Principles of Computer Science
|
|
- Roxanne Carpenter
- 6 years ago
- Views:
Transcription
1 Principles of Computer Science AVL Trees 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 1
2 Today s Topics Review Sorted Binary Tree Traversal Insert AVL Tree Sorted balanced binary tree Problems of insertion Cases of inbalance A solution 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 2
3 Sorted Binary Tree Binary Tree Each parent node has at most two children nodes. Sorted Binary Tree all left descendents are smaller than or equal to the node; and all right descendents are greater than the node. The ordering condition of binary trees 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 3
4 Sorted Binary Tree Sorted Binary Tree Not Sorted Binary Tree 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 4
5 Trees in Java class TreeNode<T> { public T content; public TreeNode<T> left; public TreeNode<T> right; } left root right class BinaryTree<T> { public TreeNode<T> root; } left right left right 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 5
6 Traversing through a binary tree public int sumofallnodes(treenode<integer> node) { int sum = 0; if(node == null) return 0; Base case } sum = node.content + sumofallnodes(node.left) + sumofallnodes(node.right); Recursion 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 6
7 Inserting a New Value into a Sorted Binary Tree Need to keep the ordering condition. Can only insert into leaf nodes Traverse through the tree for an insertion point 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 7
8 Inserting a New Value into a Sorted Binary Tree public void insert(t value, TreeNode<T> node) { if(node == null) return; // This is an error condition if(value <= node.content) { /* INSERT INTO THE LEFT SUBTREE */ if(node.left == null) node.createleftchild(value); else insert(value, node.left) } } if(value > node.content) { /* INSERT INTO THE RIGHT SUBTREE */ <... Symmetric treatment... > } 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 8
9 Problems with Insertion Insertion does not respect the balance of the binary tree. If not supervised, insertions can create long and narrow binary trees. 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 9
10 Problem with lazy insertion: setroot (10) insert (6, root) insert (5, root) insert (4, root) insert (3, root) insert (2, root) insert (1, root) Worst case: binary tree looks like a linked list 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 10
11 Why are narrow trees bad? Data insertion Data search Arrays O(n) O(n) Linked List O(1) O(n) (Balanced) Binary Trees O(log n) O(log n) Narrow Binary Trees O(n) O(n) 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 11
12 The AVL Tree Adelson-Velskii and Landis (AVL) Tree, 1962 AVL trees are balanced AVL condition for every node n, the height of n.left and the height of n.right cannot differ by more than 1. 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 12
13 AVL Condition Ok Violated 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 13
14 Inserting a New Value into an AVL Tree Insert must satisfies Insertion in log(n) Ordering condition of the sorted binary condition AVL condition Our current insertion routine satisfies Insertion in log(n) Ordering condition of the sorted binary condition Our current insertion routine does not satisfy AVL condition; it can make the tree unbalanced 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 14
15 Four cases of imbalance introduced by insertion 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 15
16 Four cases of imbalance introduced by insertion AVL condition guarantees: height(t LL ) height(t LR ) < 2 height(t RL ) height(t RR ) < 2 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 16
17 Four cases of imbalance introduced by insertion Case 1: 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 17
18 Four cases of imbalance introduced by insertion Case 1: 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 18
19 Remedy to Case 1 Single rotation rotatewithleft(k1) 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 19
20 Four cases of imbalance introduced by insertion Case 2: 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 20
21 Four cases of imbalance introduced by insertion Case 2: 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 21
22 Remedy to Case 2 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 22
23 Remedy to Case 2 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 23
24 Remedy to Case 2: Double rotation doublerotatewithleft(k3) 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 24
25 Case 3, Case 4: Mirror images of Case 1 and Case 2 Requires rotatewithright() 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 25
26 Case 3, Case 4: Mirror images of Case 1 and Case 2 Requires doublerotatewithright() 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 26
27 Inserting into AVL Tree Java Implementation 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 27
28 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 28
29 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 29
30 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 30
31 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 31
32 Summary Review of sorted binary trees Inserting new values into sorted binary trees Implications of naïve insertion AVL trees 08/11/2013 CSCI AVL Trees - F.Z. Qureshi 32
33 Readings Ch /11/2013 CSCI AVL Trees - F.Z. Qureshi 33
More Examples and Applications on AVL Tree
CSCI2100 Tutorial 11 Jianwen Zhao Department of Computer Science and Engineering The Chinese University of Hong Kong Adapted from the slides of the previous offerings of the course Recall in lectures we
More informationChapter 11 Multiway Search Trees
Chater 11 Multiway Search Trees m-way Search Trees B-Trees B + -Trees C-C Tsai P.1 m-way Search Trees Definition: An m-way search tree is either emty or satisfies the following roerties: The root has at
More informationCentral Algorithmic Techniques. Iterative Algorithms
Central Algorithmic Techniques Iterative Algorithms Code Representation of an Algorithm class InsertionSortAlgorithm extends SortAlgorithm { void sort(int a[]) throws Exception { for (int i = 1; i < a.length;
More informationChapter 8 Multioperand Addition
Chapter 8 Multioperand Addition Using Two-Operand Adders Carry-Save Adders Wallace and Dadda Trees Parallel Counters Generalized Parallel Counters Adding Multiple Signed Numbers Ch 8. Multioperand Addition
More informationCS709a: Algorithms and Complexity
CS709a: Algorithms and Complexity Focus: Spatial Data Structures and Algorithms Instructor: Dan Coming dan.coming@dri.edu Thursdays 4:00-6:45pm Office hours after class (or by appointment) Today Presentation
More informationINTRODUCTION TO MACHINE LEARNING. Decision tree learning
INTRODUCTION TO MACHINE LEARNING Decision tree learning Task of classification Automatically assign class to observations with features Observation: vector of features, with a class Automatically assign
More informationBIOL 458 BIOMETRY Lab 7 Multi-Factor ANOVA
BIOL 458 BIOMETRY Lab 7 Multi-Factor ANOVA PART 1: Introduction to Factorial ANOVA ingle factor or One - Way Analysis of Variance can be used to test the null hypothesis that k or more treatment or group
More informationCredal decision trees in noisy domains
Credal decision trees in noisy domains Carlos J. Mantas and Joaquín Abellán Department of Computer Science and Artificial Intelligence University of Granada, Granada, Spain {cmantas,jabellan}@decsai.ugr.es
More information10/6/2015. Lorrie Hargis, RA
Lorrie Hargis, RA 1 2 3 4 5 I wish I was losing weight more quickly. When has losing weight quickly EVER helped me to keep it off? The faster I ve lost it in the past, the faster I ve gained it back. Besides,
More informationLazy Associative Classification
Lazy Associative Classification By Adriano Veloso,Wagner Meira Jr., Mohammad J. Zaki Presented by: Fariba Mahdavifard Department of Computing Science University of Alberta Decision Tree (Eager) Associative
More informationClassification and Predication of Breast Cancer Risk Factors Using Id3
The International Journal Of Engineering And Science (IJES) Volume 5 Issue 11 Pages PP 29-33 2016 ISSN (e): 2319 1813 ISSN (p): 2319 1805 Classification and Predication of Breast Cancer Risk Factors Using
More informationApplication of Local Control Strategy in analyses of the effects of Radon on Lung Cancer Mortality for 2,881 US Counties
Application of Local Control Strategy in analyses of the effects of Radon on Lung Cancer Mortality for 2,881 US Counties Bob Obenchain, Risk Benefit Statistics, August 2015 Our motivation for using a Cut-Point
More informationBayesian Networks Representation. Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University
Bayesian Networks Representation Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University March 16 th, 2005 Handwriting recognition Character recognition, e.g., kernel SVMs r r r a c r rr
More informationMaximum Likelihood ofevolutionary Trees is Hard p.1
Maximum Likelihood of Evolutionary Trees is Hard Benny Chor School of Computer Science Tel-Aviv University Joint work with Tamir Tuller Maximum Likelihood ofevolutionary Trees is Hard p.1 Challenging Basic
More informationIntro to Theory of Computation
Intro to Theory of Computation LECTURE 13 Last time: Turing Machines and Variants Today Turing Machine Variants Church-Turing Thesis Sofya Raskhodnikova 2/23/2016 Sofya Raskhodnikova; based on slides by
More informationReview: Logistic regression, Gaussian naïve Bayes, linear regression, and their connections
Review: Logistic regression, Gaussian naïve Bayes, linear regression, and their connections New: Bias-variance decomposition, biasvariance tradeoff, overfitting, regularization, and feature selection Yi
More information12 Lead ECG Interpretation
12 Lead ECG Interpretation Julie Zimmerman, MSN, RN, CNS, CCRN Significant increase in mortality for every 15 minutes of delay! N Engl J Med 2007;357:1631-1638 Who should get a 12-lead ECG? Also include
More informationUnit 1 Exploring and Understanding Data
Unit 1 Exploring and Understanding Data Area Principle Bar Chart Boxplot Conditional Distribution Dotplot Empirical Rule Five Number Summary Frequency Distribution Frequency Polygon Histogram Interquartile
More informationCSE 331, Spring 2000
YOUR NAME: SECTION NUMBER: CSE 331, Spring 2000 Class Exercise 15 Algrithm Design Techniques-Greedy Algrithms March 13, 2000 Fllwing are five f the cmmn types f algrithms. Fr many prblems it is quite likely
More informationProblem Situation Form for Parents
Problem Situation Form for Parents Please complete a form for each situation you notice causes your child social anxiety. 1. WHAT WAS THE SITUATION? Please describe what happened. Provide enough information
More informationPolicy Gradients. CS : Deep Reinforcement Learning Sergey Levine
Policy Gradients CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 1 due today (11:59 pm)! Don t be late! 2. Remember to start forming final project groups Today s Lecture 1.
More informationORIGINAL RESEARCH. Correlation Between Atlas Fossa Temperature Difference and the Blair Upper Cervical Atlas Misalignment
ORIGINAL RESEARCH Correlation Between Atlas Fossa Temperature Difference and the Blair Upper Cervical Atlas Misalignment Todd Hubbard M.S., D.C. 1 & Kali Gillen D.C. 2 ABSTRACT Objective: To determine
More informationPredicting Breast Cancer Survival Using Treatment and Patient Factors
Predicting Breast Cancer Survival Using Treatment and Patient Factors William Chen wchen808@stanford.edu Henry Wang hwang9@stanford.edu 1. Introduction Breast cancer is the leading type of cancer in women
More informationSatoshi Yoshida* and Takuya Kida* *Hokkaido University Graduate school of Information Science and Technology Division of Computer Science
Stoshi Yoshid* nd Tkuy Kid* *Hokkido University Grdute school of Informtion Science nd Technology Division of Computer Science 1 Compressed Dt 01110101110111 0100101001 Serch Directly Progrm Serching on
More informationSexy Evolutionary Computation
University of Coimbra Faculty of Science and Technology Department of Computer Sciences Final Report Sexy Evolutionary Computation José Carlos Clemente Neves jcneves@student.dei.uc.pt Advisor: Fernando
More informationIntroduction to Logic: Inductive Classification
Introduction to Logic: Inductive Classification Based on the ML lecture by Raymond J. Mooney University of Texas at Austin 1 Sample Category Learning Problem Instance language: size
More informationDIABETIC RISK PREDICTION FOR WOMEN USING BOOTSTRAP AGGREGATION ON BACK-PROPAGATION NEURAL NETWORKS
International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 4, July-Aug 2018, pp. 196-201, Article IJCET_09_04_021 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=9&itype=4
More informationGame Theory. Uncertainty about Payoffs: Bayesian Games. Manar Mohaisen Department of EEC Engineering
240174 Game Theory Uncertainty about Payoffs: Bayesian Games Manar Mohaisen Department of EEC Engineering Korea University of Technology and Education (KUT) Content Bayesian Games Bayesian Nash Equilibrium
More informationData mining for Obstructive Sleep Apnea Detection. 18 October 2017 Konstantinos Nikolaidis
Data mining for Obstructive Sleep Apnea Detection 18 October 2017 Konstantinos Nikolaidis Introduction: What is Obstructive Sleep Apnea? Obstructive Sleep Apnea (OSA) is a relatively common sleep disorder
More informationFuzzy Decision Tree FID
Fuzzy Decision Tree FID Cezary Z. Janikow Krzysztof Kawa Math & Computer Science Department Math & Computer Science Department University of Missouri St. Louis University of Missouri St. Louis St. Louis,
More informationMultipower. User Guide E S S E N T I A L S T R E N G T H
E L E M E N T E S S E N T I A L S T R E N G T H User Guide The identification plate of and manufacturer, affixed to the frame behind the barbell rack, gives the following details: A B C D E Name and address
More informationVery Large Bayesian Networks in Text. classification.
Very Large Bayesian Networks in Text Classification Mieczys law A.K lopotek and Marcin Woch Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland Abstract. The paper presents results
More informationPROGRAMMER S SAFETY KIT: Important Points to Remember While Programming or Validating Safety Tables
PharmaSUG 2012 - Paper IB09 PROGRAMMER S SAFETY KIT: Important Points to Remember While Programming or Validating Safety Tables Sneha Sarmukadam, Statistical Programmer, PharmaNet/i3, Pune, India Sandeep
More informationCore and Pelvic Stability. Jim Leppik 6 January 2015
Core and Pelvic Stability Jim Leppik 6 January 2015 Pilates Box Hips, transverse abdominis, glutes and upper back. I look at core and pelvic strength in a number of phases * Basic activation * Core control,
More informationCFS for Addressing CPU Resources in Multi-Core Processors with AA Tree
CFS for Addressing CPU Resources in Multi-Core Processors with AA Tree Prajakta Pawar, S.S.Dhotre, Suhas Patil Computer Engineering Department BVDUCOE Pune-43(India) Abstract- CPU scheduler plays crucial
More informationTo open a CMA file > Download and Save file Start CMA Open file from within CMA
Example name Effect size Analysis type Level Tamiflu Hospitalized Risk ratio Basic Basic Synopsis The US government has spent 1.4 billion dollars to stockpile Tamiflu, in anticipation of a possible flu
More informationHow to Conduct On-Farm Trials. Dr. Jim Walworth Dept. of Soil, Water & Environmental Sci. University of Arizona
How to Conduct On-Farm Trials Dr. Jim Walworth Dept. of Soil, Water & Environmental Sci. University of Arizona How can you determine whether a treatment (this might be an additive, a fertilizer, snake
More informationDepartment of Statistics TEXAS A&M UNIVERSITY STAT 211. Instructor: Keith Hatfield
Department of Statistics TEXAS A&M UNIVERSITY STAT 211 Instructor: Keith Hatfield 1 Topic 1: Data collection and summarization Populations and samples Frequency distributions Histograms Mean, median, variance
More informationRecursive Partitioning Method on Survival Outcomes for Personalized Medicine
Recursive Partitioning Method on Survival Outcomes for Personalized Medicine Wei Xu, Ph.D Dalla Lana School of Public Health, University of Toronto Princess Margaret Cancer Centre 2nd International Conference
More informationSafe Work Practices From Auditing to Enabling Empowering
Safe Work Practices From Auditing to Enabling Empowering Moving from compliance auditing to competency based verification & validation, focused coaching and learning from each other John Casey, CIH, CSP
More informationMeta-Analysis David Wilson, Ph.D. Upcoming Seminar: October 20-21, 2017, Philadelphia, Pennsylvania
Meta-Analysis David Wilson, Ph.D. Upcoming Seminar: October 20-21, 2017, Philadelphia, Pennsylvania Meta-Analysis Workshop David B. Wilson, PhD September 16, 2016 George Mason University Department of
More informationModelling and Application of Logistic Regression and Artificial Neural Networks Models
Modelling and Application of Logistic Regression and Artificial Neural Networks Models Norhazlina Suhaimi a, Adriana Ismail b, Nurul Adyani Ghazali c a,c School of Ocean Engineering, Universiti Malaysia
More informationFoundations of Natural Language Processing Lecture 13 Heads, Dependency parsing
Foundations of Natural Language Processing Lecture 13 Heads, Dependency parsing Alex Lascarides (slides from Alex Lascarides, Henry Thompson, Nathan Schneider and Sharon Goldwater) 6 March 2018 Alex Lascarides
More informationEvolutionary Programming
Evolutionary Programming Searching Problem Spaces William Power April 24, 2016 1 Evolutionary Programming Can we solve problems by mi:micing the evolutionary process? Evolutionary programming is a methodology
More informationPredicting Human Immunodeficiency Virus Type 1 Drug Resistance From Genotype Using Machine Learning. Robert James Murray
Predicting Human Immunodeficiency Virus Type 1 Drug Resistance From Genotype Using Machine Learning. Robert James Murray Master of Science School of Informatics University Of Edinburgh 2004 ABSTRACT: Drug
More informationEffective Values of Physical Features for Type-2 Diabetic and Non-diabetic Patients Classifying Case Study: Shiraz University of Medical Sciences
Effective Values of Physical Features for Type-2 Diabetic and Non-diabetic Patients Classifying Case Study: Medical Sciences S. Vahid Farrahi M.Sc Student Technology,Shiraz, Iran Mohammad Mehdi Masoumi
More informationChapter 1: Introduction to Statistics
Chapter 1: Introduction to Statistics Variables A variable is a characteristic or condition that can change or take on different values. Most research begins with a general question about the relationship
More informationAmerican Society for Quality
American Society for Quality Regressions by Leaps and Bounds Author(s): George M. Furnival and Robert W. Wilson, Jr. Source: Technometrics, Vol. 6, No. (Nov., 97), pp. 99-5 Published by: American Statistical
More informationPrequential AUC: properties of the area under the ROC curve for data streams with concept drift
Knowl Inf Syst (2017) 52:531 562 DOI 10.1007/s10115-017-1022-8 REGULAR PAPER Prequential AUC: properties of the area under the ROC curve for data streams with concept drift Dariusz Brzezinski 1 Jerzy Stefanowski
More informationProblem Solving Agents
Problem Solving Agents CSL 302 ARTIFICIAL INTELLIGENCE SPRING 2014 Goal Based Agents Representation Mechanisms (propositional/first order/probabilistic logic) Learning Models Search (blind and informed)
More informationDetecting Multiple Mean Breaks At Unknown Points With Atheoretical Regression Trees
Detecting Multiple Mean Breaks At Unknown Points With Atheoretical Regression Trees 1 Cappelli, C., 2 R.N. Penny and 3 M. Reale 1 University of Naples Federico II, 2 Statistics New Zealand, 3 University
More informationINRODUCTION TO TREEAGE PRO
INRODUCTION TO TREEAGE PRO Asrul Akmal Shafie BPharm, Pg Dip Health Econs, PhD aakmal@usm.my Associate Professor & Program Chairman Universiti Sains Malaysia Board Member HTAsiaLink Adjunct Associate Professor
More informationNEED A SAMPLE SIZE? How to work with your friendly biostatistician!!!
NEED A SAMPLE SIZE? How to work with your friendly biostatistician!!! BERD Pizza & Pilots November 18, 2013 Emily Van Meter, PhD Assistant Professor Division of Cancer Biostatistics Overview Why do we
More informationappstats26.notebook April 17, 2015
Chapter 26 Comparing Counts Objective: Students will interpret chi square as a test of goodness of fit, homogeneity, and independence. Goodness of Fit A test of whether the distribution of counts in one
More informationLeman Akoglu Hanghang Tong Jilles Vreeken. Polo Chau Nikolaj Tatti Christos Faloutsos SIAM International Conference on Data Mining (SDM 2013)
Leman Akoglu Hanghang Tong Jilles Vreeken Polo Chau Nikolaj Tatti Christos Faloutsos 2013 SIAM International Conference on Data Mining (SDM 2013) What can we say about this list of authors? Use relational
More informationToday we will... Foundations of Natural Language Processing Lecture 13 Heads, Dependency parsing. Evaluating parse accuracy. Evaluating parse accuracy
Today we will... Foundations of Natural Language Processing Lecture 13 Heads, Dependency parsing Alex Lascarides (slides from Alex Lascarides, Henry Thompson, Nathan chneider and haron Goldwater) 6 March
More informationAssignment #6. Chapter 10: 14, 15 Chapter 11: 14, 18. Due tomorrow Nov. 6 th by 2pm in your TA s homework box
Assignment #6 Chapter 10: 14, 15 Chapter 11: 14, 18 Due tomorrow Nov. 6 th by 2pm in your TA s homework box Assignment #7 Chapter 12: 18, 24 Chapter 13: 28 Due next Friday Nov. 13 th by 2pm in your TA
More informationmotivation workbook why do you want to change?
motivation workbook why do you want to change? Start by figuring out your personal reasons for wanting to change. Here are some interesting truths about reasons for change: The clearer you are in your
More informationReview Questions in Introductory Knowledge... 37
Table of Contents Preface..... 17 About the Authors... 19 How This Book is Organized... 20 Who Should Buy This Book?... 20 Where to Find Answers to Review Questions and Exercises... 20 How to Report Errata...
More informationSUPPLEMENTARY MATERIAL
SUPPLEMENTARY MATERIAL Supplementary Figure 1. Recursive partitioning using PFS data in patients with advanced NSCLC with non-squamous histology treated in the placebo pemetrexed arm of LUME-Lung 2. (A)
More informationChemistry, Biology and Environmental Science Examples STRONG Lesson Series
Chemistry, Biology and Environmental Science Examples STRONG Lesson Series 1 Examples from Biology, Chemistry and Environmental Science Applications 1. In a forest near Dahlonega, 1500 randomly selected
More informationCOMPARISON OF DECISION TREE METHODS FOR BREAST CANCER DIAGNOSIS
COMPARISON OF DECISION TREE METHODS FOR BREAST CANCER DIAGNOSIS Emina Alickovic, Abdulhamit Subasi International Burch University, Faculty of Engineering and Information Technologies Sarajevo, Bosnia and
More information10/17/2018. Today s Agenda. Definition of Culture (Cultural Competency Framework) Current teaching considers culture as the sum total of:
TOWARD A CULTURALLY RESPECTFUL, RESPONSIVE, AND EQUITABLE MENTAL HEALTH PRACTICE: Moving Away from the Myth of Cultural Competence Dr. Jerry L. Johnson, MSW Associate Professor, School of Social Work Grand
More informationRumor Detection on Twitter with Tree-structured Recursive Neural Networks
1 Rumor Detection on Twitter with Tree-structured Recursive Neural Networks Jing Ma 1, Wei Gao 2, Kam-Fai Wong 1,3 1 The Chinese University of Hong Kong 2 Victoria University of Wellington, New Zealand
More informationMBA 605 Business Analytics Don Conant, PhD. GETTING TO THE STANDARD NORMAL DISTRIBUTION
MBA 605 Business Analytics Don Conant, PhD. GETTING TO THE STANDARD NORMAL DISTRIBUTION Variables In the social sciences data are the observed and/or measured characteristics of individuals and groups
More informationEffect of Source and Level of Protein on Weight Gain of Rats
Effect of Source and Level of Protein on of Rats 1 * two factor analysis of variance with interaction; 2 option ls=120 ps=75 nocenter nodate; 3 4 title Effect of Source of Protein and Level of Protein
More informationCorrelation and regression
PG Dip in High Intensity Psychological Interventions Correlation and regression Martin Bland Professor of Health Statistics University of York http://martinbland.co.uk/ Correlation Example: Muscle strength
More informationFeasibility Study in Digital Screening of Inflammatory Breast Cancer Patients using Selfie Image
Feasibility Study in Digital Screening of Inflammatory Breast Cancer Patients using Selfie Image Reshma Rajan and Chang-hee Won CSNAP Lab, Temple University Technical Memo Abstract: Inflammatory breast
More informationCOMPREHENSIVE HEALING PROGRAM
COMPREHENSIVE HEALING PROGRAM Tired of feeling like you re just surviving instead of really living? Feel like you ve tried everything yet have no resolution of your issues? Don t want to take another medication?
More informationStudy of Stress Rules Based on HRV Features
Journal of Computers Vol. 29 o. 5, 2018, pp. 41-51 doi:10.3966/199115992018102905004 Study of Stress Rules Based on HRV Features Gang Zheng 1, Yingli Wang 1*, Yanhui Chen 1 1 School of Computer Science
More informationUsing Phylogenetic Structure to Assess the Evolutionary Ecology of Microbiota! TJS! iseem Call! April 2015!
Using Phylogenetic Structure to Assess the Evolutionary Ecology of Microbiota! TJS! iseem Call! April 2015! How are Microbes Distributed In Nature?! A major question in microbial ecology! Used to assess
More informationSummarizing Data. (Ch 1.1, 1.3, , 2.4.3, 2.5)
1 Summarizing Data (Ch 1.1, 1.3, 1.10-1.13, 2.4.3, 2.5) Populations and Samples An investigation of some characteristic of a population of interest. Example: You want to study the average GPA of juniors
More informationComparative Accuracy of a Diagnostic Index Modeled Using (Optimized) Regression vs. Novometrics
Comparative Accuracy of a Diagnostic Index Modeled Using (Optimized) Regression vs. Novometrics Ariel Linden, Dr.P.H. and Paul R. Yarnold, Ph.D. Linden Consulting Group, LLC Optimal Data Analysis LLC Diagnostic
More informationFinancial Risk Forecasting Chapter 8 Backtesting and stresstesting
Financial Risk Forecasting Chapter 8 Backtesting and stresstesting Jon Danielsson 2017 London School of Economics To accompany Financial Risk Forecasting www.financialriskforecasting.com Published by Wiley
More informationResearch Methods in Forest Sciences: Learning Diary. Yoko Lu December Research process
Research Methods in Forest Sciences: Learning Diary Yoko Lu 285122 9 December 2016 1. Research process It is important to pursue and apply knowledge and understand the world under both natural and social
More informationCOMMUNITY ATTITUDES TOWARD HIROLA CONSERVATION AND RANGE RESTORATION IN EASTERN KENYA INTRODUCTION
COMMUNITY ATTITUDES TOWARD HIROLA CONSERVATION AND RANGE RESTORATION IN EASTERN KENYA INTRODUCTION Pastoralists all over the world are knowledgeable about their environments and regulated resource use
More informationElectrocardiogram ECG. Hilal Al Saffar FRCP FACC College of medicine,baghdad University
Electrocardiogram ECG Hilal Al Saffar FRCP FACC College of medicine,baghdad University Tuesday 29 October 2013 ECG introduction Wednesday 30 October 2013 Abnormal ECG ( ischemia, chamber hypertrophy, heart
More informationEmployee Feedback Form
Employee Feedback Form The following physiotherapy service questionnaire was handed to those new patients who were seen over a 3 month period (December February 7). There were 15 new patients seen, of
More informationMaterial Selection Tutorial
Material Selection Tutorial Selecting an appropriate material is a critical part of almost all engineering designs There are many factors to consider Strength, stiffness, durability, corrosion, cost, formability,
More informationPLEASE DO NOT REMOVE THIS PAGE
Thank you for downloading this document from the RMIT Research Repository. The RMIT Research Repository is an open access database showcasing the research outputs of RMIT University researchers. RMIT Research
More informationStatistical Summaries. Kerala School of MathematicsCourse in Statistics for Scientists. Descriptive Statistics. Summary Statistics
Kerala School of Mathematics Course in Statistics for Scientists Statistical Summaries Descriptive Statistics T.Krishnan Strand Life Sciences, Bangalore may be single numerical summaries of a batch, such
More informationA prediction model for type 2 diabetes using adaptive neuro-fuzzy interface system.
Biomedical Research 208; Special Issue: S69-S74 ISSN 0970-938X www.biomedres.info A prediction model for type 2 diabetes using adaptive neuro-fuzzy interface system. S Alby *, BL Shivakumar 2 Research
More informationExploratory Quantitative Contrast Set Mining: A Discretization Approach
Exploratory Quantitative Contrast Set Mining: A Discretization Approach Mondelle Simeon and Robert J. Hilderman Department of Computer Science University of Regina Regina, Saskatchewan, Canada S4S 0A2
More informationEmotional regulation the thinking-feeling link
session 6 Emotional regulation the thinking-feeling link Background This session aims to introduce the idea that emotions are not a direct consequence of events, but reflect the way people think about
More information1
www.empireperformancept.com 1 Copyright Notice 2017 Empire Performance PT, LLC All rights reserved. Any unauthorized use, sharing, reproduction or distribution of these materials by any means, electronic,
More informationAnalyzing Gene Expression Data: Fuzzy Decision Tree Algorithm applied to the Classification of Cancer Data
Analyzing Gene Expression Data: Fuzzy Decision Tree Algorithm applied to the Classification of Cancer Data Simone A. Ludwig Department of Computer Science North Dakota State University Fargo, ND, USA simone.ludwig@ndsu.edu
More informationStandard Deviation and Standard Error Tutorial. This is significantly important. Get your AP Equations and Formulas sheet
Standard Deviation and Standard Error Tutorial This is significantly important. Get your AP Equations and Formulas sheet The Basics Let s start with a review of the basics of statistics. Mean: What most
More informationInternational Journal of Pharma and Bio Sciences A NOVEL SUBSET SELECTION FOR CLASSIFICATION OF DIABETES DATASET BY ITERATIVE METHODS ABSTRACT
Research Article Bioinformatics International Journal of Pharma and Bio Sciences ISSN 0975-6299 A NOVEL SUBSET SELECTION FOR CLASSIFICATION OF DIABETES DATASET BY ITERATIVE METHODS D.UDHAYAKUMARAPANDIAN
More informationA Taxonomy of Decision Models in Decision Analysis
This section is cortesy of Prof. Carlos Bana e Costa Bayesian Nets lecture in the Decision Analysis in Practice Course at the LSE and of Prof. Alec Morton in the Bayes Nets and Influence Diagrams in the
More informationPolicy Gradients. CS : Deep Reinforcement Learning Sergey Levine
Policy Gradients CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 1 milestone due today (11:59 pm)! Don t be late! 2. Remember to start forming final project groups Today s
More informationCITY OF ST. PETERSBURG CODE ENFORCEMENT BOARD Lien Release/Reduction Request Agenda Hearing Date: 06/28/2017
CITY OF ST. PETERSBURG CODE ENFORCEMENT BOARD Release/Reduction Request Agenda Hearing : 6/28/217 LR 1: 1911 31st St S. Owner(s): Tucker Inc. Representative: Margaret Edwards LR 2: 1679 29th Ave N. Owner(s):
More informationPattern Analysis and Machine Intelligence
Pattern Analysis and Machine Intelligence Lecture tes on Machine Learning Matteo Matteucci matteucci@eletpolimiit Department of Electronics and Information Politecnico di Milano Matteo Matteucci c Lecture
More informationPerformance Analysis of Different Classification Methods in Data Mining for Diabetes Dataset Using WEKA Tool
Performance Analysis of Different Classification Methods in Data Mining for Diabetes Dataset Using WEKA Tool Sujata Joshi Assistant Professor, Dept. of CSE Nitte Meenakshi Institute of Technology Bangalore,
More informationData Mining Techniques to Predict Survival of Metastatic Breast Cancer Patients
Data Mining Techniques to Predict Survival of Metastatic Breast Cancer Patients Abstract Prognosis for stage IV (metastatic) breast cancer is difficult for clinicians to predict. This study examines the
More informationPROFILE SIMILARITY IN BIOEQUIVALENCE TRIALS
Sankhyā : The Indian Journal of Statistics Special Issue on Biostatistics 2000, Volume 62, Series B, Pt. 1, pp. 149 161 PROFILE SIMILARITY IN BIOEQUIVALENCE TRIALS By DAVID T. MAUGER and VERNON M. CHINCHILLI
More informationLecture 20: Chi Square
Statistics 20_chi.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 20: Chi Square Introduction Up until now, we done statistical test using means, but the assumptions for means have eliminated
More informationSequence balance minimisation: minimising with unequal treatment allocations
Madurasinghe Trials (2017) 18:207 DOI 10.1186/s13063-017-1942-3 METHODOLOGY Open Access Sequence balance minimisation: minimising with unequal treatment allocations Vichithranie W. Madurasinghe Abstract
More information7 Traits of the. Happiest Healthiest. and People
7 Traits of the Happiest Healthiest and People Don t change much You don t have to like the Pharrell Williams song Happy to be happy. If Motorhead or Mozart is more your speed, so be it. But if you find
More informationHuman Activities: Handling Uncertainties Using Fuzzy Time Intervals
The 19th International Conference on Pattern Recognition (ICPR), Tampa, FL, 2009 Human Activities: Handling Uncertainties Using Fuzzy Time Intervals M. S. Ryoo 1,2 and J. K. Aggarwal 1 1 Computer & Vision
More information