Experiments in Complex Stands
|
|
|
- Karen Harris
- 8 years ago
- Views:
Transcription
1 Experiments in Complex Stands Valerie LeMay, Craig Farnden and Peter Marshall March 8 to April 3, 009 LeMay, Farnden, Marshall 1
2 The Challenge Design an experiment based on an objective Constraints: Must have three blocks; treatment is a cutting pattern; two or three treatments Variations, then: Variables of interest Treatments applied Numbers of replicates (i.e., experimental units) Subsampling of experimental units Data: 50 m X 800 m area, mixed species, unevenaged, measured in 1985, 1993 and 001 Assumed: There is an interest in testing for a treatment effect as well as getting an estimate of this effect March 8 to April 3, 009 LeMay, Farnden, Marshall
3 The Experimental Design: Generalized RCB Block 1 Block Block 3 C TR1 TR1 TR1 TR TR TR C TR C TR1 C TR1 TR C TR C TR1 March 8 to April 3, 009 LeMay, Farnden, Marshall 3
4 Analysis of Variance (n=replicates) Source Df MS Effect F test Block MS BL Random Treatment MS TR Fixed MS TR / MS BL X TR Block X Treatment 4 MS BL X TR Random Replicates nested in BL X TR 3 X 3 X (n-1) MS E Random Total March 8 to April 3, 009 LeMay, Farnden, Marshall 4
5 Analysis of Variance (m=subsamples) Source Df MS Effect F test Block MS BL Random Treatment MS TR Fixed MS TR / MS BL X TR Block X Treatment Replicates nested in BL X TR Samples 4 MS BL X TR Random 3 X 3 X (n-1) MS E Random 3 X 3 X n X (m-1) MS SE Random Total March 8 to April 3, 009 LeMay, Farnden, Marshall 5
6 Generalized RCB: Expected Values J=#blocks, n=#replicates σ E[MS TR ]= σ EE E[MS BL X TR ]= E[MS E ]= EE σ + n σ K B T + Jn τ k ( K k = 1 + σ EE n B T 1) If no Block by Treatment interaction, MS E can be used in the F-test March 8 to April 3, 009 LeMay, Farnden, Marshall 6
7 E[MS TR ]= E[MS BL X TR ]= E[MS E ]= Generalized RCB with subsampling: Expected Values J=#blocks, n=#replicates, m=#subsamples σ SE + mσ σ SE σ SE + mσ EE K EE + nmσ BLK TR + Jnm τ k ( K k= 1 m σ EE + nm BLK TR + σ Often experimental units are comprised of a number of individuals (e.g. trees) Subsamples within experimental units increase both the numerator and denominator of the F- test March 8 to April 3, 009 LeMay, Farnden, Marshall 7 1)
8 Sizes of Experimental Units and Numbers of Subsampling Units As the size of the experimental unit increases, the variance of experimental units decreases to a minimum for the treatment As the number of subsamples per experimental unit increases and/or the size of subsamples increases, the variance of the experimental units decreases to the same value as when the entire experimental unit is measured March 8 to April 3, 009 LeMay, Farnden, Marshall 8
9 Power Analysis Need a good estimate of variance among experimental units within treatments and blocks for a given size of experimental unit Vary the number of replicates (n) & size of the effect to obtain power Choose an experimental unit size and number of replicates based on power and cost This choice will likely vary among variables of interest March 8 to April 3, 009 LeMay, Farnden, Marshall 9
10 Particular Issues with Complex Stands When spatial/structural complexity is increased via cutting, there will be very high variability among small-size experimental units within a specific treatment For very complex stands will need large experimental units and/or many units to obtain an acceptable level of power There are many possible treatments can only include a few in the experiment likely need to include the extremes March 8 to April 3, 009 LeMay, Farnden, Marshall 10
11 Simulated Experiment Objective: To assess the results of two treatments relative to a control in terms of average tree size Other variables of interest: Diameter and height growth rates Average tree size Volume per ha Tree regeneration Understory vegetation March 8 to April 3, 009 LeMay, Farnden, Marshall 11
12 Treatments Simulated Control: No cutting Wildlife Habitat (WH): Remove trees in regularly spaced 0 X 0 m patches to permit understory vegetation regrowth to enhance sites for wildlife Growth Release (GR): Remove 5% of trees randomly to promote growth of residual stems March 8 to April 3, 009 LeMay, Farnden, Marshall 1
13 Issues in Choosing Treatments Wildlife Habitat (WH): Regular patches vs. randomly located patches? Patches plus removal of stems in patches to promote growth response? Growth Release (GR): What level of removal? Which trees? Even species & size targeted? Recovery (value) of removed stems? March 8 to April 3, 009 LeMay, Farnden, Marshall 13
14 Process Used to Assess Power 1. Simulate the treatment over the entire study area. Break each area into experimental units 3. Calculate the variance between experimental units within a treatment 4. Use this as the experimental unit variance (within a block and treatment) 5. Forecasted changes in variance discussed but not explicitly simulated, since WH requires a spatially explicit model. March 8 to April 3, 009 LeMay, Farnden, Marshall 14
15 Control, 1985 March 8 to April 3, 009 LeMay, Farnden, Marshall 15
16 GR, 1985 March 8 to April 3, 009 LeMay, Farnden, Marshall 16
17 WH,1985 March 8 to April 3, 009 LeMay, Farnden, Marshall 17
18 C or GR K function, 1985 K function, WH K(r) border theo K(r) border theo r (m) r (m) March 8 to April 3, 009 LeMay, Farnden, Marshall 18
19 C or GR March 8 to April 3, 009 LeMay, Farnden, Marshall 19
20 Post-Cutting Statistics C WH GR DBH Mean St. Dev Volume/ ha Volume/ Tree Mean Mean March 8 to April 3, 009 LeMay, Farnden, Marshall 0
21 What Variances Can We Expect? Divided each simulated treatment into experimental units: 40 X 40m (N=10); 80 X 80 m (N= 30) Calculated the variance between all possible plots for mean dbh (i.e., variance among cell means) St.Dev. 40 X 40 m: 3.1 (C); 5.3 (WH); 3. (GR) St.Dev. 80 X 80 m: 1.5 (C);.8 (WH); 1.6 (GR) Changes in time: might expect these to increase, particularly for WH. May become heteroscedastic between treatments over time March 8 to April 3, 009 LeMay, Farnden, Marshall 1
22 Simulated Power Analysis Fix α =0.05 Size of differences for practical importance Mean dbh: 5 cm Other Variables (not done): Dbh Increment: 0.5 cm Volume/ha: 0 m 3 /ha Understory vegetation: 30% increase in desirable species Standard deviation of experimental units in each block X treatment: Assumed no block by treatment interaction March 8 to April 3, 009 LeMay, Farnden, Marshall
23 n=3 n=8 March 8 to April 3, 009 LeMay, Farnden, Marshall 3
24 Overall Comments What about using a model to assess outcomes instead? Models can be used to assess a wider range of treatments If already available, this is a cheap option Downside: Really need a spatially explicit, process model? Models have unknown accuracy Still need experiments to build models for: 1) data and ) knowledge of the processes Very hard to get estimates of treatments effects and accuracies of those affects March 8 to April 3, 009 LeMay, Farnden, Marshall 4
25 What is important in experiments in complex stands? Treatments: Should be extremes? Very large experimental units are needed: For some variables, such as spatial patterns For spatially variable treatments (e.g., WH) Should do power analysis via assumed variances of experimental units Complexity of the design: For repeated measures, better to keep this simple Use covariates to reduce between experimental unit variability Consider using covariates instead of using blocks, since blocks do not explicitly help in examining the process of why variation occurs? March 8 to April 3, 009 LeMay, Farnden, Marshall 5
TFL 55 CHANGE MONITORING INVENTORY SAMPLE PLAN
TFL 55 CHANGE MONITORING INVENTORY SAMPLE PLAN Prepared for: Mike Copperthwaite, RPF Louisiana-Pacific Canada Ltd. Malakwa, BC Prepared by: Timberline Natural Resource Group Ltd. Kamloops, BC Project Number:
Describing Populations Statistically: The Mean, Variance, and Standard Deviation
Describing Populations Statistically: The Mean, Variance, and Standard Deviation BIOLOGICAL VARIATION One aspect of biology that holds true for almost all species is that not every individual is exactly
Lesson 10: Basic Inventory Calculations
Lesson 10: Basic Inventory Calculations Review and Introduction In the preceding lessons, you learned how to establish and take measurements in sample plots. You can use a program like LMS to calculate
Why Count Birds? (cont.)
AVIAN CENSUS TECHNIQUES: Why count birds? Descriptive Studies = asks what types of birds occur in a particular habitat? - Provides gross overview of bird occurrence and perhaps a crude estimate of abundance
MULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL. by Michael L. Orlov Chemistry Department, Oregon State University (1996)
MULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL by Michael L. Orlov Chemistry Department, Oregon State University (1996) INTRODUCTION In modern science, regression analysis is a necessary part
Stand Density Management Diagram
H O W T O U S E A Stand Density Management Diagram Yield predictions for a spacing prescription Ministry of Forests Forest Practices Branch Canadian Cataloguing in Publication Data Main entry under title:
Table A1. To assess functional connectivity of Pacific marten (Martes caurina) we identified three stand types of interest (open,
Supplemental Online Appendix Table A1. To assess functional connectivity of Pacific marten (Martes caurina) we identified three stand types of interest (open, simple, complex) but divided these into subclasses
Simple Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
Assume you have 40 acres of forestland that was
A l a b a m a A & M a n d A u b u r n U n i v e r s i t i e s ANR-1371 Basal Area: A Measure Made for Management Assume you have 40 acres of forestland that was recently assessed by a natural resource
10. Analysis of Longitudinal Studies Repeat-measures analysis
Research Methods II 99 10. Analysis of Longitudinal Studies Repeat-measures analysis This chapter builds on the concepts and methods described in Chapters 7 and 8 of Mother and Child Health: Research methods.
How To Run Statistical Tests in Excel
How To Run Statistical Tests in Excel Microsoft Excel is your best tool for storing and manipulating data, calculating basic descriptive statistics such as means and standard deviations, and conducting
Example G Cost of construction of nuclear power plants
1 Example G Cost of construction of nuclear power plants Description of data Table G.1 gives data, reproduced by permission of the Rand Corporation, from a report (Mooz, 1978) on 32 light water reactor
One-Way Analysis of Variance: A Guide to Testing Differences Between Multiple Groups
One-Way Analysis of Variance: A Guide to Testing Differences Between Multiple Groups In analysis of variance, the main research question is whether the sample means are from different populations. The
1/27/2013. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Introduce moderated multiple regression Continuous predictor continuous predictor Continuous predictor categorical predictor Understand
Chapter 7 Section 1 Homework Set A
Chapter 7 Section 1 Homework Set A 7.15 Finding the critical value t *. What critical value t * from Table D (use software, go to the web and type t distribution applet) should be used to calculate the
Assessment of cork production in new Quercus suber plantations under future climate. Joana A Paulo Margarida Tomé João HN Palma
Assessment of cork production in new Quercus suber plantations under future climate Joana A Paulo Margarida Tomé João HN Palma 22 May 2012 1 Introduction Climate is related to several variables that affect
Confidence Intervals for the Difference Between Two Means
Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means
Other forms of ANOVA
Other forms of ANOVA Pierre Legendre, Université de Montréal August 009 1 - Introduction: different forms of analysis of variance 1. One-way or single classification ANOVA (previous lecture) Equal or unequal
Species-of-the-Week. Blanding s Turtle (Emydoidea blandingii) Species of Special Concern in Michigan
Species-of-the-Week Blanding s Turtle (Emydoidea blandingii) Habitat Productive & clean shallow water (soft substrates) = ponds, marshes, swamps, bogs, wet prairies, slow rivers Spring & summer = terrestrial
SPLIT PLOT DESIGN 2 A 2 B 1 B 1 A 1 B 1 A 2 B 2 A 1 B 1 A 2 A 2 B 2 A 2 B 1 A 1 B. Mathematical Model - Split Plot
SPLIT PLOT DESIGN 2 Main Plot Treatments (1, 2) 2 Sub Plot Treatments (A, B) 4 Blocks Block 1 Block 2 Block 3 Block 4 2 A 2 B 1 B 1 A 1 B 1 A 2 B 2 A 1 B 1 A 2 A 2 B 2 A 2 B 1 A 1 B Mathematical Model
An analysis method for a quantitative outcome and two categorical explanatory variables.
Chapter 11 Two-Way ANOVA An analysis method for a quantitative outcome and two categorical explanatory variables. If an experiment has a quantitative outcome and two categorical explanatory variables that
5 Analysis of Variance models, complex linear models and Random effects models
5 Analysis of Variance models, complex linear models and Random effects models In this chapter we will show any of the theoretical background of the analysis. The focus is to train the set up of ANOVA
Chapter 4 and 5 solutions
Chapter 4 and 5 solutions 4.4. Three different washing solutions are being compared to study their effectiveness in retarding bacteria growth in five gallon milk containers. The analysis is done in a laboratory,
MS 2007-0081-RR Booil Jo. Supplemental Materials (to be posted on the Web)
MS 2007-0081-RR Booil Jo Supplemental Materials (to be posted on the Web) Table 2 Mplus Input title: Table 2 Monte Carlo simulation using externally generated data. One level CACE analysis based on eqs.
Elementary Statistics Sample Exam #3
Elementary Statistics Sample Exam #3 Instructions. No books or telephones. Only the supplied calculators are allowed. The exam is worth 100 points. 1. A chi square goodness of fit test is considered to
Gwen Landburg December 2012. Anton de Kom University of Suriname
Gwen Landburg December 2012 Anton de Kom University of Suriname Short term impacts of selective logging loss of habitat drier conditions Result: only species with a broad physiological range and species
Introduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman
Introduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman Statistics lab will be mainly focused on applying what you have learned in class with
MS DATA ANALYSIS EXAM INSTRUCTIONS
MS DATA ANALYSIS EXAM INSTRUCTIONS Please Read Carefully This exam consists of one question, with two parts. You are asked to write a report on your analyses and we strongly recommend that you begin writing
USE OF REMOTE SENSING FOR MONITORING WETLAND PARAMETERS RELEVANT TO BIRD CONSERVATION
USE OF REMOTE SENSING FOR MONITORING WETLAND PARAMETERS RELEVANT TO BIRD CONSERVATION AURELIE DAVRANCHE TOUR DU VALAT ONCFS UNIVERSITY OF PROVENCE AIX-MARSEILLE 1 UFR «Sciences géographiques et de l aménagement»
1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
Randomized Block Analysis of Variance
Chapter 565 Randomized Block Analysis of Variance Introduction This module analyzes a randomized block analysis of variance with up to two treatment factors and their interaction. It provides tables of
Study on growth and sprouts of Oak Forest for Forest fire site in South Korea. lovefriendks@hanmail.net
Study on growth and sprouts of Oak Forest for Forest fire site in South Korea Sung Cheol Jung 1, Yeon Ok Seo 1*, Kang Min Moon 2 1 Warm-Temperate and Subtropical Forest Research Center, KFRI, Jejudo, 697-050,
One-Way Analysis of Variance (ANOVA) Example Problem
One-Way Analysis of Variance (ANOVA) Example Problem Introduction Analysis of Variance (ANOVA) is a hypothesis-testing technique used to test the equality of two or more population (or treatment) means
Pressure in Fluids. Introduction
Pressure in Fluids Introduction In this laboratory we begin to study another important physical quantity associated with fluids: pressure. For the time being we will concentrate on static pressure: pressure
Tuition Reimbursement Program. Handbook
EMPLOY EE Tuition Reimbursement Program Handbook For Employees... Ed u c a t i o n m a d e a f f o r d a b l e! A t t h e E r n e s t O r l a n d o L a w r e n c e B e r k e l e y N a t i o n a l L a b
Regression Analysis: A Complete Example
Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty
One-Way Analysis of Variance
One-Way Analysis of Variance Note: Much of the math here is tedious but straightforward. We ll skim over it in class but you should be sure to ask questions if you don t understand it. I. Overview A. We
1.5 Oneway Analysis of Variance
Statistics: Rosie Cornish. 200. 1.5 Oneway Analysis of Variance 1 Introduction Oneway analysis of variance (ANOVA) is used to compare several means. This method is often used in scientific or medical experiments
AP STATISTICS (Warm-Up Exercises)
AP STATISTICS (Warm-Up Exercises) 1. Describe the distribution of ages in a city: 2. Graph a box plot on your calculator for the following test scores: {90, 80, 96, 54, 80, 95, 100, 75, 87, 62, 65, 85,
Commercial Timber Harvest Planning and Operations Standard Operating Procedures Manual. Environment and Natural Resources
Commercial Timber Harvest Planning and Operations Standard Operating Procedures Manual Environment and Natural Resources First Edition November 2005 -ii- Preface The purpose of the Commercial Timber Harvest
Section 1: Simple Linear Regression
Section 1: Simple Linear Regression Carlos M. Carvalho The University of Texas McCombs School of Business http://faculty.mccombs.utexas.edu/carlos.carvalho/teaching/ 1 Regression: General Introduction
Maine Forest Service Interpretations of the Maine Forest Practices Act Statute and Rules (12 MRSA 8867-A to 8888 & MFS Rules Chapter 20)
Maine Forest Service Interpretations of the Maine Forest Practices Act Statute and Rules (12 MRSA 8867-A to 8888 & MFS Rules Chapter 20) These interpretations take into account the full context, meaning,
The UK Timber Resource and Future Supply Chain. Ben Ditchburn Forest Research
The UK Timber Resource and Future Supply Chain Ben Ditchburn Forest Research Timber availability The landscape of timber availability in Great Britain and the United Kingdom is moving through a period
Urban Tree Planting Quantification Guidance
Urban Tree Planting Quantification Guidance June 2014 This document provides guidance for quantifying an Urban Tree Planting (UTP) Project s carbon stocks, both for purposes of estimating a project s baseline
How to calculate an ANOVA table
How to calculate an ANOVA table Calculations by Hand We look at the following example: Let us say we measure the height of some plants under the effect of different fertilizers. Treatment Measures Mean
Review of basic statistics and the simplest forecasting model: the sample mean
Review of basic statistics and the simplest forecasting model: the sample mean Robert Nau Fuqua School of Business, Duke University August 2014 Most of what you need to remember about basic statistics
What Do You See? FOR 274: Forest Measurements and Inventory. Logs and Scaling: Definitions. Tree and Wood Volume Log Volumes Cords Weight Scaling
What Do You See? FOR 274: Forest Measurements and Inventory Tree and Wood Volume Log Volumes Cords Weight Scaling Logs and Scaling: Definitions Logs: cut trees lengths of 8ft or more The process of measuring
Multivariate Analysis of Variance (MANOVA)
Chapter 415 Multivariate Analysis of Variance (MANOVA) Introduction Multivariate analysis of variance (MANOVA) is an extension of common analysis of variance (ANOVA). In ANOVA, differences among various
PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU
PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard
Two-sample t-tests. - Independent samples - Pooled standard devation - The equal variance assumption
Two-sample t-tests. - Independent samples - Pooled standard devation - The equal variance assumption Last time, we used the mean of one sample to test against the hypothesis that the true mean was a particular
Figure 1. An embedded chart on a worksheet.
8. Excel Charts and Analysis ToolPak Charts, also known as graphs, have been an integral part of spreadsheets since the early days of Lotus 1-2-3. Charting features have improved significantly over the
Objectives. Raster Data Discrete Classes. Spatial Information in Natural Resources FANR 3800. Review the raster data model
Spatial Information in Natural Resources FANR 3800 Raster Analysis Objectives Review the raster data model Understand how raster analysis fundamentally differs from vector analysis Become familiar with
E(y i ) = x T i β. yield of the refined product as a percentage of crude specific gravity vapour pressure ASTM 10% point ASTM end point in degrees F
Random and Mixed Effects Models (Ch. 10) Random effects models are very useful when the observations are sampled in a highly structured way. The basic idea is that the error associated with any linear,
TRINITY COLLEGE. Faculty of Engineering, Mathematics and Science. School of Computer Science & Statistics
UNIVERSITY OF DUBLIN TRINITY COLLEGE Faculty of Engineering, Mathematics and Science School of Computer Science & Statistics BA (Mod) Enter Course Title Trinity Term 2013 Junior/Senior Sophister ST7002
APPLIED MISSING DATA ANALYSIS
APPLIED MISSING DATA ANALYSIS Craig K. Enders Series Editor's Note by Todd D. little THE GUILFORD PRESS New York London Contents 1 An Introduction to Missing Data 1 1.1 Introduction 1 1.2 Chapter Overview
Black-Litterman Return Forecasts in. Tom Idzorek and Jill Adrogue Zephyr Associates, Inc. September 9, 2003
Black-Litterman Return Forecasts in Tom Idzorek and Jill Adrogue Zephyr Associates, Inc. September 9, 2003 Using Black-Litterman Return Forecasts for Asset Allocation Results in Diversified Portfolios
Data Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
MS-EXCEL: STATISTICAL PROCEDURES
MS-EXCEL: STATISTICAL PROCEDURES Cini Varghese I.A.S.R.I., Library Avenue, New Delhi 110 012 cini_v@iasri.res.in Microsoft (MS) Excel ( ) is a powerful spreadsheet that is easy to use and allows you to
Assessing Measurement System Variation
Assessing Measurement System Variation Example 1: Fuel Injector Nozzle Diameters Problem A manufacturer of fuel injector nozzles installs a new digital measuring system. Investigators want to determine
Do termites enhance the invasion of southern African savannas by alien plants?
Do termites enhance the invasion of southern African savannas by alien plants? Mhosisi Masocha; Andrew. K. Skidmore; Herbert H.T. Prins; Milena Holmgren; & Jan de Leeuw 1 A plant community becomes more
ABSORBENCY OF PAPER TOWELS
ABSORBENCY OF PAPER TOWELS 15. Brief Version of the Case Study 15.1 Problem Formulation 15.2 Selection of Factors 15.3 Obtaining Random Samples of Paper Towels 15.4 How will the Absorbency be measured?
2 Sample t-test (unequal sample sizes and unequal variances)
Variations of the t-test: Sample tail Sample t-test (unequal sample sizes and unequal variances) Like the last example, below we have ceramic sherd thickness measurements (in cm) of two samples representing
Analysis of Variance. MINITAB User s Guide 2 3-1
3 Analysis of Variance Analysis of Variance Overview, 3-2 One-Way Analysis of Variance, 3-5 Two-Way Analysis of Variance, 3-11 Analysis of Means, 3-13 Overview of Balanced ANOVA and GLM, 3-18 Balanced
Overview and Example Application of the Landscape Treatment Designer
United States Department of Agriculture Forest Service Pacific Northwest Research Station General Technical Report PNW-GTR-859 TU DE PA RT RE February 2012 MENT OF AGRI C U L Overview and Example Application
COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared. jn2@ecs.soton.ac.uk
COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared jn2@ecs.soton.ac.uk Relationships between variables So far we have looked at ways of characterizing the distribution
Solution Let us regress percentage of games versus total payroll.
Assignment 3, MATH 2560, Due November 16th Question 1: all graphs and calculations have to be done using the computer The following table gives the 1999 payroll (rounded to the nearest million dolars)
Using Analysis Tools: Goal Seek, Solver, and Data Tables
CHAPTER Using Analysis Tools: Goal Seek, Solver, and Data Tables In this chapter Analyzing Your Data with Excel 646 Using Goal Seek 646 Using Solver 650 Creating Amortization Tables to Calculate Mortgage
STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE
STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE Perhaps Microsoft has taken pains to hide some of the most powerful tools in Excel. These add-ins tools work on top of Excel, extending its power and abilities
Handling missing data in large data sets. Agostino Di Ciaccio Dept. of Statistics University of Rome La Sapienza
Handling missing data in large data sets Agostino Di Ciaccio Dept. of Statistics University of Rome La Sapienza The problem Often in official statistics we have large data sets with many variables and
The Research Proposal
Describes the: The Research Proposal Researchable question itself Why it's important (i.e., the rationale and significance of your research) Propositions that are known or assumed to be true (i.e., axioms
Challenges and Implementation of Effort Tracking System
Challenges and Implementation of Effort Tracking System Shalu Gupta Senior Technical Officer Nilesh Kumar Dokania Assistant Professor C-DAC Noida Guru Nanak Institute of Management C-56/1, Sector-62,Institutional
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
National and Sub-national Carbon monitoring tools developed at the WHRC
National and Sub-national Carbon monitoring tools developed at the WHRC Nadine Laporte Woods Hole Research Center A. Baccini, W. Walker, S. Goetz, M. Sun, J. Kellndorfer Kigali, 20 th June 2011 Why measuring
Predictor Coef StDev T P Constant 970667056 616256122 1.58 0.154 X 0.00293 0.06163 0.05 0.963. S = 0.5597 R-Sq = 0.0% R-Sq(adj) = 0.
Statistical analysis using Microsoft Excel Microsoft Excel spreadsheets have become somewhat of a standard for data storage, at least for smaller data sets. This, along with the program often being packaged
Restoration of Fire-adapted Ecosystems in the Central and Southern Appalachians
Restoration of Fire-adapted Ecosystems in the Central and Southern Appalachians Marek Smith The Nature Conservancy 12181-A Courthouse Hill Road Warm Springs, VA 24484 marek_smith@tnc.org Sam Lindblom The
A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data
A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data Athanasius Zakhary, Neamat El Gayar Faculty of Computers and Information Cairo University, Giza, Egypt
HANDOUT #2 - TYPES OF STATISTICAL STUDIES
HANDOUT #2 - TYPES OF STATISTICAL STUDIES TOPICS 1. Ovservational vs Experimental Studies 2. Retrospective vs Prospective Studies 3. Sampling Principles: (a) Probability Sampling: SRS, Systematic, Stratified,
Forecasting in supply chains
1 Forecasting in supply chains Role of demand forecasting Effective transportation system or supply chain design is predicated on the availability of accurate inputs to the modeling process. One of the
Concepts of Experimental Design
Design Institute for Six Sigma A SAS White Paper Table of Contents Introduction...1 Basic Concepts... 1 Designing an Experiment... 2 Write Down Research Problem and Questions... 2 Define Population...
Measuring downside risk of stock returns with time-dependent volatility (Downside-Risikomessung für Aktien mit zeitabhängigen Volatilitäten)
Topic 1: Measuring downside risk of stock returns with time-dependent volatility (Downside-Risikomessung für Aktien mit zeitabhängigen Volatilitäten) One of the principal objectives of financial risk management
Chapter 7. One-way ANOVA
Chapter 7 One-way ANOVA One-way ANOVA examines equality of population means for a quantitative outcome and a single categorical explanatory variable with any number of levels. The t-test of Chapter 6 looks
THE PERFORMANCE OF EUCALYPTUS HYBRID CLONES AND LOCAL LANDRACES IN VARIOUS AGROECOLOGICAL ZONES IN KENYA
THE PERFORMANCE OF EUCALYPTUS HYBRID CLONES AND LOCAL LANDRACES IN VARIOUS AGROECOLOGICAL ZONES IN KENYA By PHANUEL OBALLA EBBY CHAGALA-ODERA LYDIA WAMALWA VINCENT OEBA ESTON MUTITU LINUS MWANGI MARCH
Error Analysis. Table 1. Capacity Tolerances for Class A Volumetric Glassware.
Significant Figures in Calculations Error Analysis Every lab report must have an error analysis. For many experiments, significant figure rules are sufficient. For a brush up on significant figure rules,
Mobilising Vegetation Plot Data: the National Vegetation Survey Databank. Susan Wiser April 2016 http://nvs.landcareresearch.co.nz
Mobilising Vegetation Plot Data: the National Vegetation Survey Databank Susan Wiser April 2016 http://nvs.landcareresearch.co.nz Nationally Significant Databases and Collections http://natsigdc.landcareresearch.co.nz/natsigdc_list.html
Agriculture, Fisheries and Conservation Department Conservation Branch. Nature Conservation Practice Note No. 02 Revised: June 2006
Agriculture, Fisheries and Conservation Department Conservation Branch Nature Conservation Practice Note No. 02 Revised: June 2006 Ref: AF GR CON 21/2 Measurement of Diameter at Breast Height (DBH) 1.
LiDAR for vegetation applications
LiDAR for vegetation applications UoL MSc Remote Sensing Dr Lewis plewis@geog.ucl.ac.uk Introduction Introduction to LiDAR RS for vegetation Review instruments and observational concepts Discuss applications
NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
Applications of R Software in Bayesian Data Analysis
Article International Journal of Information Science and System, 2012, 1(1): 7-23 International Journal of Information Science and System Journal homepage: www.modernscientificpress.com/journals/ijinfosci.aspx
business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
Confidence Intervals for Cp
Chapter 296 Confidence Intervals for Cp Introduction This routine calculates the sample size needed to obtain a specified width of a Cp confidence interval at a stated confidence level. Cp is a process
Forecasting and Planning a Multi-Skilled Workforce: What You Need To Know
Welcome Forecasting and Planning a Multi-Skilled Workforce: What You Need To Know Presented by: Skills Scheduling What is it? Scheduling that takes into account the fact that employees may have one or
Threats to tropical forests
Threats to tropical forests and what we can do about it Ryszard Laskowski Institute of Environmental Sciences Jagiellonian University www.eko.uj.edu.pl/laskowski 1/22 Human activities: dangers and hopes
The importance of graphing the data: Anscombe s regression examples
The importance of graphing the data: Anscombe s regression examples Bruce Weaver Northern Health Research Conference Nipissing University, North Bay May 30-31, 2008 B. Weaver, NHRC 2008 1 The Objective
CS 147: Computer Systems Performance Analysis
CS 147: Computer Systems Performance Analysis One-Factor Experiments CS 147: Computer Systems Performance Analysis One-Factor Experiments 1 / 42 Overview Introduction Overview Overview Introduction Finding
Section 13, Part 1 ANOVA. Analysis Of Variance
Section 13, Part 1 ANOVA Analysis Of Variance Course Overview So far in this course we ve covered: Descriptive statistics Summary statistics Tables and Graphs Probability Probability Rules Probability
Chapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
Name: Date: Use the following to answer questions 3-4:
Name: Date: 1. Determine whether each of the following statements is true or false. A) The margin of error for a 95% confidence interval for the mean increases as the sample size increases. B) The margin
Maria W. Merrill Florida Fish and Wildlife Conservation Commission Aquatic Habitat Conservation and Restoration Section June 8, 2012
Maria W. Merrill Florida Fish and Wildlife Conservation Commission Aquatic Habitat Conservation and Restoration Section June 8, 2012 Who are we? Aquatic Habitat Restoration & Enhancement Sub- Section (AHRES)
Statistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
An Interactive Tool for Residual Diagnostics for Fitting Spatial Dependencies (with Implementation in R)
DSC 2003 Working Papers (Draft Versions) http://www.ci.tuwien.ac.at/conferences/dsc-2003/ An Interactive Tool for Residual Diagnostics for Fitting Spatial Dependencies (with Implementation in R) Ernst