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

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

**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

**Class 04 – In-class assignment**

For your outside of class review and consideration:

**Class 05 – Hypothesis Testing and Statistical Power**

- Reading:
- Trafimow et al. 2017 Trafimow 2017

Benjamin et al. 2018 BenjaminEtAlRedefineStatisticalSignificance

- Trafimow et al. 2017 Trafimow 2017
- The null hypothesis
- Tests of the mean
- Significance level
- Type 1 and 2 error
- Difference between means and proportions
- Power
- Assumptions of parametric statistics

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

**Class 06 – In-class assignment**

**Class 07 – Correlation**

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

**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**