Biometry for Coastal Sciences

Syllabus Biometry for Coastal Sciences Fall 2018

Google docs questions and answers

Class 01 – Introduction to data and statistical analysis

  • Reading: Broman and Woo (2018) Data Organization in Spreadsheets
  • 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

Lecture 01 Biometry Lecture 01 Why do we need statistics

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

Biometry Lecture 07

Class 08 – Midterm 01

Exam 01 Answer Key from 2017

HW01:

  • Kachigan Excercises (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 excercises”). 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
  • Reflective reading
    • 250 words focusing on Broman and Woo (2018)
    • 500 words compare and contrast Trafimow et al. 2017 and Benjamin et al. 2018
    • What is reflective reading? See this document: Reflective reading

Class 09 – Simple Linear Regression

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

Class 10 – Multiple Linear Regression

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

Class 11 – In class assignment

Class 12 – Comparing two means

  • Statistical modeling
  • t-test

Class 13 – In-class assignment

Class 14 – One-way ANOVA

  • Statistical modeling

Class 15 – In-class assignment

Class 16 – Factorial ANOVA

  • Statistical modeling

Class 17 – Midterm 02

Class 18 – Analysis of Categorical Data

Class 19 – In class assignment
Class 20 – Non-parametric statistical methods
Class 21 – In class assignment
Class 22 – Computer-intensive methods
Class 23 – In class assignment
Class 24 – Non-linear curve fitting and AIC
Class 25 – Midterm 03
Class 26 – Review
Class 27 – Final Exam