K300 Statistics, Prof. Kruschke

Syllabus for K300 Statistics

Prof. John K. Kruschke

Jump to: Syllabus. Schedule. Oncourse (new window).

Instructor Assistant
Name Prof. John K. Kruschke Steve Grimes
Office Hours

and Location		 W 2:30-3:30
& by appt.
336 Psych		 M 4:00-5:30
& W of exam weeks 4:00-5:30
287 Psych (computer room)
E-mail kruschke@indiana.edu stgrimes@indiana.edu
Phone 855-3192

Class Time and Place: Spring semester, 2005 (Section 9885). Tuesdays and Thursdays, 2:30-3:45. Psychology Building, room 226.

Required Textbooks:

  • Aron, Aron & Coups (2005). Statistics for the Behavioral and Social Sciences: A Brief Course. 3rd Edition. Pearson/Prentice-Hall. ISBN 0-13-150508-4 (paperback).
  • Rosenberg (2004). The Excel Statistics Companion. Wadsworth/Thomson Learning. ISBN 0-534-64230-6 (paperback with CD).

    Other Required Equipment:

  • Excel. We will be using software called Excel, which is available on all the campus computers and is also available free to IU students from IUware under "Office Suites" as part of Office 2003.
  • Web pages. There will be much info posted on the class website: http://www.indiana.edu/~jkkteach/K300/. Please check often! Grades will be posted on Oncourse.
  • Calculator. You will find it very useful to have a calculator, for homeworks and exams. Any calculator with a square root function should be sufficient. I recommend a calculator powered by photovoltaic cells, because batteries have an uncanny knack for dying during exams.

    Instructional Goals: Inferential statistics are at the foundation of all scientific knowledge. Statistical methods are merely ways to make decisions when we have uncertain information. Because all information in life is uncertain, and we constantly have to make decisions, statistical inference permeates life.
          One of my goals for this class is to motivate your interest in statistical methods. I will try to provide many real-world examples that involve issues in which you can get personally invested. If you have suggestions for application topics, please do tell me.
          Another of my goals for the course is for you to acquire some transferable computer skills, no matter what field you go into later. To do this, I will have you learn to use Excel. Many of you will have already used this general-purpose spreadsheet program, but hopefully you will learn some new facets of it in this class.
          Of course I want to help you learn core concepts of statistical inference. Fundamental among these concepts is the notion of a sampling distribution; we will use computers to simulate the generation of sampling distributions. I will emphasize the logic of hypothesis testing, and the related notions of confidence intervals and statistical power. As examples of this logic, we will study various applications of the t-test and simple analysis of variance (ANOVA). (If I had my druthers, I'd teach Bayesian techniques, too, but I feel obligated to cover classical statistics first, and there won't be time for Bayesian approaches.)

    Discussion: Unfortunately we do not have separate discussion sections or lab sections outside of lecture.

  • Please do ask questions in class.
  • Please also discuss questions on the Oncourse discussion forum.
  • Please alert me to interesting real-world issues that can be addressed by statistical methods.

    Schedule. A linked page shows each week's topics, readings, homework assignments, and exam dates. You will see that the course is split into five segments of about three weeks each. During each segment there are two homework assignments and an exam.

    Homework: There are regular homework assignments, one per week except exam weeks, comprising 10 homework assignments altogether. The schedule page links to the specific assignments. The homework is your primary means for "hands on" experience with the concepts, and so doing it is crucial for success in the course!

  • Homework is due at the beginning of class on the day it is due. Any homework turned in after the instructor collects it at the beginning of class is marked late.
  • Late homework submitted to the Psychology Department Receptionist (behind the window of the Main Office) by 4:30pm the next day will be accepted but automatically marked down 20% (in addition to any deductions for errors). Homework will not be accepted after 4:30pm the day after it is due.
  • Solutions to the homework will be posted on the web sometime soon after 4:30pm the day after it is due.
  • Each homework assignment counts 40 points.
  • Your lowest homework score will not count toward the computation of your final grade. If you suffer severe circumstances that interfere with more than one homework assignment, please discuss your situation with me immediately when the crisis occurs. If you miss a second homework assignment you must have documented excuses for both missed assignments.

    Exams: There will be four midterm exams and a final exam (5 exams altogether), spaced at approximately three week intervals. See the schedule page for exact dates.

  • All exams are mandatory.
  • The final exam is cumulative. The midterm exams will emphasize material from the most recent part of the course, but the material is inherently cumulative, so in that sense the midterm exams are cumulative too.
  • Calculators are permitted during exams.
  • Each midterm exam counts 100 points. The final exam counts 160 points.
  • The lowest of your four midterm exam scores will not count toward your final grade. (The final exam does count in all cases.) In particular, if you must miss a midterm exam because of illness or obligation, that zero will not count toward your grade. If severe and documented circumstances require that you miss two midterm exams, contact the instructor immediately, preferably before the missed exams. Note that if you miss two midterm exams you must have documented excuses for both exams. In these extremely rare cases, a make-up exam will be arranged which might be different than the in-class exam.

    Grading scale, out of 820 points possible. These thresholds are subject to change during the first half of the course if student scores are distributed inappropriately. This scale was created under the assumption that homework scores will typically be very high.
    A+ >= 800 pts
    A >= 780
    A- >= 760     		B+ >= 740 pts
    B >= 720
    B- >= 700     		C+ >= 680 pts
    C >= 660
    C- >= 640     		D >= 580 pts
    F otherwise
    Grading: Course grades are based on the 9 included homework scores, the 3 included midterm exam scores, and the final exam score. No extra credit projects are allowed. Each homework assignment counts 40 points. Each midterm exam counts 100 points. The final exam counts 160 points. Thus, there are 9x40 + 3x100 + 160 = 820 points possible. The course letter grade is determined by your percentage of points obtained in the included homeworks and exams, according to the table above right.

    Important note regarding missed homework and exams. The policy regarding dropping the lowest exam and homework scores is designed to forgive a student for a week or two during which life just didn't go as smoothly as hoped, and to accommodate students with genuine emergencies or immovable outside obligations. Please notice that it would be extremely unwise for you to try take advantage of the system and just "slack off" for a homework and an exam. There are two ways that doing so would come back to haunt you. First, the final exam is cumulative and mandatory, so you need to study all the material anyway. Second, if you capriciously consume your allocation of dropped homework or exam, and then an emergency pops up and you really need to miss a homework or exam, you are stuck with a ZERO for that missed homework or exam. You will notice from the table above that losing 40 points for a homework, or 100 points (gasp!) for an exam, would be devastating to your grade. So even if you're not feeling that you've mastered the material for a week, it is still best to turn in whatever partial homework you can and show up for the exams, because even a low score is better than a zero, and you will probably get to drop that low score anyway if you stay on top of things the rest of the time.

    Lecture notes. Lecture notes are not provided. Much of the lectures will be interactive presentations of computer software, or solving of example problems. If you must miss a lecture, please get notes from a classmate (if they are willing).

    Disclaimer. All information in this syllabus is subject to change. Changes will be announced in class.