Syllabus

Week 1:

• Review: Scientific Method
• Intro to Student Projects
• Lab: Master the tools needed for processing datafiles and review basic concepts of the introductory courses.
  • Data Files, Formats, Variable Types, and Descriptive Statistics
  • Prepare for a discussion of the basic principles of parametric analysis, sich as those that underlie ANOVA, linear regression, etc.
• optional Java Exercises 1: Setup1

• Review: Descriptive Stats, Review: The Normal Distribution
• Review: Probability and the Magic of P, Review: Analytical Stats
• Lab: Discuss methods for descriptive statistics and practice concepts in probability. We will discuss the basics of Power analysis
  • Descriptive Stats
  • Probabilities1
• optional Java Exercises 2: Setup2

Week 3:

• Lecture: Matrix Operations; Lecture: Variance/Covariance Matrix; Worksheet: Variance/Covariance; Review Correlation;
• Lab: more probabilities and whether things have anything to do with each other
  • Probabilities2
  • Associations1
• optional Java Exercises 3: Matrix Algebra

• Review: ANOVA: the Basics and Review: ANOVA: the Nitty Gritty; Worksheet: ANOVA;
• Review: Linear Regression Analysis: the Basics and Worksheet: Regression; Lecture: Linear Algebra, Lecture: Curve fitting with parametric regression analysis
• Lab:
  • Associations2
  • ANOVA vs. Regression

Week 5

• Student Dataset: Melanie Project on Lark Sparrows
• Lecture: Multiple Linear Regression Analysis and Lecture: Logistic Regression for ordinal or nominal dependent variables
• Lab:
  • Melanie's Lark Sparrows
• optional Java Exercises 4: Matrix solution to Multiple Linear Regression Analysis

Week 6

• Lecture: Factor Analysis
• Lab:
  • The Running Log

Week 7

• Lecture: Multi-way ANOVA
• Lecture: Introduction to Multivariate Concepts; Lecture: Multivariate Descriptive Statistics
• Lecture: Repeated Measures ANOVA
• Lab:
  • Nikol's Cat Project; Lecture: Nested ANOVA;
  • Lab: Multi-factor Design and the Assumptions of ANOVA

Week 8

• Lecture: MANOVA; Lecture: Canonical Correlation
• Lecture: Discriminant Analysis
• Lab:
  • revisit Melanie's Lark Sparrows
  • Debo's Project

Week 9

• Lecture: Select statistical techniques
• Lecture: Proximity Matrices
• Lab:
  • Data Transformations
  • Debo's Project

Week 10

• Lecture: Cluster Analysis
• Lecture: Contingency Tables and Replicated Goodness of Fit Tests; Lecture: Log-likelihood and Log-Linear Models
• Lab:
  • Beth's Project

Week 11

• Lecture: Correspondence Analysis; Lecture: Multidimensional Scaling
• Lecture: Review of Papers with Complex Data 1
• Lab:
  • Jen's Project

Week 12

• Lecture: Review of Papers with Complex Data 2
• Lecture: Matrix Correlations
• Lab:
  • Ted's Project
• optional Java Exercises: Mantel

Week 13

• Lecture: Review of Papers with Complex Data 3
• Lecture: Spatial Analysis; Lecture: Resampling
• Lab:
  • Budha's Project

Week 14

• Lecture: Circular Statistics
• Lecture:
• Lab:
  • Mingcai's Project

Week 15

• Lecture: Deterministic non-linear Systems and the Study of Complexity; Lecture: Strange attractors and chaos
• Lecture: Sound Analysis
• Lab:
  • Lexi's Project

not used this year:

• Lecture: Time Series, Survival Analysis, Failure Analysis
• Lecture: Sequential Analysis, Temporal Autocorrelation, Analysis of Sound Data
• Lecture: Power analysis