DEFINITIVE ANNUAL REPORT Trace elements 2014

Size: px
Start display at page:

Download "DEFINITIVE ANNUAL REPORT Trace elements 2014"

Transcription

1 EXPERTISE, SERVICE PROVISION AND CUSTOMER RELATIONS QUALITY OF MEDICAL LABORATORIES CLINICAL BIOLOGY COMMISSION COMMITTEE OF EXPERTS EXTERNAL QUALITY ASSESSMENT IN CLINICAL BIOLOGY DEFINITIVE ANNUAL REPORT Trace elements 2014 IPH-2014/Trace elements/06 Quality of medical laboratories J. Wytsmanstreet, Brussels Belgium

2 ISSN: COMMITTEE OF EXPERTS IPH (secretariat) TEL: /5522 FAX: Scheme coordinator: TEL: Bernard China Alternate coordinator: TEL: Mohamed Rida Soumali Experts: Desmet Koen Dubois Nathalie Haufroid Vincent Verstraete Alain Committee of experts: 01/07/2015 Authorisation to release report: By Bernard China, scheme coordinator, on 31/07/2015. All the reports are also available on our webpage: FORM 43/125/E V5 (date of application: 17/06/2015). 2/38

3 TABLE OF CONTENTS STATISTICS... 4 GRAPHICAL REPRESENTATION... 5 THE SAMPLES AND THE DATA PROCESSING... 6 PARTICIPATION HEAVY METALS IN SERUM PARTICIPATION GLOBAL RESULTS RESULTS PER ELEMENT Al Cu Li Se Zn Non evaluable elements TRACE ELEMENTS IN BLOOD PARTICIPATION GLOBAL RESULTS RESULTS PER ELEMENT Cd Co Hg Mn Pb Tl Non evaluable elements TRACE ELEMENTS IN URINE PARTICIPATION RESULTS PER ELEMENT As Cd Co Cr Cu Hg Mg Mn Ni Pb Tl Zn Non evaluable elements FORM 43/125/E V5 (date of application: 17/06/2015). 3/38

4 Statistics For this annual report the following statistics were performed. The position of your quantitative results is given in comparison with all the results of all the participants and in comparison with the results of all the participants. The following information is given: Your result (R) The global median (M g ): the central value of the results obtained by all the laboratories confounded for all methods. In this report, the global median is considered as the target value. The global standard deviation (SD g ): It mmeasures of the dispersion of the results obtained by all the laboratories confounded for all methods. SD=(P75-P25)/1.349 The Z score: the difference between your result and the global median of your method, expressed as a Z g = (R - M g ) / SD g. NB : some additional information about the used statistics can be found on our web site : or df FORM 43/125/E V5 (date of application: 17/06/2015). 4/38

5 Graphical representation In addition to the tables of the results is a graphic in "Box et whisker" plot sometimes added. It contains the following elements for the methods with at least 6 participants: a rectangle that ranges from the percentile 25 (P 25 ) to the percentile 75 (P 75 ) a central line that shows the median of the results (P 50 ) a lower limit that shows the smallest value x > P * (P 75 - P 25 ) an upper limit that shows the largest value x < P * (P 75 - P 25 ) all points outside this interval are represented by a dot. x < P * (P 75 -P 25 ) P 75 P 50 P 25 x > P * (P 75 -P 25 ) < value < quantification limit FORM 43/125/E V5 (date of application: 17/06/2015). 5/38

6 The samples and the data processing 24 samples per matrix (Serum, whole blood, urine) were sent to the labs under dry ice (20/03/2014). The samples were purchased at SKML, Winterswijk, Netherlands. Two samples had to be analyzed per month from April 2014 to March The results were encoded via the web page: The laboratories obtained from this site an individual report, a monthly report and an annual report. WIV-ISP produced individual annual reports and this global annual report. Participation 47 Belgian laboratories participated to the EQA (figure 1). 20 laboratories for Serum, blood and urine, 5 laboratoires for Serum and urine, 2 laboratories for blood and urine, 1 laboratory for serum and blood 18 laboratories for serum alone and 1 laboratory for urine alone. Figure 1. repartion (%) of the participants in function of the matrix analysed. FORM 43/125/E V5 (date of application: 17/06/2015). 6/38

7 1. Heavy metals in Serum 1.1. Participation 44 laboratories participated to the EQA for the quantification of trace elements in serum. Table 1.1. Number of participating laboratories per element Element N Recorded Total percentage Labs results results Al Co* Cr* Cu Li Mg* Se Tl* Zn Total *: when n<6, no evaluation was done 1.2. Global results Table 1.2. Global results per element Element Total number Number of of results evaluated results Number of Z citations % citations Al Co NA NA Cr NA NA Cu Li Mg 96 0 NA NA Se Tl 17 0 NA NA Zn Total NA : not applicable In 2014, the overall percentage of Z citation was By comparison this percentage was 6.54 and 5.73 in 2012 and 2013, respectively. Nevertheless, those differences were not significant (p>0.05%). Al seems to be the most frequently cited element among the evaluated ones. FORM 43/125/E V5 (date of application: 17/06/2015). 7/38

8 1.3. Results per element Al Table 1.3. Results per sample for the quantification of Al in serum Sample Median(µg/ L) SD (µg/l) N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 8/38

9 Cu Table 1.4. Results per sample for Cu in serum Sample Median (µg/l) SD (µg/l) N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 9/38

10 Li Values not shown on graph : (33.1) Table 1.5. Results per sample for the quantification of Li in Serum Sample Median (mg/l) SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 10/38

11 Se Values not shown on graph : (408) Table 1.6. Results per sample for the quantification of Se in Serum Sample median SD N Z-citations % ( Total evaluated % FORM 43/125/E V5 (date of application: 17/06/2015). 11/38

12 Zn Values not shown on graph : ; : 1.97 ; : ; : ; : ; Table 1.7. Results per sample for the quantification of Zn in serum Sample median SD N Z-citations (3.6 %) (0 %) (7.1 %) (0 %) (3.6 %) (3.7 %) (0 %) (10.3 %) (0 %) (0 %) (0 %) (0 %) (0 %) (3.6 %) (3.6 %) (3.6 %) (4 %) (4 %) (0 %) (3.7 %) (8 %) (4 %) (3.8 %) (0 %) Total evaluated (2.8 %) FORM 43/125/E V5 (date of application: 17/06/2015). 12/38

13 Non evaluable elements When the number of participants was below 6 for all of the samples, no statistics can be calculated and no evaluation can be done. It is the case in serum for : Co, Cr, Mg and Tl. Nevertheless, the results were plotted in function of the samples (see below). FORM 43/125/E V5 (date of application: 17/06/2015). 13/38

14 FORM 43/125/E V5 (date of application: 17/06/2015). 14/38

15 2. Trace elements in blood 2.1. Participation 23 laboratories encoded results for the quantification of trace elements in whole blood. Table 2.1. Number of Participating laboratories per element in whole blood Element N Labs Recorded results Total results Percentage Cd Co Cr 3* Mg 2* Mn Hg Pb Se 3* Zn 2* Tl total * : when n<6, no evaluation was done 2.2. Global results Table 2.2. Global results per element for quantification in whole blood Elemen Total number Number of Number of Z citations % citations t of results evaluated results Cd Co Cr 72 0 NA NA Hg Mg 48 0 NA NA Mn Pb Se 56 0 NA NA Tl Zn 48 0 NA NA Total NA : not applicable In 2014, the overall percentage of Z citations was By comparison this percentage was 8.89 and 7.82 in 2012 and 2013, respectively. Nevertheless, those differences were not significant (p>0.05%). Cd and Co seem to be the most frequently cited elements (>12%) among the evaluated ones. FORM 43/125/E V5 (date of application: 17/06/2015). 15/38

16 2.3. Results per element Cd Values not shown on graph : (13) Table 2.3. result per sample for the quantification of Cd in blood Sample Median SD N Z-citations % (µg/l) Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 16/38

17 Co Values not shown on graph : (30.8) Table 2.4. results per sample for the quantification of Co in blood Sample Median SD N Z-citations % µg/l NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA Total evaluated NA : not applicable FORM 43/125/E V5 (date of application: 17/06/2015). 17/38

18 Hg Value not shown on graph : (364) Table 2.5. results per sample for the quantification of Hg in blood Sample Median (µg/l) SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 18/38

19 Mn Table 2.6. Results per sample for the quantification of Mn in blood Sample Median SD N Z-citations (µg/l) NA NA 4 NA NA NA NA 4 NA NA NA NA 5 NA NA NA NA 5 NA NA Total evaluated NA : not applicable FORM 43/125/E V5 (date of application: 17/06/2015). 19/38

20 Pb Value not shown on graph : (1200) Table 2.7. Results per sample for the quantification of Pb in whole blood Sample Median (µg/l) SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 20/38

21 Tl Table 2.8. Results per sample for the quantification of Tl in whole blood Sample median SD N Z-citations % NA NA 4 NA NA NA NA 4 NA NA Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 21/38

22 Non evaluable elements When n<6, the statistics where not calculated and no evaluation was performed. In whole blood this was the case for Cr, Mg, Se and Zn. Nevertheless, the individual results are shown here after. FORM 43/125/E V5 (date of application: 17/06/2015). 22/38

23 FORM 43/125/E V5 (date of application: 17/06/2015). 23/38

24 2. Trace elements in urine 3.1. Participation 28 laboratories encoded results for the matrix urine. The percentage of response was 84%. Table 3.1. Participation per element Recorded Parameter N Labs results Total results percentage As Co Cd Cr Cu Se* 4* Hg Mg Mn Ni Pb Tl V* I* Zn Total *: when n<6, no evaluation was performed 3.2. Global results Table 3.2. Global results per element for the quantification in urine Elemen Total number Number of Number of Z citations % citations t of results evaluated results As Cd Co Cr Cu Hg Mg I 44 0 NA NA Mn Ni Pb Se 78 0 NA NA Tl V 69 0 NA NA Zn Total The global percentage of Z citations for urine in 2014 was By comparison, the percentage of Z citations was 9.28 and 9.72 in 2012 and 2013, respectively. Cr seems to be subject to the biggest number of z-citations (14.7%). FORM 43/125/E V5 (date of application: 17/06/2015). 24/38

25 3.3 results per element As. Table 3.3. Results per sample for the quantification of As in urine Sample median SD N Z-citations % NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA Total evaluated 0 NA NA FORM 43/125/E V5 (date of application: 17/06/2015). 25/38

26 Cd Table 3.4. Result per sample for the quantification of Cd in urine Sample median SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 26/38

27 Co Values not shown on graph Table 3.5. Results per sample for the quantification of Co in urine Sample Median (µg/l) SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 27/38

28 3.3.4.Cr Table 3.6. Results per sample for the quantification of Cr in urine Sample median SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 28/38

29 Cu Values not shown on graph : ; : ; : ; Table 3.7. Results per sample for the quantification of Cu in urine Sample median SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 29/38

30 Hg Values not shown on graph : (429) (640) Table 3.8. Results per sample for the quantification of Hg in Urine Sample Median SD N Z-citations % NA NA 5 NA NA NA NA 5 NA NA Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 30/38

31 Mg Values not shown on graph : ; : ; : ; Table 3.9. Results per sample for the quantification of Mg in urine Sample Median SD N Z-citations % NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 31/38

32 Mn n=9 n=6 n=9 n=9 n=6 n=9 n=9 n=7 n=7 n=8 n=7 n= Mn (µg/l) Tableau Results per sample for the quantification of Mn in urine Sample median SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 32/38

33 Ni n=10 n=8 n=9 n=10 n=7 n=9 n=10 n=9 n=7 n=9 n=8 n= Ni (µg/l) Values not shown on graph : (95) and (116.6) Table Results per sample for the quantification of Ni in urine Sample median SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 33/38

34 Pb n=11 n=14 n=14 n=13 n=12 n=12 n=11 n=12 n=12 n=13 n=12 n= Pb (µg/l) Values not shown on graph : (258), (531) Table Results per sample for the quantification of Pb in urine Sample median SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 34/38

35 Tl n=5 n=5 n=6 n=6 n=6 n=6 n=6 n=5 n=5 n=5 n=5 n= Tl (µg/l) Table Results per sample for the quantification of Tl in urine Sample median SD N Z-citations % NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA NA NA 5 NA NA Total evaluated 72 8 FORM 43/125/E V5 (date of application: 17/06/2015). 35/38

36 Zn n=13 n=11 n=10 n=13 n=11 n=13 n=12 n=12 n=10 n=12 n=11 n= Zn (mg/l) Table Results per sample for the quantification of Zn in urine Sample median SD N Z-citations % Total evaluated FORM 43/125/E V5 (date of application: 17/06/2015). 36/38

37 Non evaluable elements When the number of participants is lower than 6, no evaluation was performed. It is the case for I, Se and V in urine. Nevertheless, the individual results are shown on graphs here after. n=3 n=3 n=4 n=3 n=4 n=4 n=4 n=4 n=3 n=3 n=4 n=3 n=4 n=4 n=3 n=3 n=4 n=3 n=3 n=3 n=3 n=2 n=2 n= Se (µg/l) FORM 43/125/E V5 (date of application: 17/06/2015). 37/38

38 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=3 n=2 n=3 n=3 n=3 n=2 n=3 n=2 n= V (µg/l) END. Scientific Institute of Public Health, Brussels This report may not be reproduced, published or distributed without the consent of the WIV-ISP. FORM 43/125/E V5 (date of application: 17/06/2015). 38/38

ISO INTERNATIONAL STANDARD. Water quality Digestion for the determination of selected elements in water Part 2: Nitric acid digestion

ISO INTERNATIONAL STANDARD. Water quality Digestion for the determination of selected elements in water Part 2: Nitric acid digestion INTERNATIONAL STANDARD ISO 15587-2 First edition 2002-03-01 Water quality Digestion for the determination of selected elements in water Part 2: Nitric acid digestion Qualité de l'eau Digestion pour la

More information

Developments in harmonised scoring systems and data presentation 'The ABC of EQA

Developments in harmonised scoring systems and data presentation 'The ABC of EQA UK NEQAS UK NEQAS FOR CLINICAL CHEMISTRY UNITED KINGDOM NATIONAL EXTERNAL QUALITY ASSESSMENT SCHEMES Developments in harmonised scoring systems and data presentation 'The ABC of EQA Dr David G Bullock

More information

Supporting Information. Highly Selective and Reversible Chemosensor for Pd 2+ Detected by Fluorescence, Colorimetry and Test Paper.

Supporting Information. Highly Selective and Reversible Chemosensor for Pd 2+ Detected by Fluorescence, Colorimetry and Test Paper. Supporting Information Highly Selective and Reversible Chemosensor for Pd 2+ Detected by Fluorescence, Colorimetry and Test Paper Mian Wang, Xiaomei Liu, Huizhe Lu, Hongmei Wang* and Zhaohai Qin* Department

More information

B.2 Measures of Central Tendency and Dispersion

B.2 Measures of Central Tendency and Dispersion Appendix B. Measures of Central Tendency and Dispersion B B. Measures of Central Tendency and Dispersion What you should learn Find and interpret the mean, median, and mode of a set of data. Determine

More information

IQC monitoring in laboratory networks

IQC monitoring in laboratory networks IQC for Networked Analysers Background and instructions for use IQC monitoring in laboratory networks Modern Laboratories continue to produce large quantities of internal quality control data (IQC) despite

More information

Chapter 1. Looking at Data-Distribution

Chapter 1. Looking at Data-Distribution Chapter 1. Looking at Data-Distribution Statistics is the scientific discipline that provides methods to draw right conclusions: 1)Collecting the data 2)Describing the data 3)Drawing the conclusions Raw

More information

IMPROVE XRF Analysis SOP 301, Version 2.1 TI 301F-Level I Validation of Monthly XRF data Date: Dec 14, 2015 Page 1 of 7.

IMPROVE XRF Analysis SOP 301, Version 2.1 TI 301F-Level I Validation of Monthly XRF data Date: Dec 14, 2015 Page 1 of 7. Page 1 of 7 Table of Contents 1. PURPOSE AND APPLICABILITY... 2 2. DEFINITION... 2 3. GENERAL GUIDELINES... 3 4. PROCEDURES... 3 4.1 Creating Set on Webapp... 3 4.2 Accessing the XRF data on cl-sql...

More information

Descriptive Statistics, Standard Deviation and Standard Error

Descriptive Statistics, Standard Deviation and Standard Error AP Biology Calculations: Descriptive Statistics, Standard Deviation and Standard Error SBI4UP The Scientific Method & Experimental Design Scientific method is used to explore observations and answer questions.

More information

QQ normality plots Harvey Motulsky, GraphPad Software Inc. July 2013

QQ normality plots Harvey Motulsky, GraphPad Software Inc. July 2013 QQ normality plots Harvey Motulsky, GraphPad Software Inc. July 213 Introduction Many statistical tests assume that data (or residuals) are sampled from a Gaussian distribution. Normality tests are often

More information

Probability and Statistics. Copyright Cengage Learning. All rights reserved.

Probability and Statistics. Copyright Cengage Learning. All rights reserved. Probability and Statistics Copyright Cengage Learning. All rights reserved. 14.5 Descriptive Statistics (Numerical) Copyright Cengage Learning. All rights reserved. Objectives Measures of Central Tendency:

More information

EMQN UPDATE: GERMLINE SCHEMES 2017 USER FEEDBACK SURVEY

EMQN UPDATE: GERMLINE SCHEMES 2017 USER FEEDBACK SURVEY UKAS accredited Proficiency Testing Provider EMQN Office Manchester Centre for Genomic Medicine, 6th Floor, St Mary s Hospital, Oxford Road, Manchester M13 9WL United Kingdom Tel: +44 161 276 6741 Fax:

More information

The National Stormwater Quality Database, Version 3.1

The National Stormwater Quality Database, Version 3.1 R. Pitt March 8, 2011 The National Stormwater Quality Database, Version 3.1 The characteristics of stormwater discharges vary considerably. Geographical area and land use have been identified as important

More information

Test Bank for Privitera, Statistics for the Behavioral Sciences

Test Bank for Privitera, Statistics for the Behavioral Sciences 1. A simple frequency distribution A) can be used to summarize grouped data B) can be used to summarize ungrouped data C) summarizes the frequency of scores in a given category or range 2. To determine

More information

ENDF/B-VII.1 versus ENDFB/-VII.0: What s Different?

ENDF/B-VII.1 versus ENDFB/-VII.0: What s Different? LLNL-TR-548633 ENDF/B-VII.1 versus ENDFB/-VII.0: What s Different? by Dermott E. Cullen Lawrence Livermore National Laboratory P.O. Box 808/L-198 Livermore, CA 94550 March 17, 2012 Approved for public

More information

Metal alloy proficiency testing program

Metal alloy proficiency testing program quality control laboratory compliance accreditation performance monitoring method validation quality control laboratory compliance Metal alloy proficiency testing program LGC Quality ISO Guide 34 GMP/GLP

More information

IT 403 Practice Problems (1-2) Answers

IT 403 Practice Problems (1-2) Answers IT 403 Practice Problems (1-2) Answers #1. Using Tukey's Hinges method ('Inclusionary'), what is Q3 for this dataset? 2 3 5 7 11 13 17 a. 7 b. 11 c. 12 d. 15 c (12) #2. How do quartiles and percentiles

More information

10.4 Measures of Central Tendency and Variation

10.4 Measures of Central Tendency and Variation 10.4 Measures of Central Tendency and Variation Mode-->The number that occurs most frequently; there can be more than one mode ; if each number appears equally often, then there is no mode at all. (mode

More information

10.4 Measures of Central Tendency and Variation

10.4 Measures of Central Tendency and Variation 10.4 Measures of Central Tendency and Variation Mode-->The number that occurs most frequently; there can be more than one mode ; if each number appears equally often, then there is no mode at all. (mode

More information

Chapter 3 - Displaying and Summarizing Quantitative Data

Chapter 3 - Displaying and Summarizing Quantitative Data Chapter 3 - Displaying and Summarizing Quantitative Data 3.1 Graphs for Quantitative Data (LABEL GRAPHS) August 25, 2014 Histogram (p. 44) - Graph that uses bars to represent different frequencies or relative

More information

Quality of Semiconductor Raw Materials: Evolution and Challenges. Yongqiang Lu Kevin McLaughlin

Quality of Semiconductor Raw Materials: Evolution and Challenges. Yongqiang Lu Kevin McLaughlin Quality of Semiconductor Raw Materials: Evolution and Challenges Yongqiang Lu Kevin McLaughlin Outline Advance of Fab technologies and the evolution of raw materials for ever higher quality Challenges:

More information

Measures of Position

Measures of Position Measures of Position In this section, we will learn to use fractiles. Fractiles are numbers that partition, or divide, an ordered data set into equal parts (each part has the same number of data entries).

More information

TEST REPORT : SCENTCO LIMITED. : China : USA, EU, ASIA. : 16-May EN71 Part 3:2013 Migration of Certain Elements Pass

TEST REPORT : SCENTCO LIMITED. : China : USA, EU, ASIA. : 16-May EN71 Part 3:2013 Migration of Certain Elements Pass TEST REPORT Page : 1 of 10 APPLICANT ADDRESS : SCENTCO LIMITED : RM 401, 4/F, SUNG KEE IND. BLDH., 18 KWAI TING ROAD, KWAI CHUNG, N.T., HONG KONG SAMPLE DESCRIPTION VENDOR COUNTRY OF ORIGIN COUNTRY OF

More information

Day 4 Percentiles and Box and Whisker.notebook. April 20, 2018

Day 4 Percentiles and Box and Whisker.notebook. April 20, 2018 Day 4 Box & Whisker Plots and Percentiles In a previous lesson, we learned that the median divides a set a data into 2 equal parts. Sometimes it is necessary to divide the data into smaller more precise

More information

Prepare a stem-and-leaf graph for the following data. In your final display, you should arrange the leaves for each stem in increasing order.

Prepare a stem-and-leaf graph for the following data. In your final display, you should arrange the leaves for each stem in increasing order. Chapter 2 2.1 Descriptive Statistics A stem-and-leaf graph, also called a stemplot, allows for a nice overview of quantitative data without losing information on individual observations. It can be a good

More information

Box and Whisker Plot Review A Five Number Summary. October 16, Box and Whisker Lesson.notebook. Oct 14 5:21 PM. Oct 14 5:21 PM.

Box and Whisker Plot Review A Five Number Summary. October 16, Box and Whisker Lesson.notebook. Oct 14 5:21 PM. Oct 14 5:21 PM. Oct 14 5:21 PM Oct 14 5:21 PM Box and Whisker Plot Review A Five Number Summary Activities Practice Labeling Title Page 1 Click on each word to view its definition. Outlier Median Lower Extreme Upper Extreme

More information

NATIONAL BOARD OF MEDICAL EXAMINERS Subject Examination Program. Introduction to Clinical Diagnosis Examination Paper Version

NATIONAL BOARD OF MEDICAL EXAMINERS Subject Examination Program. Introduction to Clinical Diagnosis Examination Paper Version NATIONAL BOARD OF MEDICAL EXAMINERS Examination Paper Version Score Interpretation Guide NBME subject examinations provide medical schools with a tool for measuring examinees' understanding of the basic

More information

Middle Years Data Analysis Display Methods

Middle Years Data Analysis Display Methods Middle Years Data Analysis Display Methods Double Bar Graph A double bar graph is an extension of a single bar graph. Any bar graph involves categories and counts of the number of people or things (frequency)

More information

COMMITTEE FOR PROPRIETARY MEDICINAL PRODUCTS (CPMP) GUIDELINE ON REQUIREMENTS FOR PLASMA MASTER FILE (PMF) CERTIFICATION

COMMITTEE FOR PROPRIETARY MEDICINAL PRODUCTS (CPMP) GUIDELINE ON REQUIREMENTS FOR PLASMA MASTER FILE (PMF) CERTIFICATION The European Agency for the Evaluation of Medicinal Products Evaluation of Medicines for Human Use London, 26 February 2004 COMMITTEE FOR PROPRIETARY MEDICINAL PRODUCTS (CPMP) GUIDELINE ON REQUIREMENTS

More information

Numerical Summaries of Data Section 14.3

Numerical Summaries of Data Section 14.3 MATH 11008: Numerical Summaries of Data Section 14.3 MEAN mean: The mean (or average) of a set of numbers is computed by determining the sum of all the numbers and dividing by the total number of observations.

More information

The main issue is that the mean and standard deviations are not accurate and should not be used in the analysis. Then what statistics should we use?

The main issue is that the mean and standard deviations are not accurate and should not be used in the analysis. Then what statistics should we use? Chapter 4 Analyzing Skewed Quantitative Data Introduction: In chapter 3, we focused on analyzing bell shaped (normal) data, but many data sets are not bell shaped. How do we analyze quantitative data when

More information

The first few questions on this worksheet will deal with measures of central tendency. These data types tell us where the center of the data set lies.

The first few questions on this worksheet will deal with measures of central tendency. These data types tell us where the center of the data set lies. Instructions: You are given the following data below these instructions. Your client (Courtney) wants you to statistically analyze the data to help her reach conclusions about how well she is teaching.

More information

Univariate Statistics Summary

Univariate Statistics Summary Further Maths Univariate Statistics Summary Types of Data Data can be classified as categorical or numerical. Categorical data are observations or records that are arranged according to category. For example:

More information

95 th Percentile Billing

95 th Percentile Billing 95 th Percentile Billing Amie Elcan, CenturyLink Principal Architect, Data Strategy and Development amie.elcan@centurylink.com Nanog53 Philadelphia, PA October 10, 2011 Outline Internet access usage trends

More information

NOTIFICATION TO THE PARTIES

NOTIFICATION TO THE PARTIES CONVENTION ON INTERNATIONAL TRADE IN ENDANGERED SPECIES OF WILD FAUNA AND FLORA NOTIFICATION TO THE PARTIES No. 2014/035 Geneva, 4 August 2014 CONCERNING: Needs assessment for strengthening the implementation

More information

IPR Äspö Hard Rock Laboratory. International Progress Report. TRUE Block Scale project. Summary of chemical data December 2002

IPR Äspö Hard Rock Laboratory. International Progress Report. TRUE Block Scale project. Summary of chemical data December 2002 International Progress Report IPR-03-25 Äspö Hard Rock Laboratory TRUE Block Scale project Summary of chemical data December 2002 Jeanette Carmström SKB April 2003 Svensk Kärnbränslehantering AB Swedish

More information

Feto-Maternal Haemorrhage - Web return of results

Feto-Maternal Haemorrhage - Web return of results Logging on Go to http://www.ukneqasbtlp.org and click on the main orange section of the page as shown in figure 1. A list of exercise types will be shown, click on the appropriate exercise to be taken

More information

The Pyramid Table. The Four Matrices; Top Level, Matrix; x = 1. Matrix; x = 2

The Pyramid Table. The Four Matrices; Top Level, Matrix; x = 1. Matrix; x = 2 The Janet Periodic Table (first printed 1928) is also known as the Left Step Table. The Janet table orders the elements according to the filling sequence of the atom. This table may be re-organized as

More information

Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics Chapter 2: Descriptive Statistics Student Learning Outcomes By the end of this chapter, you should be able to: Display data graphically and interpret graphs: stemplots, histograms and boxplots. Recognize,

More information

TEST REPORT MATERIAL SUBMITTED IMPORTER COMPANY MODEL NO SUPPLIER REFERENCE BUYER : : : :

TEST REPORT MATERIAL SUBMITTED IMPORTER COMPANY MODEL NO SUPPLIER REFERENCE BUYER : : : : TEST REPORT AB-0505-T MATERIAL SUBMITTED IMPORTER COMPANY MODEL NO SUPPLIER REFERENCE BUYER LAB LOCATIONTURKEY LAB NO. (7217)265-0049 SERVICE TYPE Regular DATE IN September 22 th, 2017 DATE OUT October

More information

Stat 428 Autumn 2006 Homework 2 Solutions

Stat 428 Autumn 2006 Homework 2 Solutions Section 6.3 (5, 8) 6.3.5 Here is the Minitab output for the service time data set. Descriptive Statistics: Service Times Service Times 0 69.35 1.24 67.88 17.59 28.00 61.00 66.00 Variable Q3 Maximum Service

More information

Box Plots. OpenStax College

Box Plots. OpenStax College Connexions module: m46920 1 Box Plots OpenStax College This work is produced by The Connexions Project and licensed under the Creative Commons Attribution License 3.0 Box plots (also called box-and-whisker

More information

Week 2: Frequency distributions

Week 2: Frequency distributions Types of data Health Sciences M.Sc. Programme Applied Biostatistics Week 2: distributions Data can be summarised to help to reveal information they contain. We do this by calculating numbers from the data

More information

Vocabulary. 5-number summary Rule. Area principle. Bar chart. Boxplot. Categorical data condition. Categorical variable.

Vocabulary. 5-number summary Rule. Area principle. Bar chart. Boxplot. Categorical data condition. Categorical variable. 5-number summary 68-95-99.7 Rule Area principle Bar chart Bimodal Boxplot Case Categorical data Categorical variable Center Changing center and spread Conditional distribution Context Contingency table

More information

MAT 110 WORKSHOP. Updated Fall 2018

MAT 110 WORKSHOP. Updated Fall 2018 MAT 110 WORKSHOP Updated Fall 2018 UNIT 3: STATISTICS Introduction Choosing a Sample Simple Random Sample: a set of individuals from the population chosen in a way that every individual has an equal chance

More information

2. Solve for n. 3. Express as a decimal.

2. Solve for n. 3. Express as a decimal. Name Assessment Date. Which equation can be used to solve the problem? On a trip to the beach, the Jacobsen family covered mi in h. What was the rate in miles per hour of their trip? x A. = B. x = C. x

More information

NEWCASTLE CLINICAL TRIALS UNIT STANDARD OPERATING PROCEDURES

NEWCASTLE CLINICAL TRIALS UNIT STANDARD OPERATING PROCEDURES SOP details SOP title: Protocol development SOP number: TM 010 SOP category: Trial Management Version number: 03 Version date: 16 December 2016 Effective date: 16 January 2017 Revision due date: 16 January

More information

Temperature Calculation of Pellet Rotary Kiln Based on Texture

Temperature Calculation of Pellet Rotary Kiln Based on Texture Intelligent Control and Automation, 2017, 8, 67-74 http://www.scirp.org/journal/ica ISSN Online: 2153-0661 ISSN Print: 2153-0653 Temperature Calculation of Pellet Rotary Kiln Based on Texture Chunli Lin,

More information

Stat 528 (Autumn 2008) Density Curves and the Normal Distribution. Measures of center and spread. Features of the normal distribution

Stat 528 (Autumn 2008) Density Curves and the Normal Distribution. Measures of center and spread. Features of the normal distribution Stat 528 (Autumn 2008) Density Curves and the Normal Distribution Reading: Section 1.3 Density curves An example: GRE scores Measures of center and spread The normal distribution Features of the normal

More information

2.1: Frequency Distributions and Their Graphs

2.1: Frequency Distributions and Their Graphs 2.1: Frequency Distributions and Their Graphs Frequency Distribution - way to display data that has many entries - table that shows classes or intervals of data entries and the number of entries in each

More information

Acquisition Description Exploration Examination Understanding what data is collected. Characterizing properties of data.

Acquisition Description Exploration Examination Understanding what data is collected. Characterizing properties of data. Summary Statistics Acquisition Description Exploration Examination what data is collected Characterizing properties of data. Exploring the data distribution(s). Identifying data quality problems. Selecting

More information

GEOPT19 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES REPORT ON ROUND 19 / July 2006 (Gabbro, MGR-N)

GEOPT19 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES REPORT ON ROUND 19 / July 2006 (Gabbro, MGR-N) GEOPT19 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES REPORT ON ROUND 19 / July 2006 (Gabbro, MGR-N) Peter C. Webb 1 *, Michael Thompson 2, Philip J. Potts 1 and B. Batjargal

More information

Using a percent or a letter grade allows us a very easy way to analyze our performance. Not a big deal, just something we do regularly.

Using a percent or a letter grade allows us a very easy way to analyze our performance. Not a big deal, just something we do regularly. GRAPHING We have used statistics all our lives, what we intend to do now is formalize that knowledge. Statistics can best be defined as a collection and analysis of numerical information. Often times we

More information

EMSL Analytical, Inc.

EMSL Analytical, Inc. EMSL Analytical, Inc. Phone: (856) 303-2500 Fax: (856) 858-4571 Email: 10/21/2016 The following analytical report covers the analysis performed on samples submitted to EMSL Analytical, Inc. on 10/7/2016.

More information

APPENDIX C. Chain of Custody Forms

APPENDIX C. Chain of Custody Forms APPENDX C Chain of Custody Forms CHAN OF CUSTODY Servkes AE,rpo OWdL 1317 South 13th Ave. Kelso, WA 98626 (360> 5777222 (800) 6957222x07 FAX (360) 6361068 SR#: PAGE _ OF 000# PROJECT NAME,'r j PROJECTNMBER

More information

Chapter 5snow year.notebook March 15, 2018

Chapter 5snow year.notebook March 15, 2018 Chapter 5: Statistical Reasoning Section 5.1 Exploring Data Measures of central tendency (Mean, Median and Mode) attempt to describe a set of data by identifying the central position within a set of data

More information

Analysis of part B GMO deliberate release field trials management in Member States and prevention of accidental entry into the marketplace

Analysis of part B GMO deliberate release field trials management in Member States and prevention of accidental entry into the marketplace Analysis of part B GMO deliberate release field trials management in Member States and prevention of accidental entry into the marketplace A study for EC DG Environment under research tender ENV.B.3/ETU/2007/0008

More information

ClimDex - Version 1.3. User's Guide

ClimDex - Version 1.3. User's Guide ClimDex - Version 1.3 User's Guide June, 2001 TABLE OF CONTENTS I. Introduction II. III. IV. Quality Control Homogeneity Testing Calculate Indices V. Region Analysis Appendix A: List of Climate Indices

More information

COMMISSION IMPLEMENTING DECISION (EU)

COMMISSION IMPLEMENTING DECISION (EU) L 127/32 18.5.2016 COMMISSION IMPLEMTING DECISION (EU) 2016/770 of 14 April 2016 establishing a common format for the submission of information concerning the operation of the procedures pursuant to Regulation

More information

Capacity Building. Highlights in 2013

Capacity Building. Highlights in 2013 Capacity Building Highlights in 2013 Considerable increase in capacity building activities of the Commission Offering a total of 20 weeks of NDC analyst training courses Launching the first purely e-learning

More information

XRF for Regulatory Programs RoHS and Consumer Safety Compliance

XRF for Regulatory Programs RoHS and Consumer Safety Compliance Compact X-ray Fluorescence Xpert XRF for Regulatory Programs RoHS and Consumer Safety Compliance RoHS CPSIA EN71-3 Proposition 65 The Xpert for Regulatory Programs Consumer Safety and RoHS Compliance The

More information

Feto-Maternal Haemorrhage - Web return of results

Feto-Maternal Haemorrhage - Web return of results UK NEQAS (BTLP) PO Box 133 WATFORD WD18 0WP T: + 44 (0)1923 217933 F: + 44 (0)1923 217934 E: btlp@ukneqas.org.uk W: www.ukneqasbtlp.org Feto-Maternal Haemorrhage - Web return of results Logging on Go to

More information

Chapter 6. THE NORMAL DISTRIBUTION

Chapter 6. THE NORMAL DISTRIBUTION Chapter 6. THE NORMAL DISTRIBUTION Introducing Normally Distributed Variables The distributions of some variables like thickness of the eggshell, serum cholesterol concentration in blood, white blood cells

More information

Ch3 E lement Elemen ar tary Descriptive Descriptiv Statistics

Ch3 E lement Elemen ar tary Descriptive Descriptiv Statistics Ch3 Elementary Descriptive Statistics Section 3.1: Elementary Graphical Treatment of Data Before doing ANYTHING with data: Understand the question. An approximate answer to the exact question is always

More information

Manage your environmental monitoring data with power, depth and ease

Manage your environmental monitoring data with power, depth and ease EQWin Software Inc. PO Box 75106 RPO White Rock Surrey BC V4A 0B1 Canada Tel: +1 (604) 669-5554 Fax: +1 (888) 620-7140 E-mail: support@eqwinsoftware.com www.eqwinsoftware.com EQWin 7 Manage your environmental

More information

STP 226 ELEMENTARY STATISTICS NOTES PART 2 - DESCRIPTIVE STATISTICS CHAPTER 3 DESCRIPTIVE MEASURES

STP 226 ELEMENTARY STATISTICS NOTES PART 2 - DESCRIPTIVE STATISTICS CHAPTER 3 DESCRIPTIVE MEASURES STP 6 ELEMENTARY STATISTICS NOTES PART - DESCRIPTIVE STATISTICS CHAPTER 3 DESCRIPTIVE MEASURES Chapter covered organizing data into tables, and summarizing data with graphical displays. We will now use

More information

Math 120 Introduction to Statistics Mr. Toner s Lecture Notes 3.1 Measures of Central Tendency

Math 120 Introduction to Statistics Mr. Toner s Lecture Notes 3.1 Measures of Central Tendency Math 1 Introduction to Statistics Mr. Toner s Lecture Notes 3.1 Measures of Central Tendency lowest value + highest value midrange The word average: is very ambiguous and can actually refer to the mean,

More information

Chapter 5. Normal. Normal Curve. the Normal. Curve Examples. Standard Units Standard Units Examples. for Data

Chapter 5. Normal. Normal Curve. the Normal. Curve Examples. Standard Units Standard Units Examples. for Data curve Approximation Part II Descriptive Statistics The Approximation Approximation The famous normal curve can often be used as an 'ideal' histogram, to which histograms for data can be compared. Its equation

More information

3 Graphical Displays of Data

3 Graphical Displays of Data 3 Graphical Displays of Data Reading: SW Chapter 2, Sections 1-6 Summarizing and Displaying Qualitative Data The data below are from a study of thyroid cancer, using NMTR data. The investigators looked

More information

Telometric 1.2 User s Guide. Jeffrey Grant Fox Chase Cancer Center Philadelphia, PA USA

Telometric 1.2 User s Guide. Jeffrey Grant Fox Chase Cancer Center Philadelphia, PA USA Telometric 1.2 User s Guide Jeffrey Grant Fox Chase Cancer Center Philadelphia, PA USA 20 May, 2002 Contents 1 Introduction 3 1.1 About Telometric.......................... 3 1.2 Java Preliminaries..........................

More information

LESSON 3: CENTRAL TENDENCY

LESSON 3: CENTRAL TENDENCY LESSON 3: CENTRAL TENDENCY Outline Arithmetic mean, median and mode Ungrouped data Grouped data Percentiles, fractiles, and quartiles Ungrouped data Grouped data 1 MEAN Mean is defined as follows: Sum

More information

Averages and Variation

Averages and Variation Averages and Variation 3 Copyright Cengage Learning. All rights reserved. 3.1-1 Section 3.1 Measures of Central Tendency: Mode, Median, and Mean Copyright Cengage Learning. All rights reserved. 3.1-2 Focus

More information

STA 570 Spring Lecture 5 Tuesday, Feb 1

STA 570 Spring Lecture 5 Tuesday, Feb 1 STA 570 Spring 2011 Lecture 5 Tuesday, Feb 1 Descriptive Statistics Summarizing Univariate Data o Standard Deviation, Empirical Rule, IQR o Boxplots Summarizing Bivariate Data o Contingency Tables o Row

More information

Chapter 6. THE NORMAL DISTRIBUTION

Chapter 6. THE NORMAL DISTRIBUTION Chapter 6. THE NORMAL DISTRIBUTION Introducing Normally Distributed Variables The distributions of some variables like thickness of the eggshell, serum cholesterol concentration in blood, white blood cells

More information

So il Geochem ica l Ba seline and Env ironm en ta l Background Va lues of Agr icultura l Reg ion s in Zhejiang Prov ince.

So il Geochem ica l Ba seline and Env ironm en ta l Background Va lues of Agr icultura l Reg ion s in Zhejiang Prov ince. 2007, 23 (2) : 81-88 Journal of Ecology and Rural Environm ent 1, 1, 2, 1 (1., 311203;, 065000) 2. :,, 13,,, 52(), : ; ; ; : P632; X8; P59 : A: 1673-4831 (2007) 02-0081 - 08 So il Geochem ica l Ba seline

More information

Detecting Polytomous Items That Have Drifted: Using Global Versus Step Difficulty 1,2. Xi Wang and Ronald K. Hambleton

Detecting Polytomous Items That Have Drifted: Using Global Versus Step Difficulty 1,2. Xi Wang and Ronald K. Hambleton Detecting Polytomous Items That Have Drifted: Using Global Versus Step Difficulty 1,2 Xi Wang and Ronald K. Hambleton University of Massachusetts Amherst Introduction When test forms are administered to

More information

Homework Packet Week #3

Homework Packet Week #3 Lesson 8.1 Choose the term that best completes statements # 1-12. 10. A data distribution is if the peak of the data is in the middle of the graph. The left and right sides of the graph are nearly mirror

More information

Paper SAS Taming the Rule. Charlotte Crain, Chris Upton, SAS Institute Inc.

Paper SAS Taming the Rule. Charlotte Crain, Chris Upton, SAS Institute Inc. ABSTRACT Paper SAS2620-2016 Taming the Rule Charlotte Crain, Chris Upton, SAS Institute Inc. When business rules are deployed and executed--whether a rule is fired or not if the rule-fire outcomes are

More information

VI... VIII

VI... VIII - 23 2017 2017 ... VI... VIII... 1 1... 2... 2 1.1... 2 1.1.1... 2 1.1.2... 9 1.1.3... 13 1.1.4... 25 1.1.5... 29 1.1.6... 34 1.1.7... 37 1.1.8... 42 1.2... 47 1.2.1... 47 1.2.2 -... 49 1.2.3... 50 1.2.4

More information

LAB 1 INSTRUCTIONS DESCRIBING AND DISPLAYING DATA

LAB 1 INSTRUCTIONS DESCRIBING AND DISPLAYING DATA LAB 1 INSTRUCTIONS DESCRIBING AND DISPLAYING DATA This lab will assist you in learning how to summarize and display categorical and quantitative data in StatCrunch. In particular, you will learn how to

More information

Using the Bruker Tracer III-SD Handheld X-Ray Fluorescence Spectrometer using PC Software for Data Collection

Using the Bruker Tracer III-SD Handheld X-Ray Fluorescence Spectrometer using PC Software for Data Collection Using the Bruker Tracer III-SD Handheld X-Ray Fluorescence Spectrometer using PC Software for Data Collection Scott A Speakman, Ph.D Center for Materials Science and Engineering at MIT For assistance in

More information

4D Magnetic Resonance Analysis. MR 4D Flow. Visualization and Quantification of Aortic Blood Flow

4D Magnetic Resonance Analysis. MR 4D Flow. Visualization and Quantification of Aortic Blood Flow 4D Magnetic Resonance Analysis MR 4D Flow Visualization and Quantification of Aortic Blood Flow 4D Magnetic Resonance Analysis Complete assesment of your MR 4D Flow data Time-efficient and intuitive analysis

More information

Waleed Pervaiz CSE 352

Waleed Pervaiz CSE 352 Waleed Pervaiz CSE 352 Classification Tool: WEKA Waikato Environment for Knowledge Analysis by The University of Waikato. Available on the internet at: http://www.cs.waikato.ac.nz/~ml/weka/index.html The

More information

P A T I E N T C E N T E R E D M E D I C A L H O M E ( P C M H ) A T T E S T A T I O N O F F A C I L I T Y C O M P L I A N C E

P A T I E N T C E N T E R E D M E D I C A L H O M E ( P C M H ) A T T E S T A T I O N O F F A C I L I T Y C O M P L I A N C E P A T I E N T C E N T E R E D M E D I C A L H O M E ( P C M H ) A T T E S T A T I O N O F F A C I L I T Y C O M P L I A N C E State of Wyoming, Department of Health, Division of Healthcare Financing 2015

More information

Myeloma XI. The telephone 24hr randomisation service will remain open throughout this period.

Myeloma XI. The telephone 24hr randomisation service will remain open throughout this period. Myeloma XI Continued success for trial! I N S I D E T H I S I S S U E : Easter opening times Recruitment update Easter drug orders Protocol amendment Sites update Data management Central laboratory results

More information

3. Data Analysis and Statistics

3. Data Analysis and Statistics 3. Data Analysis and Statistics 3.1 Visual Analysis of Data 3.2.1 Basic Statistics Examples 3.2.2 Basic Statistical Theory 3.3 Normal Distributions 3.4 Bivariate Data 3.1 Visual Analysis of Data Visual

More information

Developing Schemas for the Location of Common Web Objects

Developing Schemas for the Location of Common Web Objects SURL Home > Usability News Developing Schemas for the Location of Common Web Objects By Michael Bernard An essential ingredient in constructing the content of a website is knowing the typical users' mental

More information

Patient Information Security

Patient Information Security Patient Information Security An overview of practice and procedure UK CAB Meeting 13th April 2012 Nathan Lea Senior Research Associate CHIME, UCL Overview - Questions that have been asked What happens

More information

Design of Experiments

Design of Experiments Seite 1 von 1 Design of Experiments Module Overview In this module, you learn how to create design matrices, screen factors, and perform regression analysis and Monte Carlo simulation using Mathcad. Objectives

More information

Measures of Dispersion

Measures of Dispersion Lesson 7.6 Objectives Find the variance of a set of data. Calculate standard deviation for a set of data. Read data from a normal curve. Estimate the area under a curve. Variance Measures of Dispersion

More information

AGENCE ESANTÉ IHE CONNECTATHON 2015

AGENCE ESANTÉ IHE CONNECTATHON 2015 AGENCE ESANTÉ IHE CONNECTATHON 2015 Luxembourg, 10 Aoril 2014 You are here In the Centre of Europe «Tout n'est pas une question de taille» "Small is beautiful too» Source: Map from www.epsos.eu «Petit

More information

Frequency Distributions

Frequency Distributions Displaying Data Frequency Distributions After collecting data, the first task for a researcher is to organize and summarize the data so that it is possible to get a general overview of the results. Remember,

More information

* * TEST REPORT. Bilgi Dağ. Kitap Kırt. ve Büro Malz. Tic. Ltd. Şti. Page 1 of 5 APPLICANT NAME

* * TEST REPORT. Bilgi Dağ. Kitap Kırt. ve Büro Malz. Tic. Ltd. Şti. Page 1 of 5 APPLICANT NAME TEST REPORT Page 1 of 5 REPORT NUMBER : APPLICANT NAME ADDRESS SAMPLE DESCRIPTION : BUYER : TURA170099487 Bilgi Dağ. Kitap Kırt. ve Büro Malz. Tic. Ltd. Şti. Merkez Mah. 29 Ekim Cad.No:53 Bahçelievler/İstanbul

More information

Chapter 6: DESCRIPTIVE STATISTICS

Chapter 6: DESCRIPTIVE STATISTICS Chapter 6: DESCRIPTIVE STATISTICS Random Sampling Numerical Summaries Stem-n-Leaf plots Histograms, and Box plots Time Sequence Plots Normal Probability Plots Sections 6-1 to 6-5, and 6-7 Random Sampling

More information

MIPK (Model Independent Pharmaco-Kinetics)

MIPK (Model Independent Pharmaco-Kinetics) MIPK (Model Independent Pharmaco-Kinetics) Martin Micbael SAS Institute Mancbester, UK Introduction MlPK is the working name of a SAS/AF application, currently under development in SAS Institute, designed

More information

Clustering and Visualisation of Data

Clustering and Visualisation of Data Clustering and Visualisation of Data Hiroshi Shimodaira January-March 28 Cluster analysis aims to partition a data set into meaningful or useful groups, based on distances between data points. In some

More information

San Joaquin County Emergency Medical Services Agency

San Joaquin County Emergency Medical Services Agency San Joaquin County Emergency Medical Services Agency http://www.sjgov.org/ems Memorandum TO: All Interested Parties FROM: Rick Jones, EMS Analyst DATE: January, 19 Mailing Address PO Box French Camp, CA

More information

New Generation Explorer Handheld XRF. EXPLORER 7000 Mineral Ore Analyzer

New Generation Explorer Handheld XRF. EXPLORER 7000 Mineral Ore Analyzer New Generation Explorer Handheld XRF EXPLORER 7000 Mineral Ore Analyzer EXPLORER 7000 Mineral Ore XRF Based on ten-year research and development experience in Handheld X-Ray Fluorescense, Skyray Instruments

More information

2.1 Objectives. Math Chapter 2. Chapter 2. Variable. Categorical Variable EXPLORING DATA WITH GRAPHS AND NUMERICAL SUMMARIES

2.1 Objectives. Math Chapter 2. Chapter 2. Variable. Categorical Variable EXPLORING DATA WITH GRAPHS AND NUMERICAL SUMMARIES EXPLORING DATA WITH GRAPHS AND NUMERICAL SUMMARIES Chapter 2 2.1 Objectives 2.1 What Are the Types of Data? www.managementscientist.org 1. Know the definitions of a. Variable b. Categorical versus quantitative

More information

Boxplots. Lecture 17 Section Robb T. Koether. Hampden-Sydney College. Wed, Feb 10, 2010

Boxplots. Lecture 17 Section Robb T. Koether. Hampden-Sydney College. Wed, Feb 10, 2010 Boxplots Lecture 17 Section 5.3.3 Robb T. Koether Hampden-Sydney College Wed, Feb 10, 2010 Robb T. Koether (Hampden-Sydney College) Boxplots Wed, Feb 10, 2010 1 / 34 Outline 1 Boxplots TI-83 Boxplots 2

More information

Chapter 3 Analyzing Normal Quantitative Data

Chapter 3 Analyzing Normal Quantitative Data Chapter 3 Analyzing Normal Quantitative Data Introduction: In chapters 1 and 2, we focused on analyzing categorical data and exploring relationships between categorical data sets. We will now be doing

More information