appropriate.
Text:	Ramsey and Schaefer, The Statistical Sleuth This book is organized in a non-traditional way because it emphasizes how statistical methods get used to answer scientific questions. Each chapter starts with two (or more) case studies that illustrate the use of methods about to be discussed. This is followed by sections that describe those methods and a section of related issues. Please skim the case studies and read the main material in the assigned chapter(s) prior to the start of the lectures. In some chapters, parts of the related issues will also be assigned. These will be listed in the assigned readings section of the class website. My lectures will cover the same concepts, but I will often use different examples and may use a different presentation.  There is not time to lecture on all the details. I expect you to read the assigned material and ask questions on anything you don't understand. It will probably help to reread the chapter(s) and especially the case studies after the relevant lectures.  Throughout the semester, I will distribute a reading list identifying the most important parts of each chapter. An electronic version of the text is provided by default through Canvas/Immediate Access. The cost is billed on your U Bill. I believe the charge is ca $45. If you don't want this access, you can opt out. Additional information on Immediate Access is here.
Lab:	Lab time will be used for four different activities:  Some hands-on illustrations of statistical principles. Return HW Discussion and Q/A on lecture material and homework problems. Use of SAS, JMP, and/or R (most of the lab period). Each week's lab will cover computing for the material in that week's lecture. This part of the lab is "flipped". I provide support for three statistical computing languages, JMP, R and SAS. You choose which you want to work with. You may work with multiple if you wish. Material on the class web site will describe how to implement statistical methods in each language. For SAS and R, this will be a file of code and a document describing what the code does and how to interpret the output. For JMP, this will be a document including screen shots that describes how to navigate the menu system to obtain the desired analysis and then how to interpret the output. You are to work through your choice of document and ask if you have any questions or if something doesn't work. Most weeks, I provide a self-assessment exercise and answers. This will not be graded. I recommend you work through the self-assessments during the lab period, but it is optional. You may also work on the HW during lab period.
Homework:	Homework assignments will be posted on the web site and announced in class. They will be due Wednesday in lecture Goal is to provide practice using statistical concepts.  Discussion with friends and classmates is strongly encouraged.  Please write up your answers individually. Copying papers is not a good way to learn and will not be tolerated.  No late homework accepted. Lowest homework score will be dropped.  Solutions will be posted on the class web page soon after the due date.
Computing:	This class focuses on statistical concepts, not details of a specific computing package. We will rely on the computer to do most, if not all, the appropriate calculations, so most of lab time will discuss how to use statistical software. The choice of software will be discussed in the first week of class. We will provide support for SAS, JMP, and R. You may use another package if your lab group uses something other than SAS, JMP, or R. Please check with me to make sure that package is appropriate for this class. SPSS is fine. EXCEL is not appropriate. I don't know enough about GraphPad/Prism to assess it. You must bring a laptop to lab unless you only want to watch over someone else's shoulder. You will need to install your chosen software during or shortly after the week 1 lab.
Exams:	Midterm exams will be held during the lab period in weeks 7 and 12. The final exam time has yet to be determined. You should bring a calculator. I will provide formulae and computer output. My goal is to see how well you can use class material to analyze data. Makeup exams will be given only if you contact me and get approval prior to the scheduled exam.
Other questions:	Please ask in class or e-mail me:  pdixon at iastate dot edu
Syllabus statements	Free expression: Iowa State University supports and upholds the First Amendment protection of freedom of speech and the principle of academic freedom in order to foster a learning environment where open inquiry and the vigorous debate of a diversity of ideas are encouraged. Students will not be penalized for the content or viewpoints of their speech as long as student expression in a class context is germane to the subject matter of the class and conveyed in an appropriate manner. Other syllabus statements are linked here. These include statements on the academic honesty policy, accomodations, prep week, free expression, harassment/discrimination, and public health. Ask if you have any questions.
Academic honesty policy:	To clarify how this applies to your work in this class: On homework assignments: I encourage you to help each other understand the questions, write code, debug code, and interpret the output. You may share code, but I encourage you to understand that code even if you didn't write it. You are required to write your answers in your own words. On exams: You are to do all work individually.
Prep Week:	There will be a graded homework assignment due on Wednesday of week 15 (quiet week). This will cover material through Week 14 of the class. It will be similar in length to previous HW assignments, perhaps a bit shorter.
Contact Information:	If you are experiencing, or have experienced, a problem with any of the issues in the syllabus statements, please contact Philip Dixon. If you prefer to bring a concern to the attention of university administration, please contact Dr. Dan Nettleton, the Statistics Department Chair, or academicissues@iastate.edu.