Quadrats, ANOVA. Quadrat shape ? ? ? ? ? ? ? 1. Edge effects best worst.

Slides:



Advertisements
Similar presentations
Statistics and Research methods Wiskunde voor HMI Bijeenkomst 5.
Advertisements

Two and more factors in analysis of variance
Experimental Statistics - week 5
Multiple Comparisons in Factorial Experiments
ANOVA TABLE Factorial Experiment Completely Randomized Design.
i) Two way ANOVA without replication
Experiments with both nested and “crossed” or factorial factors
STT 511-STT411: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE Dr. Cuixian Chen Chapter 14: Nested and Split-Plot Designs Design & Analysis of Experiments.
Chapter 14Design and Analysis of Experiments 8E 2012 Montgomery 1.
Assumptions: In addition to the assumptions that we already talked about this design assumes: 1)Two or more factors, each factor having two or more levels.
Design of Experiments and Analysis of Variance
ANOVA lecture Fixed, random, mixed-model ANOVAs
Analysis of Variance 2-Way ANOVA MARE 250 Dr. Jason Turner.
N-way ANOVA. Two-factor ANOVA with equal replications Experimental design: 2  2 (or 2 2 ) factorial with n = 5 replicate Total number of observations:
The Two Factor ANOVA © 2010 Pearson Prentice Hall. All rights reserved.
© 2010 Pearson Prentice Hall. All rights reserved The Complete Randomized Block Design.
Analysis of Variance 2-Way ANOVA MARE 250 Dr. Jason Turner.
Lesson #23 Analysis of Variance. In Analysis of Variance (ANOVA), we have: H 0 :  1 =  2 =  3 = … =  k H 1 : at least one  i does not equal the others.
ANalysis Of VAriance (ANOVA) Comparing > 2 means Frequently applied to experimental data Why not do multiple t-tests? If you want to test H 0 : m 1 = m.
Lecture 14 Analysis of Variance Experimental Designs (Chapter 15.3)
Analysis of variance (3). Normality Check Frequency histogram (Skewness & Kurtosis) Probability plot, K-S test Normality Check Frequency histogram (Skewness.
ANOVA Single Factor Models Single Factor Models. ANOVA ANOVA (ANalysis Of VAriance) is a natural extension used to compare the means more than 2 populations.
Analysis of Variance Introduction to Business Statistics, 5e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing.
Intro to Statistics for the Behavioral Sciences PSYC 1900 Lecture 14: Factorial ANOVA.
Analysis of Covariance Goals: 1)Reduce error variance. 2)Remove sources of bias from experiment. 3)Obtain adjusted estimates of population means.
Nested and Split Plot Designs. Nested and Split-Plot Designs These are multifactor experiments that address common economic and practical constraints.
Biostatistics-Lecture 9 Experimental designs Ruibin Xi Peking University School of Mathematical Sciences.
Text reference, Chapter 14, Pg. 525
BIOL 582 Lecture Set 10 Nested Designs Random Effects.
More complicated ANOVA models: two-way and repeated measures Chapter 12 Zar Chapter 11 Sokal & Rohlf First, remember your ANOVA basics……….
Factorial Experiments Analysis of Variance (ANOVA) Experimental Design.
CHAPTER 12 Analysis of Variance Tests
The Randomized Complete Block Design
Analysis of Variance (ANOVA) Randomized Block Design.
Discussion 3 1/20/2014. Outline How to fill out the table in the appendix in HW3 What does the Model statement do in SAS Proc GLM ( please download lab.
Chapter 19 Analysis of Variance (ANOVA). ANOVA How to test a null hypothesis that the means of more than two populations are equal. H 0 :  1 =  2 =
IE341 Midterm. 1. The effects of a 2 x 2 fixed effects factorial design are: A effect = 20 B effect = 10 AB effect = 16 = 35 (a) Write the fitted regression.
Chapter coverage Part A Part A –1: Practical tools –2: Consulting –3: Design Principles Part B (4-6) One-way ANOVA Part B (4-6) One-way ANOVA Part C (7-9)
1 Nested (Hierarchical) Designs In certain experiments the levels of one factor (eg. Factor B) are similar but not identical for different levels of another.
1 Every achievement originates from the seed of determination.
Intermediate Applied Statistics STAT 460 Lecture 17, 11/10/2004 Instructor: Aleksandra (Seša) Slavković TA: Wang Yu
1 Always be mindful of the kindness and not the faults of others.
Experimental design.
ETM U 1 Analysis of Variance (ANOVA) Suppose we want to compare more than two means? For example, suppose a manufacturer of paper used for grocery.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 14 Comparing Groups: Analysis of Variance Methods Section 14.3 Two-Way ANOVA.
Statistical Analysis Professor Lynne Stokes Department of Statistical Science Lecture 18 Random Effects.
Randomized block designs  Environmental sampling and analysis (Quinn & Keough, 2002)
Chapter 13 Design of Experiments. Introduction “Listening” or passive statistical tools: control charts. “Conversational” or active tools: Experimental.
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 14 Factorial Experiments (Two or More Factors)
IE241: Introduction to Design of Experiments. Last term we talked about testing the difference between two independent means. For means from a normal.
One-Way Analysis of Variance Recapitulation Recapitulation 1. Comparing differences among three or more subsamples requires a different statistical test.
ANOVA Overview of Major Designs. Between or Within Subjects Between-subjects (completely randomized) designs –Subjects are nested within treatment conditions.
Psychology 202a Advanced Psychological Statistics December 8, 2015.
Factorial BG ANOVA Psy 420 Ainsworth. Topics in Factorial Designs Factorial? Crossing and Nesting Assumptions Analysis Traditional and Regression Approaches.
CHAPTER 3 Analysis of Variance (ANOVA) PART 2 =TWO- WAY ANOVA WITHOUT REPLICATION.
Дисперсионный анализ ANOVA
The 2 nd to last topic this year!!.  ANOVA Testing is similar to a “two sample t- test except” that it compares more than two samples to one another.
CHAPTER 3 Analysis of Variance (ANOVA) PART 1
Nested Designs Ex 1--Compare 4 states’ low-level radioactive hospital waste A: State B: Hospital Rep: Daily waste.
MADAM SITI AISYAH ZAKARIA
i) Two way ANOVA without replication
ANOVA lecture Fixed, random, mixed-model ANOVAs
ANalysis Of VAriance (ANOVA)
Two-Factor Studies with Equal Replication
Two-Factor Studies with Equal Replication
Area of a Composite Calculate the area of this shape Total Area =
The Randomized Complete Block Design
Quadrat sampling Quadrat shape Quadrat size Lab Regression and ANCOVA
Definitional Formulae
A Bestiary of ANOVA tables
Presentation transcript:

Quadrats, ANOVA

Quadrat shape ? ? ? ? ? ? ? 1. Edge effects best worst

Quadrat shape 2. Variance best

Quadrat size 1. Edge effects ? ? ? ?? ? best worst 3/5 on edge 3/8 on edge

Quadrat size 2. Variance High variance Low variance

Quadrat size So should we always use as large a quadrat as possible? Tradeoff with cost (bigger quadrats take l o n g e r to sample)

Quadrat lab Use a cost (“time is money”): benefit (low variance) approach to determine the optimal quadrat design for 10 tree species. Hendrick’s method Wiegert’s method Cost: total time = time to locate quadrat + time to census quadrat Benefit: Variance Size & shape affect!

Quadrat lab What is better quadrat shape? Square or rectangle? What is better quadrat size? 4, 9,16, 25 cm 2 ? Does your answer differ with tree species (distribution differs)? 16 cm 22cm

ANOVA Example: formal notation Example 1 Ecologists: E r 10 Papers: P f 2 Example 2: Populations: P r 2 Herbivory: H f 2 Example 3: Light: L f 3 Nutrients: N f 3 Blocks: B r 3

Fixed-effects ANOVA (Model I) All factors are fixed Random-effects ANOVA (Model II) All factors are random Mixed-model ANOVA (Model III) Contains both fixed and random effects, e.g. randomized block!

Two-way factorial ANOVA How to calculate “F” Fixed effect (factors A & B fixed) Random effect (factors A & B random) Mixed model (A fixed, B random) Factor A Factor B A x B MS A MS Error MS B MS Error MS A x B MS Error MS A MS A x B MS B MS A x B MS Error MS A x B MS Error MS B MS Error MS A MS A x B

Factorial design: All levels of one factor crossed by all levels of another factor, i.e. all possible combinations are represented. If you can fill in a table with unique replicates, it’s factorial! Pea plant Bean plant Corn plant Ambient CO 2 Double CO 2

Nested design In this example, strain type is “nested within” fertilizer. Fertilizer is often called “group”, strain “subgroup” The nested factor is always random No fertilizerNitrogen fertilizerPhosphorus fertilizer Strain A Strain B Strain C Strain D Strain E Strain F

Strain A Strain B Strain C ONP Strain D Strain E Strain F Fertilizer

No fertilizerNitrogen fertilizerPhosphorus fertilizer Strain A Strain B Strain C Strain D Strain E Strain F Grand mean Variance: Group

No fertilizerNitrogen fertilizerPhosphorus fertilizer Strain A Strain B Strain C Strain D Strain E Strain F Variance: Subgroup within a group Grand mean Variance: Group

No fertilizerNitrogen fertilizerPhosphorus fertilizer Strain A Strain B Strain C Strain D Strain E Strain F Variance: Subgroup within a group Variance: Among all subgroups Grand mean Variance: Group

Nested ANOVA: “A” Subgroups nested within “B” Groups, with n replicates In our example, A=2, B=3 and n=2 Total Groups MS Subgroups within groups MS Among all subgroups MS Groups MS Subgroups within groups B-1 Subgroups within groups B(A-1) ABn-1 df F Among all subgroups AB(n-1)

Formal notation cont. A f 6 x B r 5 tells us that this is a factorial design with factor A “crossed” with factor B A f 6 (B r 5 ) tells us that this is a nested design with factor A “nested within” with factor B. In other words, A is subgroup, B is group.

Group exercise (groups of 3) Experimental design handout  Write out the factors and levels using formal notation

Example 1: E r 10 x P f 2 Example 2: P r 2 (H f 2 ) Example 3: B r 3 x L f 3 x N f 3