Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

Size: px
Start display at page:

Download "Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation"

Transcription

1 Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation Kendrick Boyd 1 Vítor Santos Costa 2 Jesse Davis 3 David Page 1 1 University of Wisconsin Madison 2 University of Porto, Portugal 3 KU Leuven, Belgium June 28, 2012 Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

2 Introduction 1 Unachievable Region 0.8 Precision Recall Unachievable Region Precision-recall curves cannot go through the unachievable region. Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

3 Introduction 1 Unachievable Region 1 Unachievable Region Precision Precision Recall Recall Unachievable region varies with the data set. Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

4 Introduction 1 Unachievable Region 1 Unachievable Region Precision Precision Recall Recall Unachievable region varies with the data set. Our Goal Understand and characterize the unachievable region. Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

5 Precision-Recall Analysis TP Precision = TP + FP TP Recall = TP + FN Actual P N P TP FP N FN TN pos neg Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

6 Precision-Recall Analysis TP Precision = TP + FP TP Recall = TP + FN Scores used in PR analysis Precision-recall (PR) curves Area under PR curve (AUCPR) F β Mean average precision Precision Actual P N P TP FP N FN TN pos neg Recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

7 Known PR Space Properties Precision has high variance at low recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

8 Known PR Space Properties Precision has high variance at low recall Can translate between PR curves and ROC curves 1 PR curve 1 ROC curve Precision True Positive Rate Recall False Positive Rate Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

9 Known PR Space Properties Precision has high variance at low recall Can translate between PR curves and ROC curves Class Skew Proportion of positive examples in a data set. Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

10 Known PR Space Properties Precision has high variance at low recall Can translate between PR curves and ROC curves ROC curves - independent of class skew PR curves - sensitive to class skew Class Skew Proportion of positive examples in a data set. Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

11 Outline 1 Introduction 2 Unachievable Points 3 Unachievable Region 4 Discussion Downsampling Aggregation F1 Score 5 Conclusion Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

12 Illustration of an Unachievable Point positive examples 200 negative examples Precision Hold precision constant at Recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

13 Illustration of an Unachievable Point positive examples 200 negative examples Precision Hold precision constant at Actual P N P N recall Recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

14 Illustration of an Unachievable Point positive examples 200 negative examples Precision Hold precision constant at Recall Actual P N P N recall Actual P N P N recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

15 Illustration of an Unachievable Point positive examples 200 negative examples Precision Hold precision constant at Recall Actual P N P N recall Actual P N P N recall Actual P N P N recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

16 Unachievable Points in PR Space Theorem Precision (p) and recall (r) must satisfy where π = pos pos+neg p is the class skew. πr 1 π + πr Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

17 Outline 1 Introduction 2 Unachievable Points 3 Unachievable Region 4 Discussion Downsampling Aggregation F1 Score 5 Conclusion Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

18 Unachievable Region Precision Recall Example PR curves with π = Unachievable Region Sample PR Curve Random Guessing Minimum PR Curve Random Guessing: randomly assigned labels according to class skew Minimum PR Curve: worst possible ranking Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

19 Minimum AUCPR Theorem The area of the unachievable region in PR space and the minimum AUCPR, for class skew π, is AUCPR MIN = 1 + (1 π) ln(1 π) π Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

20 Minimum AUCPR Theorem The area of the unachievable region in PR space and the minimum AUCPR, for class skew π, is (1 π) ln(1 π) AUCPR MIN = 1 + π AUCPR MIN π Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

21 Outline 1 Introduction 2 Unachievable Points 3 Unachievable Region 4 Discussion Downsampling Aggregation F1 Score 5 Conclusion Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

22 AUCPR Comparisons Unachievable region always included for free in AUCPR Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

23 AUCPR Comparisons Unachievable region always included for free in AUCPR Range of AUCPR scores is [AUCPR MIN, 1] and thus depends on skew Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

24 AUCPR Comparisons Unachievable region always included for free in AUCPR Range of AUCPR scores is [AUCPR MIN, 1] and thus depends on skew AUCPR not comparable from different skews Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

25 AUCNPR Normalized area under precision-recall curve AUCNPR = AUCPR AUCPR MIN 1 AUCPR MIN Pros Range of AUCNPR is [0, 1] regardless of skew With same skew, preserves ordering of AUCPR Cons No good interpretation as an area in PR space AUCNPR of random guessing is not simple Still sensitive to skew Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

26 Downsampling Often downsample positives and negatives differently Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

27 Downsampling Often downsample positives and negatives differently Changes unachievable region for PR analysis Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

28 Downsampling Often downsample positives and negatives differently Changes unachievable region for PR analysis Cohort Study Should preserve the true skew Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

29 Downsampling Often downsample positives and negatives differently Changes unachievable region for PR analysis Cohort Study Should preserve the true skew Case-control Study Artificially makes the ratio of negatives to positives 1:1, 2:1, etc. Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

30 Downsampling on Mammography Cohort Study Cohort DATA SET Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

31 Downsampling on Mammography Cohort Study Case-control Study Cohort Controls Cases DATA SET DATA SET Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

32 Downsampling on Mammography Original SAYU Radiologists Downsampled Negatives SAYU Radiologists Precision Precision Recall Recall 120:1 ratio (π = 0.008) 1:1 ratio (π = 0.5) AUCPR = AUCPR = Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

33 Downsampling on Mammography Original SAYU Radiologists Downsampled Negatives SAYU Radiologists Precision Precision Recall Recall 120:1 ratio (π = 0.008) 1:1 ratio (π = 0.5) AUCPR = AUCPR = AUCPR MIN = AUCPR MIN = AUCNPR = AUCNPR = Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

34 Aggregating Results Sometimes want to combine results from problems with different skews Cross-validation folds Multiple tasks Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

35 Aggregating Results Sometimes want to combine results from problems with different skews Cross-validation folds Multiple tasks AUCNPR is a step in the right direction AUCNPR range is [0,1] for each fold/task Mean AUCNPR gives mean fraction of achievable area obtained But more work is needed! Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

36 F1 Score and Unachievable Region F1=0.8 F1 Contours Precision F1=0.6 F1 = 2pr p+r Recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

37 F1 Score and Unachievable Region A F1=0.8 Unachievable Region Minimum PR Curve F1 Contours Precision B F1=0.6 F1 = 2pr p+r Recall Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

38 Outline 1 Introduction 2 Unachievable Points 3 Unachievable Region 4 Discussion Downsampling Aggregation F1 Score 5 Conclusion Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

39 Conclusions Awareness of the unachievable region is critical for precision-recall analysis Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

40 Conclusions Awareness of the unachievable region is critical for precision-recall analysis Shown how to compute this region from just the class skew Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

41 Conclusions Awareness of the unachievable region is critical for precision-recall analysis Shown how to compute this region from just the class skew Recommendation: Always show unachievable region in figures Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

42 Conclusions Awareness of the unachievable region is critical for precision-recall analysis Shown how to compute this region from just the class skew Recommendation: Always show unachievable region in figures AUCNPR: a first step towards scores that account for the unachievable region Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

43 Acknowledgments CIBM Training Program (NIH 5T15LM007359) NIGMS grant R01GM NLM grant R01LM NIEHS grant 4R01ES Elizabeth S. Burnside Jude Shavlik ERDF through the Programme COMPETE Portuguese Government through FCT Project HORUS ref. PTDC/EIA-EIA/100897/2008 European Union Seventh Framework Programme FP7/ Research Fund K.U. Leuven (CREA/11/015 and OT/11/051) EU FP7 Marie Curie Farerr Integration Grant (#294068) FWO-Vlaanderen (G ) UW Carbone Cancer Center Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

44 Thank you Questions? Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

45 Modifying F1 Score A few desirable properties for a modified F1 score, f, f (r, p) = 0 if p = f (r 1, p) < f (r 2, p) iff r 1 < r 2 f (r, p 1 ) < f (r, p 2 ) iff p 1 < p 2 rπ 1 π + rπ Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

46 Modifying F1 Score A few desirable properties for a modified F1 score, f, f (r, p) = 0 if p = f (r 1, p) < f (r 2, p) iff r 1 < r 2 f (r, p 1 ) < f (r, p 2 ) iff p 1 < p 2 rπ 1 π + rπ Impossible! 0 = f (0, 0) < f (0, π) < f (1, π) = 0 Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

47 Modifying F1 Score Relaxed properties for a modified F1 score, f, f (r, p) = 0 if p = f (r 1, p) f (r 2, p) iff r 1 r 2 f (r, p 1 ) f (r, p 2 ) iff p 1 p 2 rπ 1 π + rπ Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

48 Modifying F1 Score Relaxed properties for a modified F1 score, f, f (r, p) = 0 if p = f (r 1, p) f (r 2, p) iff r 1 r 2 f (r, p 1 ) f (r, p 2 ) iff p 1 p 2 rπ 1 π + rπ One possible f f (r, p) = { 0 if p π 2(p π)r p π+(1 π)r if p > π Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

49 Proof of Unachievable Points Theorem Theorem Precision (p) and recall (r) must satisfy where π = pos pos+neg p πr 1 π + πr is the proportion of positive examples. TP p = TP + FP TP TP + (1 π)n rπn = rπn + (1 π)n rπ = rπ + (1 π) Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

50 Proof of Minimum AUCPR Theorem Theorem The area of the unachievable region in PR space and the minimum AUCPR, for proportion of positives π, is AUCPR MIN = 1 + (1 π) ln(1 π) π AUCPR MIN = = 1 0 rπ 1 π + rπ dr rπ + (π 1) ln(π(r 1) + 1) π r=1 r=0 = 1 (π + (π 1)(ln(1) ln(1 π))) π (1 π) ln(1 π) = 1 + π Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

51 Proof of Minimum AP Theorem Theorem The minimum average precision, for pos and neg positive and negative examples, respectively, is AP MIN = 1 pos pos i=1 i i + neg AP MIN = 1 pos = 1 pos = 1 pos pos i=1 pos i=1 pos i=1 πi pos 1 π + πi pos posi (pos+neg)pos 1 + pos pos+neg ( i pos 1) i pos+neg i+neg pos+neg = 1 pos pos i=1 i i + neg Boyd et al. (ICML 2012) Unachievable Region in PR Space June 28,

DESIGN AND ANALYSIS OF ALGORITHMS (DAA 2017)

DESIGN AND ANALYSIS OF ALGORITHMS (DAA 2017) DESIGN AND ANALYSIS OF ALGORITHMS (DAA 2017) Veli Mäkinen 12/05/2017 1 COURSE STRUCTURE 7 weeks: video lecture -> demo lecture -> study group -> exercise Video lecture: Overview, main concepts, algorithm

More information

Entropy-based benchmarking methods

Entropy-based benchmarking methods Entropy-based benchmarking methods Umed Temurshoev Faculty of Economics and Business University of Groningen, PO Box 800 9700 AV Groningen, The Netherlands This project is funded by the European Commission,

More information

THE 2018 ROSENTHAL PRIZE for Innovation in Math Teaching. Geometry Project: DARTBOARD

THE 2018 ROSENTHAL PRIZE for Innovation in Math Teaching. Geometry Project: DARTBOARD THE 2018 ROSENTHAL PRIZE for Innovation in Math Teaching Geometry Project: DARTBOARD Geometric Probability Theoretical Probability and Experimental Probability Elizabeth Masslich Geometry grades 6-12 Table

More information

A computer program that improves its performance at some task through experience.

A computer program that improves its performance at some task through experience. 1 A computer program that improves its performance at some task through experience. 2 Example: Learn to Diagnose Patients T: Diagnose tumors from images P: Percent of patients correctly diagnosed E: Pre

More information

CONTROL VALVE WHAT YOU NEED TO LEARN?

CONTROL VALVE WHAT YOU NEED TO LEARN? CONTROL VALVE WHAT YOU NEED TO LEARN? i) The control valve characteristics refers to the relationship between the volumetric flowrate F (Y-axis) through the valve AND the valve travel or opening position

More information

Chapter 20. Planning Accelerated Life Tests. William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University

Chapter 20. Planning Accelerated Life Tests. William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University Chapter 20 Planning Accelerated Life Tests William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University Copyright 1998-2008 W. Q. Meeker and L. A. Escobar. Based on the authors

More information

Chapter 2: Modeling Distributions of Data

Chapter 2: Modeling Distributions of Data Chapter 2: Modeling Distributions of Data Section 2.1 The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE Chapter 2 Modeling Distributions of Data 2.1 2.2 Normal Distributions Section

More information

Student Outcomes. Lesson Notes. Classwork. Discussion (20 minutes)

Student Outcomes. Lesson Notes. Classwork. Discussion (20 minutes) Student Outcomes Students explain a proof of the converse of the Pythagorean Theorem. Students apply the theorem and its converse to solve problems. Lesson Notes Students had their first experience with

More information

STANDARD SCORES AND THE NORMAL DISTRIBUTION

STANDARD SCORES AND THE NORMAL DISTRIBUTION STANDARD SCORES AND THE NORMAL DISTRIBUTION REVIEW 1.MEASURES OF CENTRAL TENDENCY A.MEAN B.MEDIAN C.MODE 2.MEASURES OF DISPERSIONS OR VARIABILITY A.RANGE B.DEVIATION FROM THE MEAN C.VARIANCE D.STANDARD

More information

Note that all proportions are between 0 and 1. at risk. How to construct a sentence describing a. proportion:

Note that all proportions are between 0 and 1. at risk. How to construct a sentence describing a. proportion: Biostatistics and Research Design in Dentistry Categorical Data Reading assignment Chapter 3 Summarizing data in Dawson-Trapp starting with Summarizing nominal and ordinal data with numbers on p 40 thru

More information

Concepts/Skills. Computation Ratios Problem solving. Materials

Concepts/Skills. Computation Ratios Problem solving. Materials . Overview Proportional Flag Concepts/Skills Computation Ratios Problem solving Materials TI-15 Student activity pages (pp. 82-85) Toy truck or car with scale indicated Color tiles Paper and pencils Chart

More information

MATH 118 Chapter 5 Sample Exam By: Maan Omran

MATH 118 Chapter 5 Sample Exam By: Maan Omran MATH 118 Chapter 5 Sample Exam By: Maan Omran Problem 1-4 refer to the following table: X P Product a 0.2 d 0 0.1 e 1 b 0.4 2 c? 5 0.2? E(X) = 1.7 1. The value of a in the above table is [A] 0.1 [B] 0.2

More information

At each type of conflict location, the risk is affected by certain parameters:

At each type of conflict location, the risk is affected by certain parameters: TN001 April 2016 The separated cycleway options tool (SCOT) was developed to partially address some of the gaps identified in Stage 1 of the Cycling Network Guidance project relating to separated cycleways.

More information

Bank Profitability and Macroeconomic Factors

Bank Profitability and Macroeconomic Factors Bank Profitability and Macroeconomic Factors Ricardo Martinho Banco de Portugal João Gouveia de Oliveira Banco de Portugal & Nova SBE Vitor Oliveira Banco de Portugal 17 October 2017 Lisbon Conference

More information

Unit 4: Inference for numerical variables Lecture 3: ANOVA

Unit 4: Inference for numerical variables Lecture 3: ANOVA Unit 4: Inference for numerical variables Lecture 3: ANOVA Statistics 101 Thomas Leininger June 10, 2013 Announcements Announcements Proposals due tomorrow. Will be returned to you by Wednesday. You MUST

More information

TOPIC 10: BASIC PROBABILITY AND THE HOT HAND

TOPIC 10: BASIC PROBABILITY AND THE HOT HAND TOPIC 0: BASIC PROBABILITY AND THE HOT HAND The Hot Hand Debate Let s start with a basic question, much debated in sports circles: Does the Hot Hand really exist? A number of studies on this topic can

More information

AMERICAN ACADEMY OF ACTUARIES MEDICARE SUPPLEMENT WORK GROUP SUBTEAM REPORT ON LOSS RATIO CURVES FOR REDETERMINATION OF REFUND BENCHMARKS

AMERICAN ACADEMY OF ACTUARIES MEDICARE SUPPLEMENT WORK GROUP SUBTEAM REPORT ON LOSS RATIO CURVES FOR REDETERMINATION OF REFUND BENCHMARKS AMERICAN ACADEMY OF ACTUARIES MEDICARE SUPPLEMENT WORK GROUP SUBTEAM REPORT ON LOSS RATIO CURVES FOR REDETERMINATION OF REFUND BENCHMARKS March 10, 2004 The American Academy of Actuaries is the public

More information

Using an Adaptive Thresholding Algorithm to Detect CA1 Hippocampal Sharp Wave Ripples. Jay Patel. Michigan State University

Using an Adaptive Thresholding Algorithm to Detect CA1 Hippocampal Sharp Wave Ripples. Jay Patel. Michigan State University Using an Adaptive Thresholding Algorithm to Detect CA1 Hippocampal Sharp Wave Ripples Jay Patel Michigan State University Department of Physics and Astronomy, University of California, Los Angeles 2013

More information

Software Reliability 1

Software Reliability 1 Software Reliability 1 Software Reliability What is software reliability? the probability of failure-free software operation for a specified period of time in a specified environment input sw output We

More information

An Application of Signal Detection Theory for Understanding Driver Behavior at Highway-Rail Grade Crossings

An Application of Signal Detection Theory for Understanding Driver Behavior at Highway-Rail Grade Crossings An Application of Signal Detection Theory for Understanding Driver Behavior at Highway-Rail Grade Crossings Michelle Yeh and Jordan Multer United States Department of Transportation Volpe National Transportation

More information

Balanced Binary Trees

Balanced Binary Trees CSE : AVL TREES 2 Balanced Binary Trees Recall: Worst case for find in a BST is : Worst case for find in a Balanced BST is: 3 Balanced Binary Trees Deterministic Balancing Change insert and delete operations

More information

Numerical simulation of radial compressor stages with seals and technological holes

Numerical simulation of radial compressor stages with seals and technological holes EPJ Web of Conferences 67, 02115 (2014) DOI: 10.1051/ epjconf/ 20146702115 C Owned by the authors, published by EDP Sciences, 2014 Numerical simulation of radial compressor stages with seals and technological

More information

The pth percentile of a distribution is the value with p percent of the observations less than it.

The pth percentile of a distribution is the value with p percent of the observations less than it. Describing Location in a Distribution (2.1) Measuring Position: Percentiles One way to describe the location of a value in a distribution is to tell what percent of observations are less than it. De#inition:

More information

Mathematical Ranking of the Division III Track and Field Conferences Chris Fisette

Mathematical Ranking of the Division III Track and Field Conferences Chris Fisette Mathematical Ranking of the Division III Track and Field Conferences Chris Fisette Abstract. In this article we provide a mathematical model for ranking of the National Collegiate Athletic Association

More information

Using Markov Chains to Analyze a Volleyball Rally

Using Markov Chains to Analyze a Volleyball Rally 1 Introduction Using Markov Chains to Analyze a Volleyball Rally Spencer Best Carthage College sbest@carthage.edu November 3, 212 Abstract We examine a volleyball rally between two volleyball teams. Using

More information

Concussion: where are we now?

Concussion: where are we now? Exeter Sports Medicine conference 2018 Concussion: where are we now? Dr Simon Kemp Medical Services Director, Rugby Football Union @drsimonkemp simonkemp@rfu.com Disclosures of interest Medical Services

More information

Chapter 12 Practice Test

Chapter 12 Practice Test Chapter 12 Practice Test 1. Which of the following is not one of the conditions that must be satisfied in order to perform inference about the slope of a least-squares regression line? (a) For each value

More information

(c) The hospital decided to collect the data from the first 50 patients admitted on July 4, 2010.

(c) The hospital decided to collect the data from the first 50 patients admitted on July 4, 2010. Math 155, Test 1, 18 October 2011 Name: Instructions. This is a closed-book test. You may use a calculator (but not a cell phone). Make sure all cell-phones are put away and that the ringer is off. Show

More information

Planning and Acting in Partially Observable Stochastic Domains

Planning and Acting in Partially Observable Stochastic Domains Planning and Acting in Partially Observable Stochastic Domains Leslie Pack Kaelbling and Michael L. Littman and Anthony R. Cassandra (1998). Planning and Acting in Partially Observable Stochastic Domains,

More information

This file is part of the following reference:

This file is part of the following reference: This file is part of the following reference: Hancock, Timothy Peter (2006) Multivariate consensus trees: tree-based clustering and profiling for mixed data types. PhD thesis, James Cook University. Access

More information

Robust specification testing in regression: the FRESET test and autocorrelated disturbances

Robust specification testing in regression: the FRESET test and autocorrelated disturbances Robust specification testing in regression: the FRESET test and autocorrelated disturbances Linda F. DeBenedictis and David E. A. Giles * Policy and Research Division, Ministry of Human Resources, 614

More information

Which On-Base Percentage Shows. the Highest True Ability of a. Baseball Player?

Which On-Base Percentage Shows. the Highest True Ability of a. Baseball Player? Which On-Base Percentage Shows the Highest True Ability of a Baseball Player? January 31, 2018 Abstract This paper looks at the true on-base ability of a baseball player given their on-base percentage.

More information

Inverting a Batting Average - an Application of Continued Fractions (Preliminary Version)

Inverting a Batting Average - an Application of Continued Fractions (Preliminary Version) Inverting a Batting Average - an Application of Continued Fractions (Preliminary Version) Allen Back August 30, 2000 Contents Introduction and Motivation 2 Continued Fractions 2 2. Comparison of Two Numbers

More information

/435 Artificial Intelligence Fall 2015

/435 Artificial Intelligence Fall 2015 Final Exam 600.335/435 Artificial Intelligence Fall 2015 Name: Section (335/435): Instructions Please be sure to write both your name and section in the space above! Some questions will be exclusive to

More information

MICROSIMULATION USING FOR CAPACITY ANALYSIS OF ROUNDABOUTS IN REAL CONDITIONS

MICROSIMULATION USING FOR CAPACITY ANALYSIS OF ROUNDABOUTS IN REAL CONDITIONS Session 5. Transport and Logistics System Modelling Proceedings of the 11 th International Conference Reliability and Statistics in Transportation and Communication (RelStat 11), 19 22 October 2011, Riga,

More information

Reliability. Introduction, 163 Quantifying Reliability, 163. Finding the Probability of Functioning When Activated, 163

Reliability. Introduction, 163 Quantifying Reliability, 163. Finding the Probability of Functioning When Activated, 163 ste41912_ch04_123-175 3:16:06 01.29pm Page 163 SUPPLEMENT TO CHAPTER 4 Reliability LEARNING OBJECTIVES SUPPLEMENT OUTLINE After completing this supplement, you should be able to: 1 Define reliability.

More information

Opleiding Informatica

Opleiding Informatica Opleiding Informatica Determining Good Tactics for a Football Game using Raw Positional Data Davey Verhoef Supervisors: Arno Knobbe Rens Meerhoff BACHELOR THESIS Leiden Institute of Advanced Computer Science

More information

Navigate to the golf data folder and make it your working directory. Load the data by typing

Navigate to the golf data folder and make it your working directory. Load the data by typing Golf Analysis 1.1 Introduction In a round, golfers have a number of choices to make. For a particular shot, is it better to use the longest club available to try to reach the green, or would it be better

More information

The University of Hong Kong Department of Physics Experimental Physics Laboratory

The University of Hong Kong Department of Physics Experimental Physics Laboratory The University of Hong Kong Department of Physics Experimental Physics Laboratory PHYS2260 Heat and Waves 2260-1 LABORATORY MANUAL Experiment 1: Adiabatic Gas Law Part A. Ideal Gas Law Equipment Required:

More information

Applying Generalized Pareto Curves to Inequality Analysis

Applying Generalized Pareto Curves to Inequality Analysis Applying Generalized Pareto Curves to Inequality Analysis Thomas Blanchet 1 Bertrand Garbinti 2 Jonathan Goupille-Lebret 3 Clara Martínez-Toledano 1 ASSA Conference, January 2018 1 Paris School of Economics

More information

Public Procurement Indicators 2014

Public Procurement Indicators 2014 Ref. Ares(216)78649-1/2/216 Public Procurement Indicators 214 DG GROW G4 - Innovative and e-procurement February 2, 216 1 Summary of main facts This document provides various indicators describing the

More information

Analysis of Variance. Copyright 2014 Pearson Education, Inc.

Analysis of Variance. Copyright 2014 Pearson Education, Inc. Analysis of Variance 12-1 Learning Outcomes Outcome 1. Understand the basic logic of analysis of variance. Outcome 2. Perform a hypothesis test for a single-factor design using analysis of variance manually

More information

A river runs through it

A river runs through it A river runs through it How Culverts Disrupt Salmonid Habitat Connectivity in Rivers Normand Bergeron, INRS-Eau Terre et Environnement WHAT WORKS? A WORKSHOP ON WILD ATLANTIC SALMON RECOVERY PROGRAM SEPTEMBER

More information

5.1A Introduction, The Idea of Probability, Myths about Randomness

5.1A Introduction, The Idea of Probability, Myths about Randomness 5.1A Introduction, The Idea of Probability, Myths about Randomness The Idea of Probability Chance behavior is unpredictable in the short run, but has a regular and predictable pattern in the long run.

More information

ASSESSING THE RELIABILITY OF FAIL-SAFE STRUCTURES INTRODUCTION

ASSESSING THE RELIABILITY OF FAIL-SAFE STRUCTURES INTRODUCTION ASSESSING THE RELIABILITY OF FAIL-SAFE STRUCTURES Abraham Brot * Abstract: A computer simulation method is described, that can be used to assess the reliability of a dual-path fail-safe design. The method

More information

2 Asymptotic speed deficit from boundary layer considerations

2 Asymptotic speed deficit from boundary layer considerations EWEC Techn.track Wake, paper ID 65 WAKE DECAY CONSTANT FOR THE INFINITE WIND TURBINE ARRAY Application of asymptotic speed deficit concept to existing engineering wake model. Ole Steen Rathmann, Risø-DTU

More information

Lotto Winner for La Primitiva Lottery 1/10. /two-dimensional-lotto-winner-apps/lotto-winner-for-la-primitiva-lottery/

Lotto Winner for La Primitiva Lottery 1/10.  /two-dimensional-lotto-winner-apps/lotto-winner-for-la-primitiva-lottery/ La Primitiva two-dimensional lotto app for lottery winner Mylotto-App.com :: Powerball,Powerball winning numbers,powerball numbers,mega Millions,Mega Millions numbers,euromillions,national lottery,lottery,lotto,texas

More information

LOW PRESSURE EFFUSION OF GASES adapted by Luke Hanley and Mike Trenary

LOW PRESSURE EFFUSION OF GASES adapted by Luke Hanley and Mike Trenary ADH 1/7/014 LOW PRESSURE EFFUSION OF GASES adapted by Luke Hanley and Mike Trenary This experiment will introduce you to the kinetic properties of low-pressure gases. You will make observations on the

More information

A SEMI-PRESSURE-DRIVEN APPROACH TO RELIABILITY ASSESSMENT OF WATER DISTRIBUTION NETWORKS

A SEMI-PRESSURE-DRIVEN APPROACH TO RELIABILITY ASSESSMENT OF WATER DISTRIBUTION NETWORKS A SEMI-PRESSURE-DRIVEN APPROACH TO RELIABILITY ASSESSMENT OF WATER DISTRIBUTION NETWORKS S. S. OZGER PhD Student, Dept. of Civil and Envir. Engrg., Arizona State Univ., 85287, Tempe, AZ, US Phone: +1-480-965-3589

More information

Advanced Search Hill climbing

Advanced Search Hill climbing Advanced Search Hill climbing Yingyu Liang yliang@cs.wisc.edu Computer Sciences Department University of Wisconsin, Madison [Based on slides from Jerry Zhu, Andrew Moore http://www.cs.cmu.edu/~awm/tutorials

More information

Chemistry 12 Notes on Graphs Involving LeChatelier s Principle

Chemistry 12 Notes on Graphs Involving LeChatelier s Principle Chemistry 12 Notes on Graphs Involving LeChatelier s Principle 1. Temperature Changes When a system adjusts due to a temperature change, there are no sudden changes in concentration of any species, so

More information

Money Lost or Won -$5 +$3 +$7

Money Lost or Won -$5 +$3 +$7 Math 137 Unit 7 Review 1. The numbers of endangered species for several groups are listed here. Location Mammals Birds Reptiles Amphibians Total USA 63 78 14 10 Foreign 251 175 64 8 Total If one endangered

More information

IMPORTANT BRIEFING NOTE Oireachtas Rince na Cruinne 2019

IMPORTANT BRIEFING NOTE Oireachtas Rince na Cruinne 2019 IMPORTANT BRIEFING NOTE Oireachtas Rince na Cruinne 2019 As you are aware, changes as to how Oireachtas Rince na Cruinne is run will be implemented from 2019 onwards to ensure that the Championships can

More information

The Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD

The Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD The Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD Outline Definition. Deriving the Estimates. Properties of the Estimates. Units of Measurement and Functional Form. Expected

More information

An Analysis of Reducing Pedestrian-Walking-Speed Impacts on Intersection Traffic MOEs

An Analysis of Reducing Pedestrian-Walking-Speed Impacts on Intersection Traffic MOEs An Analysis of Reducing Pedestrian-Walking-Speed Impacts on Intersection Traffic MOEs A Thesis Proposal By XIAOHAN LI Submitted to the Office of Graduate Studies of Texas A&M University In partial fulfillment

More information

DESCRIBE the effect of adding, subtracting, multiplying by, or dividing by a constant on the shape, center, and spread of a distribution of data.

DESCRIBE the effect of adding, subtracting, multiplying by, or dividing by a constant on the shape, center, and spread of a distribution of data. Today's Objectives: FIND and INTERPRET the standardized score (z-score) of an individual value within a distribution of data. DESCRIBE the effect of adding, subtracting, multiplying by, or dividing by

More information

Ch.5 Reliability System Modeling.

Ch.5 Reliability System Modeling. Certified Reliability Engineer. Ch.5 Reliability System Modeling. Industrial Engineering & Management System Research Center. - 1 - Reliability Data. [CRE Primer Ⅵ 2-6] Sources of Reliability Data. Successful

More information

Deciding When to Quit: Reference-Dependence over Slot Machine Outcomes

Deciding When to Quit: Reference-Dependence over Slot Machine Outcomes Deciding When to Quit: Reference-Dependence over Slot Machine Outcomes By JAIMIE W. LIEN * AND JIE ZHENG * Lien: Department of Economics, School of Economics and Management, Tsinghua University, Beijing,

More information

PROVISION OF SERVICES IN RELATION TO AN INVESTIGATION ON THE COASTAL SUBSURFACE GROUNDWATER DISCHARGE FLOW DYNAMICS

PROVISION OF SERVICES IN RELATION TO AN INVESTIGATION ON THE COASTAL SUBSURFACE GROUNDWATER DISCHARGE FLOW DYNAMICS Closing Date: Tuesday, 7 th August 2012 at 10:00am Quotation Reference: MRA/133/2012 Call for Quotations Issued on 31 st July 2012 PROVISION OF SERVICES IN RELATION TO AN INVESTIGATION ON THE COASTAL SUBSURFACE

More information

Performance of Fully Automated 3D Cracking Survey with Pixel Accuracy based on Deep Learning

Performance of Fully Automated 3D Cracking Survey with Pixel Accuracy based on Deep Learning Performance of Fully Automated 3D Cracking Survey with Pixel Accuracy based on Deep Learning Kelvin C.P. Wang Oklahoma State University and WayLink Systems Corp. 2017-10-19, Copenhagen, Denmark European

More information

Engineering Practice on Ice Propeller Strength Assessment Based on IACS Polar Ice Rule URI3

Engineering Practice on Ice Propeller Strength Assessment Based on IACS Polar Ice Rule URI3 10th International Symposium on Practical Design of Ships and Other Floating Structures Houston, Texas, United States of America 2007 American Bureau of Shipping Engineering Practice on Ice Propeller Strength

More information

Name May 3, 2007 Math Probability and Statistics

Name May 3, 2007 Math Probability and Statistics Name May 3, 2007 Math 341 - Probability and Statistics Long Exam IV Instructions: Please include all relevant work to get full credit. Encircle your final answers. 1. An article in Professional Geographer

More information

Modeling Approaches to Increase the Efficiency of Clear-Point- Based Solubility Characterization

Modeling Approaches to Increase the Efficiency of Clear-Point- Based Solubility Characterization Modeling Approaches to Increase the Efficiency of Clear-Point- Based Solubility Characterization Paul Larsen, Dallin Whitaker Crop Protection Product Design & Process R&D OCTOBER 4, 2018 TECHNOBIS CRYSTALLI

More information

Novel empirical correlations for estimation of bubble point pressure, saturated viscosity and gas solubility of crude oils

Novel empirical correlations for estimation of bubble point pressure, saturated viscosity and gas solubility of crude oils 86 Pet.Sci.(29)6:86-9 DOI 1.17/s12182-9-16-x Novel empirical correlations for estimation of bubble point pressure, saturated viscosity and gas solubility of crude oils Ehsan Khamehchi 1, Fariborz Rashidi

More information

Minimum Mean-Square Error (MMSE) and Linear MMSE (LMMSE) Estimation

Minimum Mean-Square Error (MMSE) and Linear MMSE (LMMSE) Estimation Minimum Mean-Square Error (MMSE) and Linear MMSE (LMMSE) Estimation Outline: MMSE estimation, Linear MMSE (LMMSE) estimation, Geometric formulation of LMMSE estimation and orthogonality principle. Reading:

More information

Trig Functions Learning Outcomes. Solve problems about trig functions in right-angled triangles. Solve problems using Pythagoras theorem.

Trig Functions Learning Outcomes. Solve problems about trig functions in right-angled triangles. Solve problems using Pythagoras theorem. 1 Trig Functions Learning Outcomes Solve problems about trig functions in right-angled triangles. Solve problems using Pythagoras theorem. Opposite Adjacent 2 Use Trig Functions (Right-Angled Triangles)

More information

FUZZY LOGIC IN COMPUTER-AIDED BREAST CANCER DIAGNOSIS: ANALYSIS OF LOBULATION 1

FUZZY LOGIC IN COMPUTER-AIDED BREAST CANCER DIAGNOSIS: ANALYSIS OF LOBULATION 1 Published in: Artificial Intelligence in Medicine, 11, pp. 75-85, 1997. FUZZY LOGIC IN COMPUTER-AIDED BREAST CANCER DIAGNOSIS: ANALYSIS OF LOBULATION 1 Boris Kovalerchuk, Evangelos Triantaphyllou Department

More information

Section I: Multiple Choice Select the best answer for each problem.

Section I: Multiple Choice Select the best answer for each problem. Inference for Linear Regression Review Section I: Multiple Choice Select the best answer for each problem. 1. Which of the following is NOT one of the conditions that must be satisfied in order to perform

More information

Webinar: Impacts of Roadway and Traffic Characteristics on Air Pollution Risks for Bicyclists

Webinar: Impacts of Roadway and Traffic Characteristics on Air Pollution Risks for Bicyclists Portland State University PDXScholar TREC Webinar Series Transportation Research and Education Center (TREC) 4-23-2015 Webinar: Impacts of Roadway and Traffic Characteristics on Air Pollution Risks for

More information

VIROLOGY QUALITY ASSURANCE PROGRAM STATISTICAL CENTER

VIROLOGY QUALITY ASSURANCE PROGRAM STATISTICAL CENTER TO: CC: Members of the VQA Advisory Board (VQAAB) Bill Meyer Bob Coombs/Ming Chang Nicole Tobin Belinda Yen-Lieberman Joan Dragavon Urvi Parikh Jessica Fogel James Bremer Cheryl Jennings Carolyn Yanavich/Diane

More information

Equine Injury Database Update and Call for More Data

Equine Injury Database Update and Call for More Data Equine Injury Database Update and Call for More Data Dr. Tim Parkin Senior Lecturer Equine Clinical Sciences University of Glasgow How do we get more from the EID? Tim.Parkin@Glasgow.ac.uk Tim.Parkin@EquineData-Analytics.co.uk

More information

Calculation of Trail Usage from Counter Data

Calculation of Trail Usage from Counter Data 1. Introduction 1 Calculation of Trail Usage from Counter Data 1/17/17 Stephen Martin, Ph.D. Automatic counters are used on trails to measure how many people are using the trail. A fundamental question

More information

Bicycle Theft Detection

Bicycle Theft Detection Bicycle Theft Detection Dima Damen and David Hogg Computer Vision Group, School of Computing University of Leeds International Crime Science Conference British Library London 17th of July 2007 17/07/2007

More information

To Illuminate or Not to Illuminate: Roadway Lighting as It Affects Traffic Safety at Intersections

To Illuminate or Not to Illuminate: Roadway Lighting as It Affects Traffic Safety at Intersections To Illuminate or Not to Illuminate: Roadway Lighting as It Affects Traffic Safety at Intersections Mark Rea Lighting Research Center Rensselaer Polytechnic Institute Eric Donnell Dept. of Civil and Environmental

More information

THE SIGNIFICANCE OF HAZARD ENDPOINTS IN QUANTITATIVE RISK ANALYSIS

THE SIGNIFICANCE OF HAZARD ENDPOINTS IN QUANTITATIVE RISK ANALYSIS THE SIGNIFICANCE OF HAZARD ENDPOINTS IN QUANTITATIVE RISK ANALYSIS John B. Cornwell and Jeffrey D. Marx Presented At 6 Process Plant Safety Symposium Houston, Texas April -2, 6 Presented By Quest Consultants

More information

DEVELOPMENT OF TRAFFIC CALMING POLICY & WARRANT TASK 3 DELIVERABLE: TRAFFIC CALMING WARRANT

DEVELOPMENT OF TRAFFIC CALMING POLICY & WARRANT TASK 3 DELIVERABLE: TRAFFIC CALMING WARRANT FINAL TECHNICAL MEMORANDUM MAY 2011 DOCUMENT CONTROL Client: Project Name: Development of Traffic Calming Policy & Warrant Report Title: IBI Reference: 27794 Version: 5.0 Development of Traffic Calming

More information

CGMP KITS PROTOCOL ASSAY PRINCIPLE

CGMP KITS PROTOCOL ASSAY PRINCIPLE CGMP KITS PROTOCOL Part # 62GM2PEG & 62GM2PEH Test size#: 500 tests (62GM2PEG), 10,000 tests (62GM2PEH) - assay volume: 20 µl Revision: 02-Jan.2018 Store at: 2-8 C (62GM2PEG); 2-8 C (62GM2PEH) For research

More information

Name: Class: Date: (First Page) Name: Class: Date: (Subsequent Pages) 1. {Exercise 5.07}

Name: Class: Date: (First Page) Name: Class: Date: (Subsequent Pages) 1. {Exercise 5.07} Name: Class: Date: _ (First Page) Name: Class: Date: _ (Subsequent Pages) 1. {Exercise 5.07} The probability distribution for the random variable x follows. Excel File: data05 07.xls a. f(x) is greater

More information

Energy Output. Outline. Characterizing Wind Variability. Characterizing Wind Variability 3/7/2015. for Wind Power Management

Energy Output. Outline. Characterizing Wind Variability. Characterizing Wind Variability 3/7/2015. for Wind Power Management Energy Output for Wind Power Management Spring 215 Variability in wind Distribution plotting Mean power of the wind Betz' law Power density Power curves The power coefficient Calculator guide The power

More information

VIROLOGY QUALITY ASSURANCE PROGRAM STATISTICAL CENTER

VIROLOGY QUALITY ASSURANCE PROGRAM STATISTICAL CENTER TO: CC: Members of the VQA Advisory Board (VQAAB) Bill Meyer Bob Coombs/Ming Chang Nicole Tobin Belinda Yen-Lieberman Joan Dragavon Urvi Parikh Jessica Fogel James Bremer Cheryl Jennings Carolyn Yanavich/Diane

More information

Scaled vs. Original Socre Mean = 77 Median = 77.1

Scaled vs. Original Socre Mean = 77 Median = 77.1 Have out... - notebook - colors - calculator (phone calc will work fine) Tests back as you come in! vocab example Tests Scaled vs. Original Socre Mean = 77 Median = 77.1 + Δ vocab example 1 2.1 Describing

More information

Relative Dosimetry. Photons

Relative Dosimetry. Photons Relative Dosimetry Photons What you need to measure! Required Data (Photon) Central Axis Percent Depth Dose Tissue Maximum Ratio Scatter Maximum Ratio Output Factors S c & S cp! S p Beam profiles Wedge

More information

Dust explosion severity characteristics of Indonesian Sebuku coal in oxy-fuel atmospheres

Dust explosion severity characteristics of Indonesian Sebuku coal in oxy-fuel atmospheres Dust explosion severity characteristics of Indonesian Sebuku coal in oxy-fuel atmospheres Frederik Norman,1, Sepideh Hosseinzadeh 2, Eric Van den Bulck 2, Jan Berghmans 1, Filip Verplaetsen 1 1 Adinex

More information

The Use of ILI In Dent Assessments

The Use of ILI In Dent Assessments The Use of ILI In Dent Assessments Prepared for: 2014 CRUG Conference Date: 11 September 2014 Prepared by: Rhett Dotson, PE Contributions from Dr. Chris Alexander and Markus Ginten Contents Current Dent

More information

Title: 4-Way-Stop Wait-Time Prediction Group members (1): David Held

Title: 4-Way-Stop Wait-Time Prediction Group members (1): David Held Title: 4-Way-Stop Wait-Time Prediction Group members (1): David Held As part of my research in Sebastian Thrun's autonomous driving team, my goal is to predict the wait-time for a car at a 4-way intersection.

More information

LEARNING OBJECTIVES. Overview of Lesson. guided practice Teacher: anticipates, monitors, selects, sequences, and connects student work

LEARNING OBJECTIVES. Overview of Lesson. guided practice Teacher: anticipates, monitors, selects, sequences, and connects student work D Rate, Lesson 1, Conversions (r. 2018) RATE Conversions Common Core Standard N.Q.A.1 Use units as a way to understand problems and to guide the solution of multi-step problems; choose and interpret units

More information

Instrumental Variables

Instrumental Variables Instrumental Variables Prof. Jacob M. Montgomery Quantitative Political Methodology (L32 363) December 6, 2016 Lecture 26 (QPM 2016) IV Analysis December 6, 2016 1 / 27 Class business PS8 due Wednesday

More information

Discovering Special Triangles Learning Task

Discovering Special Triangles Learning Task The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still

More information

Modeling Traffic Patterns using Java

Modeling Traffic Patterns using Java The College at Brockport: State University of New York Digital Commons @Brockport Lesson Plans CMST Institute 5-2005 Modeling Traffic Patterns using Java Kim Meek The College at Brockport Follow this and

More information

CATCH-AT-SIZE AND AGE ANALYSIS FOR ATLANTIC SWORDFISH

CATCH-AT-SIZE AND AGE ANALYSIS FOR ATLANTIC SWORDFISH SCRS/2017/136 Collect. Vol. Sci. Pap. ICCAT, 74(3): 1258-1274 (2017) CATCH-AT-SIZE AND AGE ANALYSIS FOR ATLANTIC SWORDFISH Alex Hanke 1, Laurie Kell, Rui Coelho and Mauricio Ortiz SUMMARY Analyses of the

More information

ENGINEERING ANALYSIS OF THOROUGHBRED RACING. Rubin Boxer. Revelation Software

ENGINEERING ANALYSIS OF THOROUGHBRED RACING. Rubin Boxer. Revelation Software ENGINEERING ANALYSIS OF THOROUGHBRED RACING by Rubin Boxer Revelation Software P.O. Box 21344 Santa Barbara, CA 93121-1344 Phone: (805) 962-9045 www.revelationprofits.com Copyright 1997-2004 Revelation

More information

LOW PRESSURE EFFUSION OF GASES revised by Igor Bolotin 03/05/12

LOW PRESSURE EFFUSION OF GASES revised by Igor Bolotin 03/05/12 LOW PRESSURE EFFUSION OF GASES revised by Igor Bolotin 03/05/ This experiment will introduce you to the kinetic properties of low-pressure gases. You will make observations on the rates with which selected

More information

2 When Some or All Labels are Missing: The EM Algorithm

2 When Some or All Labels are Missing: The EM Algorithm CS769 Spring Advanced Natural Language Processing The EM Algorithm Lecturer: Xiaojin Zhu jerryzhu@cs.wisc.edu Given labeled examples (x, y ),..., (x l, y l ), one can build a classifier. If in addition

More information

Synoptic Meteorology I: Thermal Wind Applications

Synoptic Meteorology I: Thermal Wind Applications Synoptic Meteorology I: Thermal Wind Applications The thermal wind allows us to state that a geostrophic wind that turns counterclockwise (or backs) with increasing height represents cold air advection,

More information

MATH 227 CP 3 SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.

MATH 227 CP 3 SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. MATH 227 CP 3 SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Find the mean for the given sample data. Unless indicated otherwise, round your answer to

More information

Public Procurement Indicators 2015

Public Procurement Indicators 2015 Public Procurement Indicators 21 DG GROW G4 - Innovative and e-procurement December 19, 216 1 Summary of main facts This document provides various indicators describing the public procurement market in

More information

Warm-up. Make a bar graph to display these data. What additional information do you need to make a pie chart?

Warm-up. Make a bar graph to display these data. What additional information do you need to make a pie chart? Warm-up The number of deaths among persons aged 15 to 24 years in the United States in 1997 due to the seven leading causes of death for this age group were accidents, 12,958; homicide, 5,793; suicide,

More information

Analysis of the Article Entitled: Improved Cube Handling in Races: Insights with Isight

Analysis of the Article Entitled: Improved Cube Handling in Races: Insights with Isight Analysis of the Article Entitled: Improved Cube Handling in Races: Insights with Isight Michelin Chabot (michelinchabot@gmail.com) February 2015 Abstract The article entitled Improved Cube Handling in

More information

Modal Shift in the Boulder Valley 1990 to 2009

Modal Shift in the Boulder Valley 1990 to 2009 Modal Shift in the Boulder Valley 1990 to 2009 May 2010 Prepared for the City of Boulder by National Research Center, Inc. 3005 30th Street Boulder, CO 80301 (303) 444-7863 www.n-r-c.com Table of Contents

More information

With the product rule trick,, and noting that from the ideal gas law (R being the gas constant for air) we can substitute into [1] to obtain

With the product rule trick,, and noting that from the ideal gas law (R being the gas constant for air) we can substitute into [1] to obtain What is the thermodynamic chart? Begin with the 1 st law of thermodynamics: [1] Where is the heat supplied TdS where T is temperature and ds is the entropy change, is the heat capacity of air at constant

More information