Age Length Key. Age Analysis 2 Importance Many stock parameters are annual rates. –e.g., growth & mortality (components of yield) “time” must be recorded.

Slides:



Advertisements
Similar presentations
Data Analysis for Two-Way Tables
Advertisements

Cross-Tabulation Tables Tables in R and Computing Chi Square.
CHAPTER 23: Two Categorical Variables The Chi-Square Test ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture.
AP Statistics Section 14.2 A. The two-sample z procedures of chapter 13 allowed us to compare the proportions of successes in two groups (either two populations.
FMSP stock assessment tools Training Workshop LFDA Theory.
Chapter 2 Presenting Data in Tables and Charts
Nemours Biomedical Research Statistics March 2009 Tim Bunnell, Ph.D. & Jobayer Hossain, Ph.D. Nemours Bioinformatics Core Facility.
Barents Sea fish modelling in Uncover Daniel Howell Marine Research Institute of Bergen.
1 Business 90: Business Statistics Professor David Mease Sec 03, T R 7:30-8:45AM BBC 204 Lecture 2 = Finish Chapter “Introduction and Data Collection”
Hui-Hua Lee 1, Kevin R. Piner 1, Mark N. Maunder 2 Evaluation of traditional versus conditional fitting of von Bertalanffy growth functions 1 NOAA Fisheries,
Modular 13 Ch 8.1 to 8.2.
Chapter 2 – Data Collection and Presentation
CS1100: Computer Science and Its Applications Creating Graphs and Charts in Excel.
Quantifying Data.
Alok Srivastava Chapter 2 Describing Data: Graphs and Tables Basic Concepts Frequency Tables and Histograms Bar and Pie Charts Scatter Plots Time Series.
How many conditional age-at-length data are needed to estimate growth in stock assessment models? Xi He, John C. Field, Donald E. Pearson, and Lyndsey.
Chapter 2 Presenting Data in Tables and Charts. Note: Sections 2.1 & examining data from 1 numerical variable. Section examining data from.
AP Statistics Section 14.2 A. The two-sample z procedures of chapter 13 allowed us to compare the proportions of successes in two groups (either two populations.
Methods: Collection of Blue Catfish Otoliths
N318b Winter 2002 Nursing Statistics Specific statistical tests: Correlation Lecture 10.
Population Dynamics Mortality, Growth, and More. Fish Growth Growth of fish is indeterminate Affected by: –Food abundance –Weather –Competition –Other.
Sampling distributions - for counts and proportions IPS chapter 5.1 © 2006 W. H. Freeman and Company.
The Randomized Complete Block Design
Copyright © 2009 Cengage Learning 15.1 Chapter 16 Chi-Squared Tests.
1/19/00 Survey Methodology Sampling EPID 626 Lecture 2.
Data Gathering Techniques. Essential Question: What are the different methods for gathering data about a population?
1 Systematic Sampling (SYS) Up to now, we have only considered one design: SRS of size n from a population of size N New design: SYS DEFN: A 1-in-k systematic.
Overview of CCSS Statistics and Probability Math Alliance September 2011.
1 STAT 500 – Statistics for Managers STAT 500 Statistics for Managers.
CHAPTER 11 SECTION 2 Inference for Relationships.
Chapter 1: Exploring Data Sec. 1.1 Analyzing Categorical Data.
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 2-1 Chapter 2 Presenting Data in Tables and Charts Statistics For Managers 4 th.
BPS - 5th Ed. Chapter 221 Two Categorical Variables: The Chi-Square Test.
Please turn off cell phones, pagers, etc. The lecture will begin shortly.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 7 Sampling and Sampling Distributions.
Introduction to Statistics Chapter 6 Feb 11-16, 2010 Classes #8-9
The two way frequency table The  2 statistic Techniques for examining dependence amongst two categorical variables.
Remote Sensing Classification Accuracy
Aim: How do we analyze data with a two-way table?
Slide Slide 1 Section 6-4 Sampling Distributions and Estimators.
Sampling distributions rule of thumb…. Some important points about sample distributions… If we obtain a sample that meets the rules of thumb, then…
Chap 8-1 Fundamentals of Hypothesis Testing: One-Sample Tests.
N318b Winter 2002 Nursing Statistics Specific statistical tests Chi-square (  2 ) Lecture 7.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Chapter 10 Correlation and Regression 10-2 Correlation 10-3 Regression.
Section 12.2: Tests for Homogeneity and Independence in a Two-Way Table.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
Basic Business Statistics, 8e © 2002 Prentice-Hall, Inc. Chap 1-1 Inferential Statistics for Forecasting Dr. Ghada Abo-zaid Inferential Statistics for.
Chapter 13 Sampling distributions
Chi-Square Analyses.
Outline of Today’s Discussion 1.The Chi-Square Test of Independence 2.The Chi-Square Test of Goodness of Fit.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
1. State the null and alternative hypotheses. 2. Select a random sample and record observed frequency f i for the i th category ( k categories) Compute.
European Patients’ Academy on Therapeutic Innovation Clinical Trial Designs.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 11 Inference for Distributions of Categorical.
ESTIMATION OF THE MEAN. 2 INTRO :: ESTIMATION Definition The assignment of plausible value(s) to a population parameter based on a value of a sample statistic.
6-1 Copyright © 2014, 2011, and 2008 Pearson Education, Inc.
SAMPLING TECHNIQUES N. JAYAKUMAR Assistant professor Dept. of Fisheries Biology and Resource Management Fisheries college & Research Institute, Thoothukudi-8.
Goodness-of-Fit and Contingency Tables Chapter 11.
Chi Square Procedures Chapter 14. Chi-Square Goodness-of-Fit Tests Section 14.1.
MTH 231 nerd/ mth231nerddotcom.  MTH 231 Week 1 DQs  MTH 231 Week 1 Populations and Sampling Paper  MTH 231 Week 2 DQs  MTH 231 Week 2 Data— Organizing,
CHAPTER 8 Estimating with Confidence
Chapter 2 Describing Data: Graphs and Tables
Organizing and Visualizing Variables
Inference for Relationships
The Randomized Complete Block Design
Sampling Distributions
Sampling.
Displaying Data – Charts & Graphs
Experimental Design Experiments Observational Studies
Presentation transcript:

Age Length Key

Age Analysis 2 Importance Many stock parameters are annual rates. –e.g., growth & mortality (components of yield) “time” must be recorded. –Usually as the age of the fish. Age assessment is vitally important. It is also EXPENSIVE!

Age Length Key 3 Concept – Overall Have a large sample of fish. Measure length on all fish. Choose a portion of sample to assign age. –Called the age sample. –Fish chosen either in proportion to the number of fish in each length category as a fixed number per each length category (more common) –Fish that are not aged are called the length sample. Develop relationship between age and length from fish in age sample. –two-way contingency table called an age-length key “Assign” age to fish in length sample with age- length key.

Age Length Key 4 Concept – Age-Length Key Age Sample len age Use 10-cm intervals for length categories Make raw contingency table Age LCat Convert to row-proportions table Age LCat LCat

Age Length Key 5 Concept – Age-Length Key Length Sample len age Age-Length Key (as a reminder) Age LCat Use 10-cm intervals for length categories Create length distribution LCat Freq LCat

Age Length Key 6 Concept – Age-Length Key Length Sample len age Age-Length Key (as a reminder) LCat Length distribution (as a reminder) LCat Freq Identify number in each length category to be assigned each age 20-cm  4*0.5 = 2 age-1  4*0.5 = 2 age-2  4*0 = 0 age-3 Randomly determine which fish are assigned these ages. LCat

Age Length Key 7 Concept – Age-Length Key Length Sample len age Age-Length Key (as a reminder) LCat Length distribution (as a reminder) LCat Freq Identify number in each length category to be assigned each age 30-cm  3*0.25 = 0.75 age-1  3*0.5 = 1.5 age-2  3*0.25 = 0.75 age-3 What to do now? LCat

Age Length Key 8 Age-Length Key – Fractionation Fractionation –When a fraction of a fish should be assigned a given age. Handling fractionation –Round all values down to integers. –For remaining number of fish, choose random ages in proportion to the proportion in each age.

Age Length Key 9 Age-Length Key – Fractionation Handling fractionation –Round all values down to integers. 30-cm  3*0.25 = 0.75 = 0 age-1  3*0.5 = 1.5 = 1 age-2  3*0.25 = 0.75 = 0 age-3 –For remaining number of fish... two ages must be chosen such that age-1 has 25% chance, age-2 has 50% chance, and age-3 has 25% chance of being selected. e.g., 2, 1 was chosen thus, randomly assign 1 age-1, 2 age-2, & 0 age-3 for fish in the 30-cm length interval

Age Length Key 10 Concept – Age-Length Key Length Sample len age Age-Length Key (as a reminder) LCat Length distribution (as a reminder) LCat Freq Identify number in each length category to be assigned each age 30-cm  3*0.25 = 0.75 age-1  3*0.5 = 1.5 age-2  3*0.25 = 0.75 age-3 Randomly determine which fish are assigned these ages. LCat  1  2 

Age Length Key 11 Concept – Age-Length Key Length Sample len age Age-Length Key (as a reminder) LCat Length distribution (as a reminder) LCat Freq Identify number in each length category to be assigned each age 40-cm  5*0.25 = 1.25 = 1 age-1  5*0.25 = 1.25 = 1 age-2  5*0.5 = 2.5 = 2 age-3 Extra fish was chosen to be age-3. Randomize age assignments. LCat  1 

Age Length Key 12 Concept – Age-Length Key Length Sample len age Age-Length Key (as a reminder) LCat Length distribution (as a reminder) LCat Freq Identify number in each length category to be assigned each age 50-cm  2*0.5 = 1 age-2  2*0.5 = 1 age-3 Randomize age assignments. LCat

Age Length Key 13 Utility Lengths in age sample should cover range of lengths in length sample. Age sample and length sample must be from the same population. –typically age sample is a subsample “Extra” effort should be put in age, not length, sample. Combine age sample and age-assigned length sample for further analysis.

Age Length Key 14 How – Apply the A-L Key Use the age.key() function to assign ages to fish in a length sample given an A-L Key. –required arguments: age-length key row proportions table as first argument. data frame with length sample as second argument. cl= (name or number of column containing the measured lengths) –optional arguments: ca= (name or number of column that should receive the assigned ages) –if this column does not exist it will be created & called “Age”. type= (A string indicating the type of randomization) – type=“SR”  semi-random (default, method described here) – type=“CR”  completely random

Age Length Key 15 How – Apply the A-L Key Demonstration with the age and lengths of spot (Leiostomus xanthurus) from Virginia. –403 fish were collected –as many as 10 per 1-inch length category were aged from otolith thin sections. 72 fish were aged –interested in mean length-at-age and age distribution. Examine Handout –Summarize() –lencat() –table() –prop.table() –ageKey()