# Robust procedures for Canadian Test Day Model final report for the Holstein breed

Save this PDF as:

Size: px
Start display at page:

Download "Robust procedures for Canadian Test Day Model final report for the Holstein breed"

## Transcription

1 Robust procedures for Canadian Test Day Model final report for the Holstein breed J. Jamrozik, J. Fatehi and L.R. Schaeffer Centre for Genetic Improvement of Livestock, University of Guelph Introduction Objective of this research was to apply the robust estimation procedures to the Canadian Test Day Model (CTDM) and to the Holstein breed in particular. Following recommendations from our previous report (Jamrozik et al., 2006), the robust method with k=2.75 was selected to be tested on the Holstein data. Material and Methods Data: November 2006 genetic evaluation run (CDN) data for the Holstein breed was used, with: 42,605,959 test-day (TD) records, 3,641,329 herd test-day classes, 2,706,031 cows (with data), 3,727,746 animals in pedigree, 23 phantom parent groups, and 190 classes for the effect of region-age-season of calving. Model: The model included multiple lactations (the first three parities) and multiple traits (milk, fat, protein and SCS) and was the same as the routine genetic evaluation model used by CDN (Schaeffer et al., 2000). Methods: The robust estimation method was as in Yang et al. (2004) with k=2.75 adapted for the multiple trait model. The process was as follows in each round of iteration: 1. Calculate residuals for all observations within each of DIM, r i = y i x i b - z i a w i p and the variance of residuals, s 2 j, for the each DIM 2. Modify y i based on the value of the residuals and the DIM class as follows y * i = y i if r i < ks m, y * i = x i b + z i a + w i p - ks m if r i < - ks m, y * i = x i b + z i a + w i p + ks m if r i > ks m. Mixed model equations were also solved (for comparison purposes) with the regular BLUP method. Estimation procedures were compared overall by: sum of squared residuals, sum of absolute residuals, average and standard deviation of residuals. Separate statistics were calculated for each trait and lactation and for each trait (lactations combined). Residuals were defined either as calculated for the original observation (y i ) or the observation used in the iteration process (y * i ). 1

2 Outliers for the robust method were defined as observations that were corrected during the iteration. They were quantified by: number and proportion of outliers, average, standard deviation, and minimal and maximal values of changes. Outliers were also characterized by number of outliers (1, 2, 3 or 4) for a cow on a given test-day. Distributions of outliers for cows and sires of cows by trait and parity were also calculated. Estimated breeding values for the first two regression coefficients (total yield in lactation and persistency of lactation for a given trait) were summarized by trait and parity for all animals. Correlations between the BLUP and the robust method were estimated for all animals and the first two regression coefficients, by trait and parity. Combined breeding values (first three lactations) were estimated using the official CDN weights for respective traits. Estimates of the intercept of the genetic lactation curve were used for the expression of the total yield and the average SCS in lactation. Milk lactation persistency was approximated by the linear coefficient of animal genetic lactation curve for milk yield. The lactation weights were: 0.33, 0.33, 0.33 for milk, fat and protein yields; 0.25, 0.65, 0.10 for SCS; and 0.50, 0.25, 0.25 for milk lactation persistency. Neither base correction nor scaling of lactation EBV to the same variation level was performed on animal genetic solutions of the mixed model equation. Top bulls (cows) in common, between BLUP and the robust analysis were inspected in relation to number (proportion) of their outlier records. Results Approximately 1600 rounds of iteration were needed to solve MME for both methods, with convergence criterion equal to 2.7e-7. Tables 1 and 2 present sums of squared residuals (SSR) and sums of absolute residuals (SAR) for different estimation procedures, by trait, for corrected and original records, respectively. When corrected records were used for calculating residuals, the robust method performed better than the regular BLUP in terms of both SSR and SAR. Slightly different patterns for SSR and SAR were observed when original observations were used for calculating residuals of the model. The BLUP method gave the lowest SSR for all traits. Table 3 shows sum of squared residuals (SSR), sum of absolute residuals (SAR), average (MEAN) and standard deviation (SD) of residuals for protein yield, by estimation method and parity. Residuals were calculated using the corrected observations in the iteration process. Similar statistic for residuals defined for original observations are in Table 4. Within lactation SSR and SAR statistics followed described earlier trends for traits overall (Tables 1 and 2), for both definitions of residuals. No evident differences in average residuals and their SD were noticed for different methods. Residuals for original observations, however, had the smallest means and variation for the BLUP method compared with the robust procedure. The same patterns were found for within lactation fat, protein and SCS (results not shown). Table 5 gives number (N) and proportion (%) of corrected records, and average (MEAN), standard deviation (SD), minimal (MIN) and maximal (MAX) values of corrections for protein yield from the robust method, by parity. Proportion of corrected records (= outliers) was from 2 to 3%. Average values of corrections were close to zero. Similar observations were made for the remaining traits (results not shown). 2

3 Table 6 contains number (N) and proportion (relative to the total number of outlier records in a given lactation, in %) of corrections (1, 2, 3 or 4) for a cow on a given TD for the robust procedure in the first parity. Majority of outliers occurred for just a single trait. Proportion of single trait outliers for milk yield in the first lactation was equal to 7%. Estimates for fat (15%) and protein (5%) yields were smaller than for SCS (23%). SCS exhibited the largest number of single outliers compared with the remaining traits. Later lactations gave similar numbers of single trait outliers. Between-trait trends for later parities were the same as those for the first lactation. Two outliers (out of possible four observations) were detected for approximately 14% of records in a given lactation. Outliers for all four traits consisted only a marginal proportion of all records (not more than 1%). Distributions of outliers by DIM (all four traits combined) were in general uniform in the interval from 10 to 305 DIM within each lactation. Average proportion of outlier records in this interval was about 2.7%. The beginning of lactation (DIM from 5 to 10) was characterized by a slightly larger proportion of detected outliers. Proportion of outliers on DIM 5 ranged from 5% (first lactation) to 7% (third lactation). Distributions of outliers for protein yield resulting from the robust method, by trait and parity, are given in Tables 7 and 8 for cows with records and sires of cows, respectively. Most cows for which outliers were detected had a single corrected record. Proportion of cows with more than four records for all traits was equal to zero. Similar observations could be made for distributions of outliers for sires with daughters. Proportions of affected sires, however, were larger than the respective statistics for cows with records. No less than 53% of all sires had at least one outlier. Distributions for milk, fat and SCS (results not shown) followed in general the trends observed for protein yield. Table 9 shows average (MEAN EBV), standard deviation (SD EBV), minimal (MIN EBV) and maximal (MAX EBV) values of estimated breeding values for protein yield lactation curve intercept for all animals (N=3,722,746) for protein yield, by parity. Average estimated breeding values and their SD were practically the same for both methods. Slightly larger difference between distributions of EBV from different estimation procedures could be noticed for linear coefficient of lactation curve (results not shown). Fat, protein and SCS followed in general the behaviour of milk yield distributions (results not shown). Correlations (x1000) between estimated breeding values from BLUP and the robust procedure for the lactation curve intercept and the linear term (all animals, N=3,722,746), by trait and parity, are in Table 10. All correlations were larger than 0.99 indicating that the rankings of animals would be very similar between methods. Table 11 give number of bulls and cows in common in the top 100 lists between BLUP and the robust analysis for combined yields, by trait. Rankings of top cows were more affected by the estimation procedures than rankings for sires. Differences reflected the overall pattern of correlation coefficients between EBV: larger discrepancies between top lists for cows than for bulls, more differences for SCS and lactation persistency compared with milk, fat and protein yields. List of the top 10 sires from the BLUP method contrasted with the respective evaluations from the robust estimation method for combined protein yield is shown in Table 12. Corresponding top 10 cow results for combined protein yield are presented in Table 13. 3

4 Characteristics of sires that dropped from the BLUP top 100 list for combined protein yield by using the robust estimation method are in Table 14. Table 15 describes cows with data that dropped from the BLUP 100 top list for combined protein yield by using the robust estimation. Proportions of outlier observations were in the same range as those reported for the top animals. No apparent association between the magnitude of changes in EBV and the occurrence of outliers for the selected animals could therefore be established. Discussion Two ways of calculating residuals in the model were applied in this study. The first one used the values of corrected observations while the other used original observations when calculating residuals. The robust procedure was clearly superior (sum of squared residuals and sum of absolute residuals) over the BLUP method when corrected observations were used for residuals. Model comparisons that used original observations for calculating residuals followed in general single trait model results of Yang et al. (2004) and our previous CTDM results for the Jersey breed (Jamrozik et al., 2006). The robust method gave smaller sum of absolute residuals compared with the BLUP model, overall and for all traits and lactations analysed individually. Outliers were defined in this study in an arbitrary way using residuals calculated for each DIM and the coefficient k. Outliers were therefore method dependant and they did not necessarily correspond to the usual definition (perception) of outlier observations. Distributions of outliers by cows with data did not exhibit any evident trends. The same observations were made for sires of cows with records. On a given TD, outliers were more likely associated with one trait only. SCS was the trait that provided the largest proportions of outliers compared with other traits. The model might not be able to handle elevated SCS observation in an optimal way. Similar arguments might apply to the explanation why proportion of outliers was larger at the very beginning of lactation. This period of lactation could be associated with erratic or problematic values of milk recording. Again, inability of the model to account properly for all sources of variation in this part of lactation could be partially responsible for this phenomenon. More than one outlier on a given TD occurred in smaller proportions compared to single outliers. Two or more outlier observations were usually associated with yield traits (milk, fat or protein). Larger environmental correlations between these traits could have been the reason for correlated outliers. Robust estimation methods had in general little effect on estimated breeding values of animals. Rankings for different methods, as indicated by correlation coefficient, did not differ much in comparison with the regular BLUP evaluations. This is in agreement with the results of Yang et al. (2004) for the single trait model and our previous CTDM results for the Jersey breed (Jamrozik et al., 2006). Some bulls and cows changed their position on the list of superior animals. This could not be explained, however, by number or proportion of outlier observations for these animals. Traits differed slightly in their performance by the robust method. Total yields were less affected than persistency; SCS was subject to more changes compared with BLUP than milk, fat or protein yields. 4

5 Conclusions Application of the robust procedure for genetic evaluation of Canadian Holsteins in CTDM for production traits gave the same overall results as observed earlier for the Jersey breed. The robust method would reduce the influence of outlier observations in the model and improve the model performance in general. Differences in rankings for animals, however, would be small compared with the regular BLUP method. References Jamrozik, J. J. Fatehi, L.R. Schaeffer Robust procedures for Canadian Test Day Model. Research Report to the GEB, September 2006, pp. 21. Schaeffer, L.R., J. Jamrozik, G.J. Kistemaker, B.J. Van Doormall Experience with a test-day model. J. Dairy Sci. 83: Yang, R., L.R. Schaeffer, J. Jamrozik Robust estimation of breeding values in a random regression test-day model. J. Anim. Breed. Genet. 121:

6 Table 1: Sum of squared residuals 1 (SSR) and sum of absolute residuals (SAR) for different estimation procedures, by trait; residuals were defined for corrected observations Trait Number Method SSR SAR of records Milk 42,605,959 BLUP 186,009, ,835,216 63,643,616 60,632,072 Fat 42,301,201 BLUP Protein 42,302,907 BLUP SCS 38,406,234 BLUP 1 Residual = y * - E(y) 569, , , ,690 22,593,166 17,674,522 3,411,699 3,266,542 2,016,746 1,926,642 20,765,974 19,433,730 Table 2: Sum of squared residuals 1 (SSR) and sum of absolute residuals (SAR) for different estimation procedures, by trait; residuals were defined for original observations Trait Number Method SSR SAR of records Milk 42,605,959 BLUP 186,009, ,708,368 63,643,616 63,451,420 Fat 42,301,201 BLUP Protein 42,302,907 BLUP SCS 38,406,234 BLUP 1 Residual = y E(y) 569, , , ,094 22,593,166 24,014,146 3,411,699 3,406,423 2,016,746 2,009,682 20,765,974 20,569,862 6

7 Table 3: Sum of squared residuals 1 (SSR), sum of absolute residuals (SAR), average (MEAN) and standard deviation (SD) of residuals for protein yield, by estimation method and parity; residuals were defined for corrected observations Parity Number Method SSR SAR MEAN SD of records 1 20,035,249 BLUP 69,232 57, , , ,315,412 BLUP 65,085 54, , , ,952,246 BLUP 49,459 41, , , Residual = y * - E(y) Table 4: Sum of squared residuals 1 (SSR), sum of absolute residuals (SAR), average (MEAN) and standard deviation (SD) of residuals for protein yield, by estimation method and parity; residuals were defined for original observations Parity Number Method SSR SAR MEAN SD of records 1 20,035,249 BLUP 69,232 73, , , ,315,412 BLUP 65,085 68, , , ,952,246 BLUP 49,459 52, , , Residual = y - E(y) Table 5: Number (N) and proportion (%) of corrected records for the robust procedure, average (MEAN), standard deviation (SD), minimal (MIN) and maximal (MAX) values of corrections for protein yield, by parity Parity Corrected records MEAN SD MIN MAX N % 1 499, , ,

8 Table 6: Number (N) and proportion (%) of corrected records (1,2,3 or 4) from the robust procedure for a cow on a given test-day for the first parity Corrected Corrections records N % 1 1,058, , , <1 Table 7: Distribution of cows with corrected records from the robust procedure for protein yield, by parity Parity Cows Corrected records (%) with records >0 >1 >4 >6 >8 1 2,631, ,765, ,200, Table 8: Distribution of sires with corrected records from the robust procedure for protein yield, by parity Parity Sires Corrected records (%) with records >0 >1 >10 >100 > , , ,

9 Table 9: Average (MEAN EBV), standard deviation (SD EBV), minimal (MIN EBV) and maximal (MAX EBV) values of estimated breeding values for all animals (N=3,722,746) for the intercept of lactation curve for protein yield, by estimation method and parity Parity Method MEAN SD MIN MAX 1 BLUP EBV EBV EBV EBV BLUP BLUP Table 10: Correlations (x1000) between estimated breeding values from BLUP and the robust procedure, for the lactation curve intercept (a 0 ) and the lactation curve linear term (a 1 ) (all animals, N=3,722,746), by trait and parity Trait Parity Correlation a 0 a 1 Milk Fat Protein SCS Table 11: Number of bulls (cows) in common in the top 100 lists between BLUP and the robust analysis for combined yields, by trait Trait Bulls Cows Milk Fat Protein SCS Milk Persistency

10 Table 12: Top 10 sires from the BLUP method for combined protein yield in comparison with the ranking from the robust estimation method Sire ID BLUP Outliers EBV Rank EBV Rank N % HOCANM HOCANM HOUSAM HOCANM HOCANM HOCANM HOCANM HOCANM HOUSAM HONLDM Table 13: Top 10 cows from the BLUP method for combined protein yield in comparison with the ranking from the robust estimation method Cow ID BLUP Outliers EBV Rank EBV Rank N % HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF Table 14: Characteristics of sires that dropped from the BLUP 100 top list for combined protein yield by using the robust estimation method Sire ID BLUP Outliers EBV Rank EBV Rank N % HONLDM HOCANM HOUSAM HOCANM

11 Table 15: Characteristics of cows that dropped from the BLUP 100 top list for combined protein yield by using the robust estimation method Cow ID BLUP Outliers EBV Rank EBV Rank N % HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF HOUSAF HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF HOCANF

### Scope for the Use of Pregnancy Confirmation Data in Genetic Evaluation for Reproductive Performance

Scope for the Use of Pregnancy Confirmation Data in Genetic Evaluation for Reproductive Performance J. Jamrozik and G.J. Kistemaker Canadian Dairy Network The data on cow's pregnancy diagnostics has been

### Genetic Evaluation of Dairy Cattle in Canada

Genetic Evaluation of Dairy Cattle in Canada Responsibility The calculation and publication of all dairy cattle genetic evaluations in Canada is the responsibility of Canadian Dairy Network (CDN). An 8-member

### Abbreviation key: NS = natural service breeding system, AI = artificial insemination, BV = breeding value, RBV = relative breeding value

Archiva Zootechnica 11:2, 29-34, 2008 29 Comparison between breeding values for milk production and reproduction of bulls of Holstein breed in artificial insemination and bulls in natural service J. 1,

### Evaluations for service-sire conception rate for heifer and cow inseminations with conventional and sexed semen

J. Dairy Sci. 94 :6135 6142 doi: 10.3168/jds.2010-3875 American Dairy Science Association, 2011. Evaluations for service-sire conception rate for heifer and cow inseminations with conventional and sexed

### COMPARISON OF DIFFERENT PROCEDURES FOR LACTATION LENGTH ADJUSTMENT OF MILK YIELD IN SAHIWAL CATTLE

117 COMPARISON OF DIFFERENT PROCEDURES FOR LACTATION LENGTH ADJUSTMENT OF MILK YIELD IN SAHIWAL CATTLE I. R. Bajwa, M. S. Khan, M. A. Khan 1 and K. Z. Gondal 2 Department of Animal Breeding and Genetics,

### Genetic improvement: a major component of increased dairy farm profitability

Genetic improvement: a major component of increased dairy farm profitability Filippo Miglior 1,2, Jacques Chesnais 3 & Brian Van Doormaal 2 1 2 Canadian Dairy Network 3 Semex Alliance Agri-Food Canada

### Longevity of Holstein Cows Bred to be Large versus Small for Body Size

Longevity of Holstein Cows Bred to be Large versus Small for Body Size L. B. Hansen, J. B. Cole, G. D. Marx and A. J. Seykora Department of Animal Science, University of Minnesota, St. Paul 55108 USA E-mail:

### Genomics: how well does it work?

Is genomics working? Genomics: how well does it work? Jacques Chesnais and Nicolas Caron, Semex Alliance The only way to find out is to do some validations Two types of validation - Backward validation

### NAV routine genetic evaluation of Dairy Cattle

NAV routine genetic evaluation of Dairy Cattle data and genetic models NAV December 2013 Second edition 1 Genetic evaluation within NAV Introduction... 6 NTM - Nordic Total Merit... 7 Traits included in

### Guelph, Ontario, Canada INTRODUCTION

The Effect of Pregnancy on Milk, Fat and Protein Yields of Canadian Ayrshire, Jersey, Brown Swiss and Guernsey breeds S. Loker 1, F. Miglior,3, J. Bohmanova 1, L. R. Schaeffer 1 and J. Jamrozik 1 1 CGIL,

### 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

### Genomic Selection in. Applied Training Workshop, Sterling. Hans Daetwyler, The Roslin Institute and R(D)SVS

Genomic Selection in Dairy Cattle AQUAGENOME Applied Training Workshop, Sterling Hans Daetwyler, The Roslin Institute and R(D)SVS Dairy introduction Overview Traditional breeding Genomic selection Advantages

### Crossbreeding results in Canadian dairy cattle for production, reproduction, and conformation.

Crossbreeding results in Canadian dairy cattle for production, reproduction, and conformation. Lawrence R. Schaeffer, Edward B Burnside, Paige Glover, Jalal Fatehi Centre for Genetic Improvement of Livestock,

### Individual piglet birth weight

Individual piglet birth weight Horst Brandt Institute for Animal Breeding and Genetics, University of Göttingen, Albrecht-Thaer-Weg 3, 37075 Göttingen, Germany Introduction Beside the litter size at birth

### Final Exam Practice Problem Answers

Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal

### Exploratory data analysis (Chapter 2) Fall 2011

Exploratory data analysis (Chapter 2) Fall 2011 Data Examples Example 1: Survey Data 1 Data collected from a Stat 371 class in Fall 2005 2 They answered questions about their: gender, major, year in school,

### Presentation by: Ahmad Alsahaf. Research collaborator at the Hydroinformatics lab - Politecnico di Milano MSc in Automation and Control Engineering

Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen 9-October 2015 Presentation by: Ahmad Alsahaf Research collaborator at the Hydroinformatics lab - Politecnico di

### INTRODUCTION. The identification system of dairy cattle; The recording of production of dairy cattle; Laboratory analysis; Data processing.

POLISH FEDERATION OF CATTLE BREEDERS AND DAIRY FARMERS INTRODUCTION Polish Federation of Cattle Breeders and Dairy Farmers was established in 1995 as a merger of 20 regional breeding organizations from

### Comparative Study of Artificial Insemination and Natural Service Cost Effectiveness in Dairy Cattle

Comparative Study of Artificial Insemination and Natural Service Cost Effectiveness in Dairy Cattle Valergakis G.E., Banos G., Arsenos G. Department of Animal Production, School of Veterinary Medicine,

### Bayesian Methods. 1 The Joint Posterior Distribution

Bayesian Methods Every variable in a linear model is a random variable derived from a distribution function. A fixed factor becomes a random variable with possibly a uniform distribution going from a lower

### EDUCATION AND PRODUCTION. A Model for Persistency of Egg Production 1

EDUCATION AND PRODUCTION A Model for Persistency of Egg Production 1 M. Grossman,*,,2 T. N. Gossman,* and W. J. Koops*, *Department of Animal Sciences, University of Illinois, Urbana, Illinois 61801; Department

### Factors Impacting Dairy Profitability: An Analysis of Kansas Farm Management Association Dairy Enterprise Data

www.agmanager.info Factors Impacting Dairy Profitability: An Analysis of Kansas Farm Management Association Dairy Enterprise Data August 2011 (available at www.agmanager.info) Kevin Dhuyvetter, (785) 532-3527,

### UNIFORM DATA COLLECTION PROCEDURES

UNIFORM DATA COLLECTION PROCEDURES PURPOSE: The purpose of these procedures is to provide the framework for a uniform, accurate record system that will increase dairy farmers' net profit. The uniform records

### Crossbreeding Dairy Cattle

The Babcock Institute University of Wisconsin Dairy Updates Crossbreeding Dairy Cattle Reproduction and Genetics No. 610 Author: Daniel Z. Caraviello 1 Crossbreeding 1 The primary goal of dairy cattle

### Univariate Regression

Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is

### Example: Boats and Manatees

Figure 9-6 Example: Boats and Manatees Slide 1 Given the sample data in Table 9-1, find the value of the linear correlation coefficient r, then refer to Table A-6 to determine whether there is a significant

### Chapter 8. Linear Regression. Copyright 2012, 2008, 2005 Pearson Education, Inc.

Chapter 8 Linear Regression Copyright 2012, 2008, 2005 Pearson Education, Inc. Fat Versus Protein: An Example The following is a scatterplot of total fat versus protein for 30 items on the Burger King

### Linear and Piecewise Linear Regressions

Tarigan Statistical Consulting & Coaching statistical-coaching.ch Doctoral Program in Computer Science of the Universities of Fribourg, Geneva, Lausanne, Neuchâtel, Bern and the EPFL Hands-on Data Analysis

### 5. Multiple regression

5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful

### The impact of genomic selection on North American dairy cattle breeding organizations

The impact of genomic selection on North American dairy cattle breeding organizations Jacques Chesnais, George Wiggans and Filippo Miglior The Semex Alliance, USDA and Canadian Dairy Network 2000 09 Genomic

### Statistics for Management II-STAT 362-Final Review

Statistics for Management II-STAT 362-Final Review Multiple Choice Identify the letter of the choice that best completes the statement or answers the question. 1. The ability of an interval estimate to

### where b is the slope of the line and a is the intercept i.e. where the line cuts the y axis.

Least Squares Introduction We have mentioned that one should not always conclude that because two variables are correlated that one variable is causing the other to behave a certain way. However, sometimes

### MISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group

MISSING DATA TECHNIQUES WITH SAS IDRE Statistical Consulting Group ROAD MAP FOR TODAY To discuss: 1. Commonly used techniques for handling missing data, focusing on multiple imputation 2. Issues that could

### Multiple Linear Regression in Data Mining

Multiple Linear Regression in Data Mining Contents 2.1. A Review of Multiple Linear Regression 2.2. Illustration of the Regression Process 2.3. Subset Selection in Linear Regression 1 2 Chap. 2 Multiple

### Local outlier detection in data forensics: data mining approach to flag unusual schools

Local outlier detection in data forensics: data mining approach to flag unusual schools Mayuko Simon Data Recognition Corporation Paper presented at the 2012 Conference on Statistical Detection of Potential

### Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle

Genet. Sel. Evol. 35 (2003) 457 468 457 INRA, EDP Sciences, 2003 DOI: 10.1051/gse:2003034 Original article Longitudinal random effects models for genetic analysis of binary data with application to mastitis

### Statistics. Measurement. Scales of Measurement 7/18/2012

Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does

### 5. Linear Regression

5. Linear Regression Outline.................................................................... 2 Simple linear regression 3 Linear model............................................................. 4

### Least Squares Estimation

Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David

### Phenotypic Factor Analysis for Linear Type Traits in Beijing Holstein Cows**

1527 Phenotypic Factor Analysis for Linear Type Traits in Beijing Holstein Cows** M. X. Chu* and S. K. Shi 1 Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100094, P. R.

### , then the form of the model is given by: which comprises a deterministic component involving the three regression coefficients (

Multiple regression Introduction Multiple regression is a logical extension of the principles of simple linear regression to situations in which there are several predictor variables. For instance if we

### Regression Analysis: Basic Concepts

The simple linear model Regression Analysis: Basic Concepts Allin Cottrell Represents the dependent variable, y i, as a linear function of one independent variable, x i, subject to a random disturbance

### BREEDING VALUE ESTIMATION ON SOME SELECTION TRAITS OF PERFORMANCE PRODUCTIVITY OF SMALL PIG POPULATIONS FROM THE DANUBE WHITE BREEDS

276 Bulgarian Journal of Agricultural Science, 15 (No 3) 2009, 276-280 Agricultural Academy BREEDING VALUE ESTIMATION ON SOME SELECTION TRAITS OF PERFORMANCE PRODUCTIVITY OF SMALL PIG POPULATIONS FROM

### 2. Linear regression with multiple regressors

2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measures-of-fit in multiple regression Assumptions

### Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation

Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation B. J. Van Doormaal and G. J. Kistemaker Canadian Dairy Network / Réseau laitier canadien, 150 Research

### Dairy Cattle Background Information

Dairy Cattle Background Information Dairying is another major Australian rural industry in which production significantly exceeds domestic requirements and Australia has emerged as one of the world s major

### Simple Linear Regression Chapter 11

Simple Linear Regression Chapter 11 Rationale Frequently decision-making situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related

### Exercise 1.12 (Pg. 22-23)

Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.

### International Strain Trial. Trial confirms superior attributes of New Zealand Holstein genetics

International Strain Trial Trial confirms superior attributes of New Zealand Holstein genetics Strain Trial Summary Introduction Livestock Improvement as a company aims to constantly improve its products

### The general form of the PROC GLM statement is

Linear Regression Analysis using PROC GLM Regression analysis is a statistical method of obtaining an equation that represents a linear relationship between two variables (simple linear regression), or

### 5-30. (25 min.) Methods of Estimating Costs High-Low: Adriana Corporation. a. High-low estimate

5-30. (25 min.) Methods of Estimating Costs High-Low: Adriana Corporation. a. High-low estimate Machine- Hours Overhead Costs Highest activity (month 12)... 8,020 \$564,210 Lowest activity (month 11)...

### Stats Review Chapters 3-4

Stats Review Chapters 3-4 Created by Teri Johnson Math Coordinator, Mary Stangler Center for Academic Success Examples are taken from Statistics 4 E by Michael Sullivan, III And the corresponding Test

### Genetic correlations among body condition score, somatic cell score, milk production, fertility and conformation traits in dairy cows

Animal Science 2004, 79: 191-201 1357-7298/04/40230191\$20 00 2004 British Society of Animal Science Genetic correlations among body condition score, somatic cell score, milk production, fertility and conformation

### How To Read An Official Holstein Pedigree

GETTING THE MOST FOR YOUR INVESTMENT How To Read An Official Holstein Pedigree Holstein Association USA, Inc. 1 Holstein Place, PO Box 808 Brattleboro, VT 05302-0808 800.952.5200 www.holsteinusa.com 7

### Estimated genetic parameters for growth traits of German shepherd dog and Labrador retriever dog guides 1

Estimated genetic parameters for growth traits of German shepherd dog and Labrador retriever dog guides 1 S. K. Helmink*, S. L. Rodriguez-Zas*, R. D. Shanks*,, and E. A. Leighton *Department of Animal

### The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy

BMI Paper The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy Faculty of Sciences VU University Amsterdam De Boelelaan 1081 1081 HV Amsterdam Netherlands Author: R.D.R.

### e = random error, assumed to be normally distributed with mean 0 and standard deviation σ

1 Linear Regression 1.1 Simple Linear Regression Model The linear regression model is applied if we want to model a numeric response variable and its dependency on at least one numeric factor variable.

### Non-Additive Animal Models

Non-Additive Animal Models 1 Non-Additive Genetic Effects Non-additive genetic effects (or epistatic effects) are the interactions among loci in the genome. There are many possible degrees of interaction

### Basic Statistics and Data Analysis for Health Researchers from Foreign Countries

Basic Statistics and Data Analysis for Health Researchers from Foreign Countries Volkert Siersma siersma@sund.ku.dk The Research Unit for General Practice in Copenhagen Dias 1 Content Quantifying association

### Review of Key Concepts: 1.2 Characteristics of Polynomial Functions

Review of Key Concepts: 1.2 Characteristics of Polynomial Functions Polynomial functions of the same degree have similar characteristics The degree and leading coefficient of the equation of the polynomial

### CORRELATION AND SIMPLE REGRESSION ANALYSIS USING SAS IN DAIRY SCIENCE

CORRELATION AND SIMPLE REGRESSION ANALYSIS USING SAS IN DAIRY SCIENCE A. K. Gupta, Vipul Sharma and M. Manoj NDRI, Karnal-132001 When analyzing farm records, simple descriptive statistics can reveal a

### 1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number

1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x - x) B. x 3 x C. 3x - x D. x - 3x 2) Write the following as an algebraic expression

### Practical application of daughter yield deviations in dairy cattle breeding

J Appl Genet 49(2), 2008, pp. 183 191 Original article Practical application of daughter yield deviations in dairy cattle breeding Joanna Szyda 1,2, Ewa Ptak 3, Jolanta Komisarek 4, Andrzej arnecki 5 1

### LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016

UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe

### 2. Simple Linear Regression

Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according

### Modeling Extended Lactations of Dairy Cows

Modeling Extended Lactations of Dairy Cows B. Vargas,*, W. J. Koops, M. Herrero,, and J.A.M. Van Arendonk *Escuela de Medicina Veterinaria, Universidad Nacional de Costa Rica, PO Box 304-3000, Heredia,

### Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

### Simple linear regression

Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between

### MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Module 7 Test Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. You are given information about a straight line. Use two points to graph the equation.

: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

### The aspect of the data that we want to describe/measure is the degree of linear relationship between and The statistic r describes/measures the degree

PS 511: Advanced Statistics for Psychological and Behavioral Research 1 Both examine linear (straight line) relationships Correlation works with a pair of scores One score on each of two variables ( and

### Applying Statistics Recommended by Regulatory Documents

Applying Statistics Recommended by Regulatory Documents Steven Walfish President, Statistical Outsourcing Services steven@statisticaloutsourcingservices.com 301-325 325-31293129 About the Speaker Mr. Steven

### Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear.

Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. In the main dialog box, input the dependent variable and several predictors.

### Australian Santa Gertrudis Selection Indexes

Australian Santa Gertrudis Selection Indexes There are currently two different selection indexes calculated for Australian Santa Gertrudis animals. These are: Domestic Production Index Export Production

### Chapter 10 Correlation and Regression. Overview. Section 10-2 Correlation Key Concept. Definition. Definition. Exploring the Data

Chapter 10 Correlation and Regression 10-1 Overview 10-2 Correlation 10- Regression Overview This chapter introduces important methods for making inferences about a correlation (or relationship) between

### Four Systematic Breeding Programs with Timed Artificial Insemination for Lactating Dairy Cows: A Revisit

Four Systematic Breeding Programs with Timed Artificial Insemination for Lactating Dairy Cows: A Revisit Amin Ahmadzadeh Animal and Veterinary Science Department University of Idaho Why Should We Consider

### Multiple Linear Regression

Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is

### Chapter Additional: Standard Deviation and Chi- Square

Chapter Additional: Standard Deviation and Chi- Square Chapter Outline: 6.4 Confidence Intervals for the Standard Deviation 7.5 Hypothesis testing for Standard Deviation Section 6.4 Objectives Interpret

### Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

### Genetic parameters for linear type traits in Czech Holstein cattle

Czech J. Anim. Sci., 56, 2011 (4): 157 162 Original Paper Genetic parameters for linear type traits in Czech Holstein cattle E. Němcová, M. Štípková, L. Zavadilová Institute of Animal Science Praha Uhříněves,

### Iowa State College Agricultural Experiment Station

INTERRELATIONS OF MILK PRODUCTION AND BREEDING EFFICIENCY IN DAIRY COWS 1 G. M. CARMAN 2 Iowa State College Agricultural Experiment Station HE many studies conducted on the genetic aspects of breeding

### The All-Breed Animal Model Bennet Cassell, Extension Dairy Scientist, Genetics and Management

publication 404-086 The All-Breed Animal Model Bennet Cassell, Extension Dairy Scientist, Genetics and Management Introduction The all-breed animal model is the genetic-evaluation system used to evaluate

### Integrated IT Solutions in-between Farm Management Systems and Global Holstein Breeding Dr. Stefan Rensing

Integrated IT Solutions in-between Farm Management Systems and Global Holstein Breeding vit Verden, Germany email: stefan.rensing@vit.de, web: http://www.vit.de Abstract An integrated shared data base

### Breeding. Chromosomes

Breeding Domesticated 10,000 12,000 years ago Major changes have been genetic (to benefit man) Increased production can be achieved through environment but must be repeated daily, seasonally or at least

### Simple Regression Theory II 2010 Samuel L. Baker

SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the

### Dr. G van der Veen (BVSc) Technical manager: Ruminants gerjan.vanderveen@zoetis.com

Dr. G van der Veen (BVSc) Technical manager: Ruminants gerjan.vanderveen@zoetis.com GENETICS NUTRITION MANAGEMENT Improved productivity and quality GENETICS Breeding programs are: Optimize genetic progress

### Descriptive Statistics

Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

### Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model

Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written

### SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.

Practice for Chapter 9 and 10 The acutal exam differs. SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Find the number of successes x suggested by the

### Linear Regression. Chapter 5. Prediction via Regression Line Number of new birds and Percent returning. Least Squares

Linear Regression Chapter 5 Regression Objective: To quantify the linear relationship between an explanatory variable (x) and response variable (y). We can then predict the average response for all subjects

### Strategies for introducing new traits in routine genetic evaluations for dairy cattle in Germany: Health traits in the focus of R&D

IT-Solutions for Animal Production 26 February 2015, Verden / Germany Strategies for introducing new traits in routine genetic evaluations for dairy cattle in Germany: Health traits in the focus of R&D

### MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Open book and note Calculator OK Multiple Choice 1 point each MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find the mean for the given sample data.

### Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a

### " Y. Notation and Equations for Regression Lecture 11/4. Notation:

Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through

### Analytical Methods: A Statistical Perspective on the ICH Q2A and Q2B Guidelines for Validation of Analytical Methods

Page 1 of 6 Analytical Methods: A Statistical Perspective on the ICH Q2A and Q2B Guidelines for Validation of Analytical Methods Dec 1, 2006 By: Steven Walfish BioPharm International ABSTRACT Vagueness

### E205 Final: Version B

Name: Class: Date: E205 Final: Version B Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The owner of a local nightclub has recently surveyed a random

### Genetic parameters for female fertility and milk production traits in first-parity Czech Holstein cows

Genetic parameters for female fertility and milk production traits in first-parity Czech Holstein cows V. Zink 1, J. Lassen 2, M. Štípková 1 1 Institute of Animal Science, Prague-Uhříněves, Czech Republic

### CHARACTERIZATION OF BOXED BEEF VALUE IN ANGUS FIELD DATA. Authors:

CHARACTERIZATION OF BOXED BEEF VALUE IN ANGUS FIELD DATA 1999 Animal Science Research Report Authors: Story in Brief Pages 32-40 B.R. Schutte, S.L. Dolezal, H.G. Dolezal and D.S. Buchanan The OSU Boxed