Class 1: Single Samples
Data: Excel file Swine Weights.xls
Weight gains of 100 swine during a 20-day period (From Snedecor and Cochran, 1967)
I. Estimating Simple Descriptive Statistics
A. Convert xls
Lesson 16-Correlation Analysis
I. Simple Correlation
A. Correlation vs. Regression
1. Suppose we have the following data on the relationship between femur length
and height from 11 men:
2. Clearly it
Lesson 15-Non-Parametric Methods
I. Why non-parametric methods.
A. Most of the methods we have covered, including regression and ANOVA, assume
that errors are normally distributed. This is true for bo
Lesson 14-Bootstrapping and Jackknifing
I. The Bootstrap
A. Purpose
1. The Bootstrap is a method for
a. estimating parameters and their confidence intervals
b. testing for differences in means between
Lesson 13-Split Plot Designs
I. What is a split-plot design?
A. Split-plot designs have at least two experimental factors.
1. Typically, treatment levels of at least one of these factors cannot be app
Lesson 12: Analysis of Covariance
I. Analysis of Covariance: Theory
A. We have so far examined regression analysis, which involves continuous variables,
and ANOVA, which involves class variables. ANCO
Lesson 11 Mixed Model Anova
I. What is a mixed model ANOVA?
A. It is an ANOVA that contains both random and fixed effects, typically as crossed
factors.
B. The mechanics of performing the ANOVA are si
Lesson 10: Factorial ANOVA
I. Types of Factorial ANOVA
A. Randomized block
1. Blocking is a technique that is used to factor out some of the environmental
variance in order to increase the power of th
Lesson 9: Nested ANOVA
I. Theory
A. For this lesson, we will consider Nested ANOVA.
1. Last time, we considered only one factor. Schematically, this may be
represented in the following way:
2. There i
Lesson 8: One-Way ANOVA
I. Basic Theory for Fixed Experimental Effects
A. Imagine we do an experiment in which we establish a number of different treatments,
we subject a number of individuals to each
Lesson 7: Non-linear Regression and Logistic Regression
I. Reasons for doing Non-Linear regression
A. Curve-fitting
B. Hypothesis Testing (e.g. Testing for non-linearity)
II. Polynomial Regression
A.
Lesson 6: Multiple Regression
I. In this lesson we will cover three topics:
A. Performing regression with more than one independent variable
B. Choosing the "best" regression model
C. Performing contr
Lesson 5-Simple Regression
I. The Data
A. X: Independent variable-in principle, measured without error
B. Y: Dependent variable-in principle, thought to be determined by independent
variable.
C. Some
Lesson 4: Two-sample comparisons
I. Comparison of means of continuously distributed variable
A. We will use the Bumpas dataset again. This time we will compare the mean lengths
of birds that survived
Lesson 3
Single Samples (cont.) and Two-Sample Analysis
I. Estimation of selfing rate
A. Common problem for plant evolutionary biologists
B. Experiment
1. Take 50 plants that are homozygous for allele
Class 2: Single Binomial Sample
I. The data
A. This data comes from crosses that one of my graduate students, Tom Chappell did for
his dissertation. He was studying the interaction between morning glo
Answer to Lesson 4 Assignment
First, set up the likelihood model.
To do this, recognie there are 4 types of maternal plant: WWAA, WWaa, wwAA, and
wwaa. Let the frequency of A in the outcross pollen th