Syllabus

Week 1: Meet and Greet

• M: Apocalyptic Revelations
• W: The Scientific Method - Active Learning Exercise, Review: Scientific Method
• F: Lab/Exercise: Install software tools

Week 2: Introduction

• M: Labor Day ... aaawww shucks!
• W: Python Intro, R Intro
• F: Lab/Exercise: GREP Searches - Data Files and Descriptive Statistics

Week 3: Review Descriptive Statistics

• M: Review: Descriptive Stats; Review: Probability and the Magic of P; Review: Analytical Stats; R Exercises: Setup; R Exercises: DataManagement; Python Exercises: Setup
• W: Self-guided solutions for probability challenges
• F: Lab/Exercise: Please solve these problems without discussing with others in the class: Lab - Probabilities1. Also please quickly review what goes into standard error, confidence intervals, and one sample t-test

Week 4: Review Correlations - Two Dependent Variables

We will learn some basic tools for obtaining terms for statistical analysis. In preparation for next week remind yourself of the basic principles of parametric analysis, such as those underlying ANOVA, linear regression, etc.

• M: Self-guided solutions for probability challenges
• W: Review: The Normal Distribution, Review: t-Tests
• F: Assignment: Lab - Probabilities2;
  Practices: R Exercises: Combos; R Exercises: Associations; R Exercises: Normal Distribution; Python Exercises: Associations; Python Exercises: Normal Distribution; Lab - Associations2

Week 5: Review ANOVA - One Dependent Variable, One Independent Variable

• M: Review: ANOVA: - the Basics, the Terms, the Asumptions
• W: Self-guided exploration of R and Python code: Review: Correlation; Example Code: R Exercises: tTest; R Exercises: ANOVA; optional: Python Exercises: tTest/ANOVA
  
• F: Lab - ANOVA and ANOVA vs. Regression

Week 6: Parametric Models - One Dependent Variable, One Independent Variable

• M: Multi-way ANOVA
• W: Review: Linear Regression Analysis: the Basics; Curve fitting with parametric Regression Analysis
• F: Assignment: Self-guided exploration on Regression. R Exercises: Regression Models and Curve Fitting
  Practices: Python Exercises: Regression

Week 7: Parametric Models - One Dependent Variable, Multiple Independent Variables

• M: Assumption of ANOVA/Regression, Exploration of the Residuals
  
• W: 
• F: Assignment: Multi-factor Design and the Assumptions of ANOVA , Lab Assignment:Select Predictor Model for Response Variable, Cichlid Brain Morphology

Week 8: Multiple Independent Terms

• M: Matrix Operations, Variance/Covariance Matrix
  
• W: Multiple Linear Regression Analysis: the Basics, R Exercises: Multiple Linear Regression Models
• F: 

Week 9: Chicago/Running

• M:
  
• W: 
• Lab: Assignment: Analysis of a Running Log

Week 10: Factor Analysis

• M: Factor Analysis
• W: self-guided analysis
• Lab: Michelle Dataset of Chemical Measures in Sandusky Bay

Week 11:

• M: Proximities and Cluster Analysis
• W: self-guided analysis
• Lab: Michelle Dataset of Chemical Measures in Sandusky Bay 

Week 12:

• M:
  
• W: 2d graphing
• Lab:
  

Week 13:

• M:
• W: 
• Lab: Vet Day

Week 14:

• M:
  
• W:
  
• Lab: 

Week 15:

• M:
  
• W:
• Lab:

Week 16

• M: 
• W:
  
• Lab:
  

Week 17:

• M:
  
• W:
• Lab: lab: testing physiological responses to osmotic regulation and caloric loading, with no nutrition whatsoever ...

ready to use ...

• Repeated Measures Designs, Analyze data on repeated measures design. Own data if available?
• Discriminant Analysis, Multivariate Descriptive Statistics
• MANOVA, Canonical Correlation
• MANOVA/DFA of Expression Data, optiponal: Grouping data based on shared characteristics
• R Exercises: MANOVA, R Exercises: Canonical Correlation
• Assignment: Cluster Analysis of Expression Data, optional: R Exercises: Multivariate Descriptive Statistics
• Power analysis
• Lecture: Replicated Goodness of Fit Tests, Lecture: Contingency Tables
• Proximity Matrices, Matrix Correlations
• Power Analysis

• Lecture: Log-likelihood and Log-Linear Models
• Lecture: Logistic Regression for ordinal or nominal dependent variables
• Lecture: Circular Statistics
• Lecture: Spatial Data Analysis
• Lecture: Multidimensional Scaling
• Lecture: Correspondence Analysis
• Lecture: Review of Stats Paper
• Lecture: Review of Papers with Complex Data 2
• Lecture: Review of Papers with Complex Data 3
• Lecture: Deterministic non-linear Systems and the Study of Complexity
• Lecture: Time Series, Survival Analysis, Failure Analysis
• Sequential Analysis, Temporal Autocorrelation, Analysis of Sound Data
• Lecture: Resampling and Randomization
• Lecture: Contingency Tables
• Strange attractors and chaos
• Review: Experimental Designs

• Overview
  
• Descriptives [1,2,n]
  
• Parametric [1,2,n * 1,2,n]
• Non-Parametric [1,2,n * 1,2,n]
  

• Lab:
  • Data Transformations
  • Multivariate Analyses 1
  • R Exercises: Repeated Measures Design
  • R Exercises: Descriptives
  • optional: Python Exercises: Descriptives
  • optional: Lab - Associations1

  Java Exercises 8: Mantel's Matrix Correlations