EXST7015 : Statistical Techniques II
Simple Linear Regression
Anotated SAS example
Geaghan
Page 1
The SAS program.
I will presume you are familiar with the SAS data step. I will discuss it briefly only for this first
example.
SAS Statements – all SAS stat

EXST7025 : Biological Population Statistics II
Random coefficients regression
Geaghan
Page 1
Random coefficients model
Yij = β0 + si + (β 1 + di)Xij + eij
Note that
the βk are fixed estimates of the population average
the dependent variable is Yij
Xij is

dm'log;clear;output;clear';
*;
* From: http:/fishbull.noaa.gov/1053/demartini.pdf, Age and growth
*:
* of swordfish (Xiphias gladius) caught by the Hawaii-based pelagic
*:
* longline fishery, DeMartini, Edward E., James H. Uchiyama,
*:
* Robert L. Humphre

EXST025 - Biological Population Statistics
BEVERTON - HOLT MODEL
Development and Modeling process
a) start with a model of recruitment over time
R = N! e-M(t< -t! )
where,
R = recruitment at time tR to fishery
N! = the number at time t!
M = the instantane

EXST7025 : Biological Population Statistics
Cohort Analysis
James Geaghan
Page 1
COHORT ANALYSIS
This term was originally applied by Pope to describe a simplified Virtual Population Analysis
(VPA), which is a type of life table or demographic analysis. Th

EXST7025 - Biological Population Statistics
Monte Carlo example using Growth Models
Geaghan
Page 1
Monte Carlo Modeling
Objective - Usually used to examine the effectiveness of some analytical tool. Statisticians, in
particular, often use this to examine

ECOLOGICAL SYTEMS MODELING SYSTEMS MODEL APPROACH
A) This is a numerical method of examining models which are too large to be fitted
statistically.
1) STATISTICS allows us to fit a certain range of linear models and
non linear models.
SYSREG in SAS will

MULTIVARIATE ANALYSIS - UNIVARIATE REVIEW
a) ttest
Yi = + i
H0: = 0
Yij = + i + ij or Yij = i + ij
H0: 1 = 2 or H0: i = 0 (t-test only)
b) ANOVA
Yij = + i + ij
H0: all i equal or H0: i = 0 , CRD
Yijk = + i + ij + ijk
same H0; CRD, with nested error
Yij =

As I said in class, my thought was to let you develop this homework more or less from scratch as if it was you
own data. However, in doing the homework over the last few days I have found a few tricky aspects and I
thought I should provide guidance so you

EXST 7025
Syllabus
Spring 2011
EXST7025 - Spring 2011 - Biological Population Statistics II
Class meets: Tuesday and Thursday from 8:00 to 9:20 PM in 15 Atkinson
Professor: JAMES P. GEAGHAN
Office
149 Agriculture Administration Building
Office hours
10:45

EXST7025 : Biological Population Statistics
Growth Assignment
Spring 2009
Page 1
Source:httpwww.nicholls.edubayousphereGraduateStudentsJDavisDavis_Thesis.pdf,02/09/2011
JohnathanG.Davis.ReproductiveBiology,LifeHistoryandPopulationStructureofaBowfinAmia
Ca

EXST 7025 Population Statistics
Homework 1 (2/22/11)
Page 1
Source: 2/13/11, http:/www.nicholls.edu/bayousphere/GraduateStudents/MDantin/Dantinthesis.pdf
Mattilynn D. Dantin. Distribution and Relative Abundance of Blue Crab Callinectes Sapidus in
the Uppe

EXST7025:BiologicalPopulationStatistics
RecruitmentAssignment
Compare various versions of Recruitment Models.
A. The data are in a data set called
'PLAICE.TXT'.
B. Run the same 5 models for the Ricker
and Beverton-Holt that I ran on the
Arcto Norwegian Co

EXST7025 : Biological Population Statistics
Special Growth Topics
Page 1
Given a growth model, there are occasions when we want to estimate age from size of the change
in age between two sizes.
Starting with a von Bertalanffy growth model,
Lt L 1 e
K t t0

EXST025 - Biological Population Statistics
Growth Models Page 1
GROWTH CURVES
There are many candidate models for growth curves.
Linear models will often work adequately for short segments of the live span.
Exponential models often fit well for initial gr

Growth models: Introduction
Growth models describe the changing size of something over time. In our case we will use some
type of regression to fit the relationship between the size of an organism (or population) and time
since its inception.
The paramete

Biological Population Statistics II
Page 1
SIMPLE LINEAR REGRESSION (review - unify concepts and notation)
1) MODEL: Yi = β 0 + β1 X i + ε i or Yij = β 0 + β1 X i + ε ij
a) subscript “i” identifies individual values of X
b) subscript “j”, when used, ident

EXST 7025
Lack of Fit
Page 1
EXST7015: Marathon Footrace Example
Mean age and time by gender
The MEANS Procedure
Gender=F
Variable
N
Mean
Std Dev
Minimum
Maximum
-Age
751
33.6790945
9.2423418
17.0000000
61.0000000
TIME
753
271.7157769
45.6046112
151.75

EXST 7025 – Biological Population Statistics II
Analysis of Covariance and Lack of Fit Symmary
Page 1
Lack of Fit and Model adequacy
We have had manuscripts returned with the opinion expressed by editor or referee that means should
be fitted instead of ra

Fish
Stock
Natural Mortality (m)
Nt = N 0e− mt = N 0e− M
One of the most difficult parameters to measure
Important parameter, but may be minor influence
Mortality relationship between number and time
Fishing Mortality (f)
Leslie Model
U t = U 0 + qΣCt
Del

Stock DEPLETION MODELS (recruitment assumed to be zero)
RELATIONSHIP BETWEEN SURVIVAL and the APPLICATION of FISHING EFFORT
there are two models, one linear and one exponential
LESLIE'S METHOD
Assume that we have a fishery which is CLOSED (no in or out) a

EXST025 - Biological Population Statistics
Page 1
APPLICATION OF MODELS TO MERISTIC (or MORPHOMETRIC)
RELATIONSHIPS
- MERISTIC refers to the geometric relation between body parts
Examples archaeologists - calculate the height and weight of dead and extinc

EXST025 - Biological Population Statistics
Page 21
Another Recruitment Curve (Geaghan & Castilla, 1986)
Very little has been done with continuous recruitment, the type least well fitted by
the Ricker model and Beverton Holt model.
Suppose we have a fisher

RECRUITMENT MODELS
A. Recruits - for our purposes “Recruitment" is not reproduction (as eggs or larvae), but
those entering the population as usable stock
a) KNIFE - EDGED recruitment - all fish of an age enter at the same time of year
b) By PLATOONS - th

Experimental Statistics 7025
April 8, 1997
Exam 1
NAME _
Geaghan
Generic equations
Eq 1) Yi 1 X i i
Eq 2) Yi 0 1 X i i
Eq 3) Yi 0 1 X i 2 X i2 i
Eq 4) Yi 0 1 X i 2 X i2 3 X i3 i
Eq 5) Yi 0 X i 1 i
Eq 7) Yi 0e
1 X i
Eq 6) Yi 0 X i 1 i
Eq 8) Yi 1 X i i
i
X

EXST 7025
Homework 2
Page 1
Source: Spatial and Seasonal stock dynamics of Northern Tiger Prawns using fine-scale commercial
catch-effort data. Tasmanian Aquaculture and Fisheries Institute project no. 1999/100. Malcolm Haddon
and Kate Hodgson. December 2