# Biometry for Coastal Sciences

Syllabus Biometry for Coastal Sciences Fall 2018

Class 01 – Introduction to data and statistical analysis

• The nature of statistical analysis
• Objectives of statistical analysis
• Description and inference
• Objects, variables, and scales
• Types of biometric data
• Variable classes

Food for thought

Class 02 – Frequency Distributions and Variation

• Frequency and cumulative frequency
• Theoretical frequency distributions
• Normal Distribution
• Measures of central tendency
• Variation and associated measures

Biometry Lecture 02

Class 03 – Probability, Sampling, and Parameters

• Probability
• Random variables
• Sampling from a normal distribution
• Sampling from a bionomial distribution
• Parameter Estimation
• Student’s t-distribution

Biometry Lecture 03

Class 04 – In-class assignment

For your outside of class review and consideration:

Class 05 – Hypothesis Testing and Statistical Power

Biometry Lecture 05

Link to zipped file with the R code for: power analysis function

Class 06 – In-class assignment

In_class_assignment_06

Class 07 – Correlation

• Patterns of correlation
• Indicators
• Interpretation and calculation of the correlation coefficient, r
• Rank correlation

Clarification on determining the critical value of the t distribution for null hypothesis significance testing (best resource I have found):

http://janda.org/c10/Lectures/topic06/L24-significanceR.htm

Biometry Lecture 07

Class 08 – Midterm 01

Exam 01 Answer Key from 2017

HW01:

• Kachigan Exercises (p. 495):
• Note if you don’t have time or desire to do all of the these then only do those questions that have answers to ‘selected’ exercises (Kachigan 546, Zar “Answers to selected exercises”). I give you this large HW assignment because I believe it is the best way to learn the material. Only the selected exercises will be graded.
• Ch. 1: 1, 2, 3, 4, 5, 6, 7, 8 (use internet), 12
• Ch. 2: 1, 3 (use < 15 words), 4, 5, 6, 7, 8-20
• Ch. 3: 1, 2, 3, 5, 6, 7, 8 (use technical terminology), 9, 10, 11 to 13, 15, 16 (but explain using your statistical knowledge and investigation, why pie charts should be avoided)
• Ch 4: 1 to 25
• Ch 5: 1 to 1 to 14, 17, 18, 19, 20, 23, 26, 27, 28 (this is z-score!), 29 (and this is one reason we use them), 29 to 32
• Ch 6: 1 to 3, 5 to 7, 26, 27
• Ch 7: 1 to 6, 12 to 15, 17
• Ch 8: 1 to 7
• Ch 9: 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13 to 16, 19, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34, 35, 36
• Ch 10: 1-15
• Zar (4th edition)
• 6.2, 6.3
• 7.2, 7.4
• 250 words focusing on Broman and Woo (2018)
• 500 words compare and contrast Trafimow et al. 2017 and Benjamin et al. 2018

Class 09 – Simple Linear Regression

• Regression model
• Fitting
• Confidence intervals and description of variance
• Residuals
• Predictors and Scaling

Biometry Lecture 09

Lecture 09 Simple Linear Regression (Excel Worksheet)

Class 10 – Multiple Linear Regression

• Regression model
• Fitting
• Dummy variables
• Multi-colinearity

Biometry Lecture 10

Class 11 – In class assignment

Correlation practice

In_class_assignment_11

Correlation and Power In class assignment

correlation and power (access this file to get the function for the simulation)

Differences in power among statistical tests

Class 12- In class assignment

Simple and Multiple linear Regression

In_class_assignment_12 (Simple Linear Regression)

In_class_assignment_12b (Multiple Linear Regression)

Class 13 – Comparing two mean values

• t-test

Biometry Lecture 13 Comparing two means

Class 14 – In-class assignment

In-Class Assignment 14 Comparisons of two means

Class 15 – One-way ANOVA

Biometry Lecture 15 Comparing multiple means one-way ANOVA

Class 16 – In-class assignment

In_class_assignment_16

Class 17 – Factorial ANOVA

Also:

ANOVA as a linear model video – I recorded this for a previous class and thought it may be informative to you. Let me know if you found it helpful!

Here is the text (slightly modified) used in the “ANOVA as a linear model” video

Anova_as_linear_model

Class 18 – Midterm 02

Exam 02 Answer Key from 2017

Exam 02 Answer Key from 2018

HW02:

• Note if you don’t have time or desire to do all of the these then only do those questions that have answers to ‘selected’ exercises (Kachigan 546, Zar “Answers to selected exercises”).
• Fields
• Kachigan
• Ch. 11 (All Selected Exercises)
• Zar (4th edition)
• 17.1
• 17.3
• 20.1 (must show that data can be used for parametric analysis)
• 20.3
• 20.4

Class 19 – Analysis of Categorical Data

Biometry Lecture GOF and Contingency Tables

Class 20- In class assignment

Biometry_Lecture_20_GOF

Class 21- Non-parametric statistical methods

Biometry Lecture 20 Non Parametric Tests

Class 22- In class assignment

To be posted

Class 23- Computer-intensive methods

In_class_assignment_(show and tell)

Hesterberg bootstrap primer

Lab 10 Permutation Test

Class 24- Eating Turkey
Class 25- Non-linear curve fitting and AIC

Biometry Lecture AIC

Leaf and Murphy Case Study

Leaf and Murphy Case study data

Class 26- Midterm 03

HW 1 of 3: Focus 100% of your commentary on the statistical evaluation of these works. Take the role as a reviewer of the statistical approaches on these methods. Really looking forward to seeing your thoughts – ps. Increasing sample size is rarely if ever the answer. No minimum page/word number – just give me your best effort, max would be about 750 words or so total. Choose one: 1 and 2, or 3 below.

HW 2 of 3:

Zar (4th edition) 22.1, 22.2, 22.3, 22.4

HW 3 of 3:

1. Use non-linear curve-fitting techniques to answer question #4 (at the end of the case study).
2. Fit at least three different models to these data.
3. Use AIC to evaluate alternative model fit. If the delta AIC value among competing models is < 4, use a model weighing approach to determine predicted weight-at-length.

Class 27- Review
Class 28- Final Exam