DAMA Slides/Graphics
|
|
- Jeffery Charles
- 5 years ago
- Views:
Transcription
1 DAMA Slides/Graphics Rick Gaitskell Center for Particle Astrophysics UC Berkeley source at Gaitskell
2 Best fit to Ann Mod data alone Best Fit Minimum DAMA NaI/1-4 DAMA NaI/1-4 (3σ) New CDMS limit Modulation Amplitude Best-fit WIMP model s expected annual modulation does not appear to fit data; lowest point of 3σ contour is much worse. Why? Additional constraint applied during max likelihood analysis: DC WIMP signal implied by AC signal must not exceed observed DC count rate best-fit cross-section is decreased mean over 2-6 kevee (22 66 kev recoil) Best fit to Ann Mod data alone DAMA 2000 paper Figure 2 DAMA 1 DAMA 2 DAMA DAMASummaryGaitskell3.pp June ,000 kg-day 15,000 kg-day 38,000 kg-day Rick Gaitskell
3 DAMA - Cartoon of Lowest Energy Bins 30,000 evts 12,000 evts 6,000 evts ~600 days x 100 kg ~300,000 b/g events in range kev recoil ~50,000 WIMP events (latter expected in sample but are not observed directly) DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
4 Bernabei et al. ROM2F/98/34, INFN/AE-98/20, LNGS Preprint DAMA - Energy Spectra Data re-plotted by Gaitskell, CfPA Rate corresponding to best fit to Ann Mod data alone (σ = cm 2 ) is shown as magenta dashed line - exceeds signal in 2-3 kev bin green: WIMP component assuming (50 GeV,7.2x10-42 cm 2 ) red points: individual spectra red line: average spectrum Very low background Very low background Energy is kevee, not recoil energy: 9% iodine quenching factor Plotted spectra are already efficiency-corrected Possible problems: Signal entirely determined by 2-3 and 3-4 kevee bins Large drop in background in lowest bin; worsened by large inferred WIMP component Strange background shape; would expect flat or rising Large fluctuations in low energy background Consistency of signal among detectors? % is ratio of 2-3 kev bin background to 3-4 kev background rate DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
5 DAMA Sensitivity of Different Energy Bins The shape of DAMA Signal Region is determined by gross sensitivity of experiment, not by any WIMP mass selectivity in analysis Reduction in sensitivity corresponds to pivot point of modulation WIMP spectrum passing through that bin DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
6 Compatibility of CDMS and DAMA CDMS results incompatible with DAMA Figure 2 data (left) at > 99.98% CL Best simultaneous fit to CDMS and DAMA predicts too little annual modulation in DAMA, too many events in CDMS DAMA 2000 paper Figure 2 CDMS data predicted spectrum Estimate full DAMA likelihood function: Two experiments are incompatible at 99.8% CL Ignore multiple scatters: 96.8% CL DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
7 CDMS & Expected WIMP Spectra DAMA NaI/1-4 best-fit WIMP (52 GeV, cm 2 ): CDMS expects 20 WIMP events DAMA NaI/1-4 lowest 3σ contour (40 GeV, cm 2 ): CDMS expects 6 WIMP events Expected WIMPs Detected CDMS NR spectrum after subtraction of most likely neutron background Recoil Energy (kev) WIMP spectra corrected for CDMS efficiency function DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
8 Slides of Location DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
9 Gran Sasso National Laboratory (LNGS) DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
10 Gran Sasso National Laboratory (LNGS) First Director: Zichichi CUORE Large Volume Detector (SN Det) (Opera) (will be removed in 2001) (MiBeta - Fiorini Group) HDMS Heidelberg-Moscow & HDMS DAMA DAQ DAMA NaI ICARUS First Module 0.6kt Sept 99 CRESST DAMA Updated DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
11 DAMA - Experimental Apparatus Very elegant experimental setup - in place >1996 Low Activity NaI scintillator kg NaI crystals, each viewed by 2 PMTs Located at Gran Sasso Underground Lab (4000 mwe) + Photon and Neutron shielding Two modes of Background discrimination Pulse shape Annual modulation: ~2% modulation amplitude PMT NaI NaI NaI NaI PMT DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
12 DAMA NaI Experiment Run 100 kg of ultrapure NaI Now 58,000 kg-days See annual modulation signal DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
13 In depth/technical slides DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
14 DAMA - Energy Histograms from NaI Array Lowest energy bin is most important when setting dark matter limit Earlier paper contained spectra for all 9 detectors separately (Note log plot covers 6 orders of magnitude) Dark Matter sensitivity is determined primarily by behaviour of bottom bins covering 2-3keVee (which is recoil energy, after adjusting for nuclear recoil quenching factor on I nuclei of 0.09) Bernabei et al. ROM2F/98/34, INFN/AE-98/20, LNGS Preprint DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
15 DAMA - Energy Resolution/Trigger Issues Bernabei et al. ROM2F/98/34, INFN/AE-98/20, LNGS Preprint Blue histograms show estimated resolution at 46.5 kev, 13 kev and 2.5 kev 210 Pb 46.5 kev X-ray [kev] = 58.5% E / kev Data re-plotted by Gaitskell, CfPA Spectra incompatible with resolution Energy calibration - requires X% stability Large trigger efficiency correction: trigger stability? Trigger Efficiency 100% >8 kevee 40% in 2-3 kevee bin Drop in spectrum inconsistent with resolution function Energy [kevee] DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
16 Annual Modulation of Rate & Spectrum DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
17 Modulation Animation in NaI 150 GeV WIMP 50 GeV WIMP DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell!
18 Annual Modulation Not distinguish between WIMP signal and Background directly From the amplitude of the modulation, we can calculate the underlying WIMP interaction rate WIMP Signal ±2% Background Dec June Dec June Dec June Dec June DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
19 DAMA Modulation 3 year result ROM2F/2000/01 LNGS Preprint (just released) DAMA 1 5,000 kg-day DAMA 2 15,000 kg-day DAMA ,000 kg-day Rebuilt Shown above is residual modulation in events/kevee/kg/day 2-6 kevee range Total Counts 2-6 kevee WIMPs+Background is ~700 events/day Note that most of modulation is expected in 2-3 kevee bin New data covering 3 year data was released February 2000 LNGS approved (Dec 1999) upgrade to 250 kg NaI array DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
20 DAMA allowed region & SUSY predictions DAMA/1-4 (3σ) ~2.3 event/kgge/day DAMA/1-4 (3σ) Most Likely Fit (52 GeV, cm 2 ) DAMA/1-4 (3σ) Lowest cross-section (40 GeV, cm 2 ) DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
21 DAMA NaI Questions Annual Modulation Experiment It isn t simply a matter of sufficient statistics. if σ~10-5 pb then 100 kg NaI array is sensitive enough However, must also demonstrate that other possible fake sources are not present And, that the control of experiment environment is much better than 1% level of apparent signal E.g. Not only confirm energy calibration, but that stability of width of lowest energy bin is good at <1%) BIN WIDTH/THRESHOLD STABILITY Physical Shape of Raw Background Spectrum Looks odd, especially when compared to other NaI Low Background Experiments DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
22 DAMA NaI Pulse Shape Discrim. Question Pulse Shape Discrimination NOT used in annual modulation identification of 3σ signal Doesn t really help reduce background in lowest energy bin 2-3 kevee (which due to I QF=9% dominates stats.) due to poor signal/noise Saclay and UKDM have both identified additional population of events during background running that are neither electron or nuclear recoils. Anomalous events not been observed in DAMA data, and exclusion limit is quoted accordingly DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
23 DAMA NaI Outlook New data from 2 year run has been published 3 times previous data Have they provided improved comment on systematics? INFN Approved upgrade of DAMA to 250 kg NaI Anomolous Events seen in other NaI Experiments UKDM (Boulby) & Saclay (Modane) NaI experiments Close to an explanation (may be surface contamination) Signal Region Being checked by CDMS DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
24 Test of Compatibility with DAMA (not using DAMA-0) We have compared CDMS upper limit CL to a region allowed by DAMA signal without including constraint from DAMA's 1996 (NaI-0) upper limit Region including this constraint is made under assumption that DAMA's signal and upper limit are both correct -- but this may not be the case. If CDMS is incompatible with DAMA's signal, either DAMA's signal is wrong or CDMS's limit is wrong. The fact that DAMA has data producing an upper limit should not make their signal appear to be more compatible with other upper limits. The large difference between DAMA's two regions is due to fact that their upper limit is already somewhat incompatible with their signal (although not too significantly); the most likely (M,σ) point for their signal is ruled out by their 90% CL upper limit DAMASummaryGaitskell3.pp June 2000 Rick Gaitskell
LUX & LZ Dark Matter Sanford Lab / DUSEL
LUX & LZ Dark Matter Experiments @ Sanford Lab / DUSEL Rick Gaitskell, Joint Spokesperson, LUX Collaboration US Spokesperson, LUX ZEPLIN (LZ) Collaboration Particle Astrophysics Group, Brown University,
More information12 GeV CEBAF Upgrade. Risk Management Plan
12 GeV CEBAF Upgrade Risk Management Plan May 29, 2007 12 GeV CEBAF Upgrade Risk Management Plan 1 Apr 05 ISSUE DATE PAGES AFFECTED DESCRIPTION Original CD-2 4/01/05 5/29/07 All All General update to maintain
More informationChandra s PSF: Use it Wisely
Chandra s PSF: Use it Wisely Diab Jerius Smithsonian Astrophysical Observatory 2014 Chandra Calibration/Ciao Workshop Diab Jerius (SAO) Chandra s PSF CCCW 2014 1 / 41 All you need to know Outline 1 All
More informationDisclaimer: This resource package is for studying purposes only EDUCATION
Disclaimer: This resource package is for studying purposes only EDUCATION Chapter 6: Valuing stocks Bond Cash Flows, Prices, and Yields - Maturity date: Final payment date - Term: Time remaining until
More informationProbability distributions relevant to radiowave propagation modelling
Rec. ITU-R P.57 RECOMMENDATION ITU-R P.57 PROBABILITY DISTRIBUTIONS RELEVANT TO RADIOWAVE PROPAGATION MODELLING (994) Rec. ITU-R P.57 The ITU Radiocommunication Assembly, considering a) that the propagation
More informationChapter 8 Estimation
Chapter 8 Estimation There are two important forms of statistical inference: estimation (Confidence Intervals) Hypothesis Testing Statistical Inference drawing conclusions about populations based on samples
More informationMaking Sense of Cents
Name: Date: Making Sense of Cents Exploring the Central Limit Theorem Many of the variables that you have studied so far in this class have had a normal distribution. You have used a table of the normal
More informationModelling catastrophic risk in international equity markets: An extreme value approach. JOHN COTTER University College Dublin
Modelling catastrophic risk in international equity markets: An extreme value approach JOHN COTTER University College Dublin Abstract: This letter uses the Block Maxima Extreme Value approach to quantify
More informationSiPM characterization report for the Muon Portal Project Device: SiPM type N on P - S/N. SPM10H5-60N-Y wf16 ST Microelectronics
OSSERVATORIO ASTROFISICO DI CATANIA SiPM characterization report for the Muon Portal Project Device: SiPM type N on P - S/N. SPM10H5-60N-Y223131-wf16 ST Microelectronics Osservatorio Astrofisico di Catania
More informationThe Higgs Particle Mass, Width and Couplings Seminar Particle Physics at the LHC
The Higgs Particle, and Seminar Particle Physics at the LHC Freiburg, 22.07.2014 Albert-Ludwigs-Universität Freiburg Michael Schubert Contents 2/54 introduction We found a Higgs boson! So... What now?
More informationSTAT 157 HW1 Solutions
STAT 157 HW1 Solutions http://www.stat.ucla.edu/~dinov/courses_students.dir/10/spring/stats157.dir/ Problem 1. 1.a: (6 points) Determine the Relative Frequency and the Cumulative Relative Frequency (fill
More informationLHC DATA! Current status of LHC and ATLAS and prospects for
LHC DATA! Current status of LHC and ATLAS and prospects for 2010-2011 Andy Haas SLAC West Coast LHC Theory Meeting SCIPP, UC Santa Cruz May 21, -2010 Andy Haas 5/21/2010 Slide 1 Overview LHC commissioning
More informationSimple, low-cost fabrication of highly uniform and reproducible SERS substrates composed of Ag-Pt nanoparticles
Supporting Information Simple, low-cost fabrication of highly uniform and reproducible SERS substrates composed of Ag-Pt nanoparticles Tao Wang, Juhong Zhou* and Yan Wang Provincial Key Laboratory of Functional
More informationROBUST CHAUVENET OUTLIER REJECTION
Submitted to the Astrophysical Journal Supplement Series Preprint typeset using L A TEX style emulateapj v. 12/16/11 ROBUST CHAUVENET OUTLIER REJECTION M. P. Maples, D. E. Reichart 1, T. A. Berger, A.
More information1. You roll a six sided die two times. What is the probability that you do not get a three on either roll? 5/6 * 5/6 = 25/36.694
Math 107 Review for final test 1. You roll a six sided die two times. What is the probability that you do not get a three on either roll? 5/6 * 5/6 = 25/36.694 2. Consider a box with 5 blue balls, 7 red
More informationStudy of the rare decays of B 0 and B 0 to muon pairs with the ATLAS detector at LHC Run- 1
Study of the rare decays of B 0 and B 0 to muon pairs s with the ATLAS detector at LHC Run- 1 Sandro Pales@ni (CERN) On behalf of the ATLAS Collabora@on Rencontres de Moriond EW interac@on and Unified
More informationNotes on bioburden distribution metrics: The log-normal distribution
Notes on bioburden distribution metrics: The log-normal distribution Mark Bailey, March 21 Introduction The shape of distributions of bioburden measurements on devices is usually treated in a very simple
More informationParameter Sensitivities for Radionuclide Concentration Prediction in PRAME
Environment Report RL 07/05 Parameter Sensitivities for Radionuclide Concentration Prediction in PRAME The Centre for Environment, Fisheries and Aquaculture Science Lowestoft Laboratory Pakefield Road
More informationStrike Point Control on EAST Using an Isoflux Control Method
Plasma Science and Technology, Vol.17, No.9, Sep. 2015 Strike Point Control on EAST Using an Isoflux Control Method XING Zhe ( ) 1, XIAO Bingjia ( ) 1,2, LUO Zhengping ( ) 1, M. L. WALKER 3, D. A. HUMPHREYS
More informationCLASS 4: ASSEt pricing. The Intertemporal Model. Theory and Experiment
CLASS 4: ASSEt pricing. The Intertemporal Model. Theory and Experiment Lessons from the 1- period model If markets are complete then the resulting equilibrium is Paretooptimal (no alternative allocation
More informationMeasures of Dispersion (Range, standard deviation, standard error) Introduction
Measures of Dispersion (Range, standard deviation, standard error) Introduction We have already learnt that frequency distribution table gives a rough idea of the distribution of the variables in a sample
More informationA.REPRESENTATION OF DATA
A.REPRESENTATION OF DATA (a) GRAPHS : PART I Q: Why do we need a graph paper? Ans: You need graph paper to draw: (i) Histogram (ii) Cumulative Frequency Curve (iii) Frequency Polygon (iv) Box-and-Whisker
More informationBasic Procedure for Histograms
Basic Procedure for Histograms 1. Compute the range of observations (min. & max. value) 2. Choose an initial # of classes (most likely based on the range of values, try and find a number of classes that
More informationSpike Statistics: A Tutorial
Spike Statistics: A Tutorial File: spike statistics4.tex JV Stone, Psychology Department, Sheffield University, England. Email: j.v.stone@sheffield.ac.uk December 10, 2007 1 Introduction Why do we need
More informationASTM D02, Dec./ Orlando Statistics Seminar
ASTM D02, Dec./ Orlando Statistics Seminar Presented by: Alex Lau, Chairman, D02.94 This brief seminar will provide : a very simple explanation on the use of statistics to estimate population parameters
More informationThe Taboga Options Pricing Model
The Taboga Options Pricing Model 1.2 1.167 1 0.833... with applications 0.8 0.6 0.556 0.4 0.333 2018 Gary R. Evans. This slide set by Gary R. Evans is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike
More informationBig Data & Machine Learning in HEP
Big Data & Machine Learning in HEP Mike Williams Department of Physics & Laboratory for Nuclear Science Massachusetts Institute of Technology March 17, 216 The Large Hadron Collider Outline { Big Data
More informationDescriptive Statistics (Devore Chapter One)
Descriptive Statistics (Devore Chapter One) 1016-345-01 Probability and Statistics for Engineers Winter 2010-2011 Contents 0 Perspective 1 1 Pictorial and Tabular Descriptions of Data 2 1.1 Stem-and-Leaf
More informationTrading With Time Fractals to Reduce Risk and Improve Profit Potential
June 16, 1998 Trading With Time Fractals to Reduce Risk and Improve Profit Potential A special Report by Walter Bressert Time and price cycles in the futures markets and stocks exhibit patterns in time
More informationHonors Statistics. Daily Agenda
Honors Statistics Aug 23-8:26 PM Daily Agenda Aug 23-8:31 PM 1 Write a program to generate random numbers. I've decided to give them free will. A Skip 4, 12, 16 Apr 25-10:55 AM Toss 4 times Suppose you
More informationSummarising Data. Summarising Data. Examples of Types of Data. Types of Data
Summarising Data Summarising Data Mark Lunt Arthritis Research UK Epidemiology Unit University of Manchester Today we will consider Different types of data Appropriate ways to summarise these data 17/10/2017
More informationDATA SUMMARIZATION AND VISUALIZATION
APPENDIX DATA SUMMARIZATION AND VISUALIZATION PART 1 SUMMARIZATION 1: BUILDING BLOCKS OF DATA ANALYSIS 294 PART 2 PART 3 PART 4 VISUALIZATION: GRAPHS AND TABLES FOR SUMMARIZING AND ORGANIZING DATA 296
More informationStat 101 Exam 1 - Embers Important Formulas and Concepts 1
1 Chapter 1 1.1 Definitions Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1. Data Any collection of numbers, characters, images, or other items that provide information about something. 2.
More informationConverting to the Standard Normal rv: Exponential PDF and CDF for x 0 Chapter 7: expected value of x
Key Formula Sheet ASU ECN 22 ASWCC Chapter : no key formulas Chapter 2: Relative Frequency=freq of the class/n Approx Class Width: =(largest value-smallest value) /number of classes Chapter 3: sample and
More informationPresented at the 2003 SCEA-ISPA Joint Annual Conference and Training Workshop -
Predicting Final CPI Estimating the EAC based on current performance has traditionally been a point estimate or, at best, a range based on different EAC calculations (CPI, SPI, CPI*SPI, etc.). NAVAIR is
More informationDATA HANDLING Five-Number Summary
DATA HANDLING Five-Number Summary The five-number summary consists of the minimum and maximum values, the median, and the upper and lower quartiles. The minimum and the maximum are the smallest and greatest
More informationPercentiles, STATA, Box Plots, Standardizing, and Other Transformations
Percentiles, STATA, Box Plots, Standardizing, and Other Transformations Lecture 3 Reading: Sections 5.7 54 Remember, when you finish a chapter make sure not to miss the last couple of boxes: What Can Go
More informationThe latest CALICE activities
The latest CALICE activities Fabrizio Salvatore Royal Holloway University of London HEP seminar, RHUL, 22 nd January 2008 Rationale for a Linear Collider The LHC start up next year is expected to mark
More informationUsing Comparative Inventory to Bet Against the Oil Market
Using Comparative Inventory to Bet Against the Oil Market Art Berman MacroVoices Live Vancouver January 19, 2019 Slide 1 Oil-Price Collapse and Previous Collapses $220 Oil-Price Collapse Appears to be
More informationMaking Risk Management Tools More Credible: Calibrating the Risk Cube
Making Risk Management Tools More Credible: Calibrating the Risk Cube SCEA 2006 Washington, DC Richard L. Coleman, Jessica R. Summerville, Megan E. Dameron Northrop Grumman Corporation 0 Outline! The General
More informationStructural credit risk models and systemic capital
Structural credit risk models and systemic capital Somnath Chatterjee CCBS, Bank of England November 7, 2013 Structural credit risk model Structural credit risk models are based on the notion that both
More informationBoth the quizzes and exams are closed book. However, For quizzes: Formulas will be provided with quiz papers if there is any need.
Both the quizzes and exams are closed book. However, For quizzes: Formulas will be provided with quiz papers if there is any need. For exams (MD1, MD2, and Final): You may bring one 8.5 by 11 sheet of
More informationMixed models in R using the lme4 package Part 3: Inference based on profiled deviance
Mixed models in R using the lme4 package Part 3: Inference based on profiled deviance Douglas Bates Department of Statistics University of Wisconsin - Madison Madison January 11, 2011
More informationPortfolio modelling of operational losses John Gavin 1, QRMS, Risk Control, UBS, London. April 2004.
Portfolio modelling of operational losses John Gavin 1, QRMS, Risk Control, UBS, London. April 2004. What is operational risk Trends over time Empirical distributions Loss distribution approach Compound
More informationPresentation to PPAP meeting. Jordan Nash PPAN Chair
Presentation to PPAP meeting Jordan Nash PPAN Chair Why this meeting? What is happening What do we need from the PP Community What is happening STFC needs to prepare a budget for 2010/2011 and planning
More informationDescription of Data I
Description of Data I (Summary and Variability measures) Objectives: Able to understand how to summarize the data Able to understand how to measure the variability of the data Able to use and interpret
More informationSierra Environmental Studies Foundation
TN0903-1: Gini Index Made Simple George Rebane, Ph.D. SESF, Director of Research 22 March 2009 1 Overview The distribution of wealth or income over a population is of great interest to economists, sociologists,
More informationMinimizing Basis Risk for Cat-In- Catastrophe Bonds Editor s note: AIR Worldwide has long dominanted the market for. By Dr.
Minimizing Basis Risk for Cat-In- A-Box Parametric Earthquake Catastrophe Bonds Editor s note: AIR Worldwide has long dominanted the market for 06.2010 AIRCurrents catastrophe risk modeling and analytical
More informationOnline Appendix. income and saving-consumption preferences in the context of dividend and interest income).
Online Appendix 1 Bunching A classical model predicts bunching at tax kinks when the budget set is convex, because individuals above the tax kink wish to decrease their income as the tax rate above the
More informationR & R Study. Chapter 254. Introduction. Data Structure
Chapter 54 Introduction A repeatability and reproducibility (R & R) study (sometimes called a gauge study) is conducted to determine if a particular measurement procedure is adequate. If the measurement
More informationMath 140 Introductory Statistics. First midterm September
Math 140 Introductory Statistics First midterm September 23 2010 Box Plots Graphical display of 5 number summary Q1, Q2 (median), Q3, max, min Outliers If a value is more than 1.5 times the IQR from the
More informationSystem Flexibility Indicators. Operational Forum, 26 May 2010
System Flexibility Indicators Operational Forum, 26 May 21 Introduction There is potential for greater volatility in gas flows in future years driven, in particular, by: Growth in renewable sources of
More informationStructure & Learning Objectives
U1 Structure & Learning Objectives In this part of the course, we will study the newly revamped IMF framework for public debt sustainability in market-access countries A historical overview of debt-to-gdp
More informationData Analysis. BCF106 Fundamentals of Cost Analysis
Data Analysis BCF106 Fundamentals of Cost Analysis June 009 Chapter 5 Data Analysis 5.0 Introduction... 3 5.1 Terminology... 3 5. Measures of Central Tendency... 5 5.3 Measures of Dispersion... 7 5.4 Frequency
More informationMATHEMATICS APPLIED TO BIOLOGICAL SCIENCES MVE PA 07. LP07 DESCRIPTIVE STATISTICS - Calculating of statistical indicators (1)
LP07 DESCRIPTIVE STATISTICS - Calculating of statistical indicators (1) Descriptive statistics are ways of summarizing large sets of quantitative (numerical) information. The best way to reduce a set of
More informationBID REFERENCE: NITD/CHEMISTRY/SSP/TCSPC/ /01 date:
NATIONAL INSTITUTE OF TECHNOLOGY, DURGAPUR MAHATMA GANDHI AVENUE DURGAPUR 713 209, WEST BENGAL, INDIA FAX: 0343-2547375; Website: www.nitdgp.ac.in; Telephones: + 91-343 2545290 BID REFERENCE: NITD/CHEMISTRY/SSP/TCSPC/2016-17/01
More informationModern Methods of Data Analysis - SS 2009
Modern Methods of Data Analysis Lecture II (7.04.09) Contents: Characterize data samples Characterize distributions Correlations, covariance Reminder: Average of a Sample arithmetic mean of data set: weighted
More informationTests for Two Variances
Chapter 655 Tests for Two Variances Introduction Occasionally, researchers are interested in comparing the variances (or standard deviations) of two groups rather than their means. This module calculates
More informationLecture 6: Non Normal Distributions
Lecture 6: Non Normal Distributions and their Uses in GARCH Modelling Prof. Massimo Guidolin 20192 Financial Econometrics Spring 2015 Overview Non-normalities in (standardized) residuals from asset return
More informationMeasuring Financial Risk using Extreme Value Theory: evidence from Pakistan
Measuring Financial Risk using Extreme Value Theory: evidence from Pakistan Dr. Abdul Qayyum and Faisal Nawaz Abstract The purpose of the paper is to show some methods of extreme value theory through analysis
More informationQuantitative Introduction ro Risk and Uncertainty in Business Module 5: Hypothesis Testing Examples
Quantitative Introduction ro Risk and Uncertainty in Business Module 5: Hypothesis Testing Examples M. Vidyasagar Cecil & Ida Green Chair The University of Texas at Dallas Email: M.Vidyasagar@utdallas.edu
More informationEffects of Survey Geometry on Power Spectrum Covariance for Cosmic Shear Survey
Effects of Survey Geometry on Power Spectrum Covariance for Cosmic Shear Survey Ryuichi Takahashi (Hirosaki U) with Shunji Soma, Masahiro Takada and Issha Kayo RT+, in preparation Abstract What survey
More informationChapter 3. Numerical Descriptive Measures. Copyright 2016 Pearson Education, Ltd. Chapter 3, Slide 1
Chapter 3 Numerical Descriptive Measures Copyright 2016 Pearson Education, Ltd. Chapter 3, Slide 1 Objectives In this chapter, you learn to: Describe the properties of central tendency, variation, and
More informationIn both the neighbourhood and mean-field models, the mean susceptible density S falls
Supplementary Information Epidemic cholera spreads like wildfire Manojit Roy 1,2, Richard D. Zinck 1+, Menno J. Bouma 3, Mercedes Pascual 1,2 Supplementary note on model behaviour near 2 nd order criticality:
More informationNUMERACY BOOKLET: HELPFUL HINTS
NUMERACY BOOKLET: HELPFUL HINTS ADDITION / SUBTRACTION Column Addition 38 + 26 = 64 38 Start at the right adding the + 26 64 units. Remember to carry over the tens, hundreds etc. Column Subtraction 138-65
More informationIOP 201-Q (Industrial Psychological Research) Tutorial 5
IOP 201-Q (Industrial Psychological Research) Tutorial 5 TRUE/FALSE [1 point each] Indicate whether the sentence or statement is true or false. 1. To establish a cause-and-effect relation between two variables,
More informationAcritical aspect of any capital budgeting decision. Using Excel to Perform Monte Carlo Simulations TECHNOLOGY
Using Excel to Perform Monte Carlo Simulations By Thomas E. McKee, CMA, CPA, and Linda J.B. McKee, CPA Acritical aspect of any capital budgeting decision is evaluating the risk surrounding key variables
More informationImplied Volatility Surface
White Paper Implied Volatility Surface By Amir Akhundzadeh, James Porter, Eric Schneider Originally published 19-Aug-2015. Updated 24-Jan-2017. White Paper Implied Volatility Surface Contents Introduction...
More informationLikelihood-Based Statistical Estimation From Quantized Data
Likelihood-Based Statistical Estimation From Quantized Data by Stephen B. Vardeman * Statistics and IMSE Departments Iowa State University Ames, Iowa vardeman@iastate.edu Chiang-Sheng Lee Department of
More informationOverview/Outline. Moving beyond raw data. PSY 464 Advanced Experimental Design. Describing and Exploring Data The Normal Distribution
PSY 464 Advanced Experimental Design Describing and Exploring Data The Normal Distribution 1 Overview/Outline Questions-problems? Exploring/Describing data Organizing/summarizing data Graphical presentations
More informationNon-Inferiority Tests for the Ratio of Two Proportions
Chapter Non-Inferiority Tests for the Ratio of Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority tests of the ratio in twosample designs in
More informationIn the book Candlesticks, Fibonacci,
TRADING Strategies T h e THREE RISING VA L L E Y S p a t t e r n A series of three lows with specific characteristics marks bullish trend changes. Find out how the pattern has performed in the past and
More informationMAS1403. Quantitative Methods for Business Management. Semester 1, Module leader: Dr. David Walshaw
MAS1403 Quantitative Methods for Business Management Semester 1, 2018 2019 Module leader: Dr. David Walshaw Additional lecturers: Dr. James Waldren and Dr. Stuart Hall Announcements: Written assignment
More informationSTA Module 3B Discrete Random Variables
STA 2023 Module 3B Discrete Random Variables Learning Objectives Upon completing this module, you should be able to 1. Determine the probability distribution of a discrete random variable. 2. Construct
More information1 The ECN module. Note
Version 1.11.0 NOVA ECN tutorial 1 The ECN module The ECN is an optional module for the Autolab PGSTAT. The ECN module provides the means to perform Electrochemical Noise measurements (ECN). Electrochemical
More informationDesigning Price Contracts for Boundedly Rational Customers: Does the Number of Block Matter?
Designing Price Contracts for Boundedly ational Customers: Does the Number of Block Matter? Teck H. Ho University of California, Berkeley Forthcoming, Marketing Science Coauthor: Noah Lim, University of
More informationThe Binomial Distribution
The Binomial Distribution January 31, 2019 Contents The Binomial Distribution The Normal Approximation to the Binomial The Binomial Hypothesis Test Computing Binomial Probabilities in R 30 Problems The
More informationTechnical analysis & Charting The Foundation of technical analysis is the Chart.
Technical analysis & Charting The Foundation of technical analysis is the Chart. Charts Mainly there are 2 types of charts 1. Line Chart 2. Candlestick Chart Line charts A chart shown below is the Line
More informationSpike Statistics. File: spike statistics3.tex JV Stone Psychology Department, Sheffield University, England.
Spike Statistics File: spike statistics3.tex JV Stone Psychology Department, Sheffield University, England. Email: j.v.stone@sheffield.ac.uk November 27, 2007 1 Introduction Why do we need to know about
More informationSome Characteristics of Data
Some Characteristics of Data Not all data is the same, and depending on some characteristics of a particular dataset, there are some limitations as to what can and cannot be done with that data. Some key
More informationA LEVEL MATHEMATICS ANSWERS AND MARKSCHEMES SUMMARY STATISTICS AND DIAGRAMS. 1. a) 45 B1 [1] b) 7 th value 37 M1 A1 [2]
1. a) 45 [1] b) 7 th value 37 [] n c) LQ : 4 = 3.5 4 th value so LQ = 5 3 n UQ : 4 = 9.75 10 th value so UQ = 45 IQR = 0 f.t. d) Median is closer to upper quartile Hence negative skew [] Page 1 . a) Orders
More informationHow I Trade Forex Using the Slope Direction Line
How I Trade Forex Using the Slope Direction Line by Jeff Glenellis Copyright 2009, Simple4xSystem.net By now, you should already have both the Slope Direction Line (S.D.L.) and the Fibonacci Pivot (FiboPiv)
More informationWHS FutureStation - Guide LiveStatistics
WHS FutureStation - Guide LiveStatistics LiveStatistics is a paying module for the WHS FutureStation trading platform. This guide is intended to give the reader a flavour of the phenomenal possibilities
More informationPHYS 1140 lecture 5. Expt. 2: complete report and turn in at end of the third weekday after your lab session. Sign up for your choice of Expt.
Deadlines coming up During lab this week: PHYS 1140 lecture 5 Expt. 2: complete report and turn in at end of the third weekday after your lab session. Sign up for your choice of Expt. 3 Remember the M,
More informationSOUTH ASIA CHARTSPEAK ISSUE 4 MARCH 2015 ISSUE 5, MAR 2015
ISSUE 4 MARCH 2015 CONTENTS FX Technical outlook Pg USDINR Bias is for USD weakness till 63 holds 1 EURUSD Thrust is terminal; 1.07 is major support 2 GBPUSD Approaching the critical 1.4800 area 3 USDJPY
More informationMath 2311 Bekki George Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment
Math 2311 Bekki George bekki@math.uh.edu Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment Class webpage: http://www.math.uh.edu/~bekki/math2311.html Math 2311 Class
More informationDavid Tenenbaum GEOG 090 UNC-CH Spring 2005
Simple Descriptive Statistics Review and Examples You will likely make use of all three measures of central tendency (mode, median, and mean), as well as some key measures of dispersion (standard deviation,
More informationMortality Distributions for Multiple Impairments
Mortality Distributions for Multiple Impairments Jochen Russ Washington D.C., November 2016 www.ifa-ulm.de Agenda Introduction: How do LE-Providers work? Multiple Impairments Examples where Medical Experts
More informationReview: Chebyshev s Rule. Measures of Dispersion II. Review: Empirical Rule. Review: Empirical Rule. Auto Batteries Example, p 59.
Review: Chebyshev s Rule Measures of Dispersion II Tom Ilvento STAT 200 Is based on a mathematical theorem for any data At least ¾ of the measurements will fall within ± 2 standard deviations from the
More informationSolutions for practice questions: Chapter 9, Statistics
Solutions for practice questions: Chapter 9, Statistics If you find any errors, please let me know at mailto:msfrisbie@pfrisbie.com. 1. We know that µ is the mean of 30 values of y, 30 30 i= 1 2 ( y i
More informationINSTITUTE OF ACTUARIES OF INDIA
INSTITUTE OF ACTUARIES OF INDIA EXAMINATIONS 27 th May, 2014 Subject SA3 General Insurance Time allowed: Three hours (14.45* - 18.00 Hours) Total Marks: 100 INSTRUCTIONS TO THE CANDIDATES 1. Please read
More informationNon-Inferiority Tests for the Odds Ratio of Two Proportions
Chapter Non-Inferiority Tests for the Odds Ratio of Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority tests of the odds ratio in twosample
More informationData Analysis and Statistical Methods Statistics 651
Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 10 (MWF) Checking for normality of the data using the QQplot Suhasini Subba Rao Review of previous
More informationCategorical. A general name for non-numerical data; the data is separated into categories of some kind.
Chapter 5 Categorical A general name for non-numerical data; the data is separated into categories of some kind. Nominal data Categorical data with no implied order. Eg. Eye colours, favourite TV show,
More informationThe U.S. and Global Economies
The U.S. and Global Economies 2 When you have completed your study of this chapter, you will be able to 1 Describe what, how, and for whom goods and services are produced in the United States. 2 Describe
More informationAdding longs in the SPX zone will be well-rewarded longer term we believe.
Executive Summary Last week we found, based on our analyses of the charts: Our SPX2146-2069 target zone remains and can now be narrowed down to SPX2117-2069, as the S&P500 closed at SPX2128 yesterday,
More informationExploring Data and Graphics
Exploring Data and Graphics Rick White Department of Statistics, UBC Graduate Pathways to Success Graduate & Postdoctoral Studies November 13, 2013 Outline Summarizing Data Types of Data Visualizing Data
More informationESRC application and success rate data
ESRC application and success rate data This analysis accompanies the most recent release of ESRC success rate data: https://esrc.ukri.org/about-us/performance-information/application-and-award-data/ in
More informationDetermination of manufacturing exports in the euro area countries using a supply-demand model
Determination of manufacturing exports in the euro area countries using a supply-demand model By Ana Buisán, Juan Carlos Caballero and Noelia Jiménez, Directorate General Economics, Statistics and Research
More informationElectro-Optical Characterization Report Device: SiPM MPPC HAMAMATSU S/N. 1 50µm
OSSERVATORIO ASTROFISICO DI CATANIA Electro-Optical Characterization Report Device: SiPM MPPC HAMAMATSU S/N. 1 50µm Osservatorio Astrofisico di Catania G.ROMEO (1),G.BONANNO (1) (1) INAF Osservatorio Astrofisico
More information