GEO GRA P HICAL RESEA RCH

Size: px
Start display at page:

Download "GEO GRA P HICAL RESEA RCH"

Transcription

1 GEO GRA P HICAL RESEA RCH Vol125, No13 May, , 1,2, 1, 3, 1, 1,2 (11, ; 21, ) : ,, mg/ kg mg/ kg, ;, 4104 mg/ d, : ; ; ; ; : (2006) [1,2 ] [3,4 ], [5 8 ] [9 14 ] [ ] [18 ], [19 ] [20 ],,, 1 111,, (1) : i = m ai ai i = 1 N (1) : ; : : ( ) ( ) : (19742),, 3 : (19632),, E2mail

2 440 25, i, ai, m, N ( t) 7419 % [5,21 ],,,,, h, HNO32HClO4, [2 ] ( GSV - 3) 112 ( 1) 5, 100 [ 5 ] U SEPA 3050B [22 ], [2 ] ( GSS - 1) 113 Arc GIS, SPSS, Miitab Box2Cox [23 ], Shapiro2Wilk ( PS2W) Z Box2Cox Box2Cox, z = y + 1, z Box2Cox, y Box2Cox, 114,, (2) : Z = Q i =1 1 Fig11 A sketch map showig samplig sites of vegetables take f rom Beijig ( XB- Ci N2 i N 2 i, Z ; Q ; i =1 ) (2) X B - Ci

3 3 : 441 Box2Cox ; N ; (3) : A = i = 1 ( ai N 2 i N 2 i i = 1 ) (3), A, ai, N i ; , ( 2186), ( p = 01015) ( 1) Box2Cox, ( p = 01277), ( 5916 mg/ kg 5715mg/ kg [24 ] ), ( P 2 = 01000), Fig12 Distributio characteristics of, soil Z i farm fields of Beijig (307 mg/ kg), [25 ] (p H > 715) II ( ) (300mg/ kg) ( 2) [26 ] mg/ kg, 5 % 30 % [ 27 ] ( ) [28 ] [3,19 ] 1 Tab11 Cocetratios ad distributio characteristics of soil Z from farm f ields i Beijig (mg/ kg) PS2W Box2Cox ( 2) (20 mg/ kg [29 ] ) (4010 mg/ kg) (5010 mg/ kg) [2 ], 0 %

4 Tab12 Cotets of Z i vegetables collected from farm f ields ad markets of Beijig (mg/ kg, ) Box2Cox ( ) PS - W ( ) PS - W (11 27) (1180) A (21 54) (2123) (11 59) (2131) ( ) B (11 31) (1169) C (11 69) (2107) (11 48) (1156) ( ) D (01 79) (1136) (01 64) (1153) (01 40) (1132) (11 51) (1184) E (01 99) (1150) (31 62) (2190) (61 81) (4111) (21 55) (1177) (11 74) (1195) (01 53) (1151) (11 13) (1148) (01 38) (1117) (21 16) (2118) F (11 77) (2100) (21 35) (2112) (11 05) (2178) (01 11) (1105) (01 76) ( 11 74) (11 79) (2115) G (01 44) (1160) (01 59) (1153) H (21 48) (2125) I (11 99) (2136) (01 39) (1160) (31 08) (3183) (11 52) (1176) (61 33) (2130) (01 28) (1111) (31 70) (3114) J (11 21) (2111) K (11 09) (1173) (01 56) (1160) (11 32) (1181) (11 18) (1147) (11 24) (2104) (11 67) (1163) (21 73) (2131) (01 88) (1152) (11 40) (2103) L (21 63) (21 774) (21 18) (2144) (21 69) (2154) ; A ; B ; C ; D ; E ; F ; G ; H ; I ; J ; K ; L ( )

5 3 : 443 [30 ] Box2Cox [23 ] ( ) Box - Cox, : > > > ( 3) ( P = 01001), Box2Cox ( 2), : I, ; ( ) II, ; III, ( 3) Spearma, ( R = 01003, P = 01967) : Box2Cox 3 Box2Cox Fig13 Hierarchical cluster aalysis based o Box2Cox meas of Z cocetratios i vegetables from Beijig

6 , Box2Cox, ( = 0131) ( = ) ( ), ( P = 01009), Box2Cox ( = 0134), ( P = 01314) ( 4) 3 Tab13 Compariso of Z cotets i vegetables produced from Beijig ad other provices of Chia, ad cultured uder f ield coditios ad greehouse (mg/ kg, ) Box2Cox ( ) PS2W ( ) PS2W (2125) (215) (3197) (11 54) (3101) (21 63) (2122) (21 43) ,,, Box2Cox ( 4) 4 Tab14 Biococetratio factors ( BCF) of Ni i major vegetables collected from farms of Beijig Box2Cox ( ) PS2W ( ) PS2W (01 029) (11 21) (01 018) (11 19) (01 021) (11 18) (01 025) (11 20) 0155 ( ) (01 062) (11 30) (01 039) (11 29) (01 036) (11 23) (01 007) (11 14) (01 005) (11 12) (01 013) (11 16) (01 045) (11 40) (0104) (11 33) (01 022) (11 23) (01 037) (11 28) 0191

7 3 : Box2Cox Fig14 Hierarchical cluster aalysis o Box2Cox meas of BCF of Z cocetratios i vegetables from Beijig Box2Cox, ; ;,, ( 4) 215, 116 kg/ ( d) [5 ] Box2Cox ( 2), (2) 4104 mg/ ( d) FAO/ W HO [ 2 ] EPA [31 ] 013 mg/ (kg d) FAO/ W HO 1 mg/ ( kg d) EPA (NOA EL) 0191 mg/ ( kg d) 60 kg, 2214 %, 617 % ( FAO/ W HO) 714 % ( EPA), 76 % ;, [32 ],,, [33 ], hm 2, 5111 % [34 ] [35 ],, [1 ], 15 % 25 %, [26 ][36 ], 30 % 40 % [32,37 ], [38 ] [39,40 ], ( 65 % 86 %) [40 ], [29 ],,,,,

8 (1),,,,,, (2), 4104 mg/ ( d), : [ 1 ],,,. -. :,1996. [ 2 ],,.. :. 1998, [ 3 ],,.., 2003, 22 (4) : [ 4 ],,,.., 2002, 22 (5) : [ 5 ],,,.., 2006, 61 (3) : [ 6 ],,,.., 2006, 21(3) : [ 7 ],,,.., 2006, 26. [ 8 ],,,.., 2006, 26. [ 9 ],,,.., 2006, 21 (1) : [ 10 ],,,.., 2005, 27 (6) : [ 11 ],,,.., 2005, 60 (5) : [ 12 ],,,.., 2005, 24 (4) : [ 13 ],,,. -., 2005, 20 (5) : [ 14 ],,,.., 2005, 24 (2) : [ 15 ],,,.., 2006,25 (3) : [ 16 ],,,.., 2005, 25 (12) : [ 17 ],,,.., 2005, 25 (9) : [ 18 ],,. (SHMIS) -., 2003, 22 (3) : [19 ],,,.., 2004, 24 (3) : [ 20 ],.., 1997, 17 (3) :

9 3 : 447 [ 21 ].. :, , 173. [ 22 ] EPA. Acid Digestio of Sedimet s Sludge ad Soils. USEPA 3050B. http :/ / www. epa. gov/ SW2846/ pdf s/ 3050b. pdf [ 23 ] Zhag C S, Zhag S. A robust2symmetric mea : a ew way of mea calculatio for evirometal data. GeoJour2 al, 1995, 40 (1/ 2) : [ 24 ],,,.., 2004, 24 (1) : [ 25 ]. ( GB ). :,1995. [ 26 ],,,.., 2003, (1) : 3 6,9. [27 ],,,.., 2000, 6 (1) : [ 28 ],.., 2004, 21 (3) : [ 29 ]. ( GB ). :,1991. [ 30 ] GB [ 31 ] EPA. Toxicological Review of Zic ad Compouds (Cas No ) I : Support of Summary Iformatio o the Itegrated Risk Iformatio System ( IRIS)1 http :/ / www. epa. gov/ iris/ toxreviews/ 04262tr. pdf [ 32 ].., 1998, 14 (12) : [ 33 ].. :, [ 34 ],,,.., 2003, 23 (5) : [ 35 ].., 1994, 27 (1) : [ 36 ],,,.., 2004, 26 (5) : [ 37 ],,,.., 1999, 20 (5) : [ 38 ],.., 1999, 29 (1) : [ 39 ],,,.., 2002, 10 (4) : [ 40 ],,,. 700., 1997, 4 (11) : A survey of zic cocetratios i vegetables ad soils i Beijig ad their health risk HUAN G Ze2chu 1, SON G Bo 1,2, CHEN Tog2bi 1, ZHEN G Yua2mig 1, YAN GJ u 1,2 (11 Ceter for Evirometal Remediatio, Istitute of Geographic Scieces ad Natural Resources Research,CAS, Beijig , Chia ; 21 Graduate School, Chiese Academy of Scieces, Beijig , Chia) Abstract :To assess t he huma health risk posed by elevated cocetratios of zic i vege2 tables, ad to idetif y pollutio 2tolerat vegetable varieties, a large scale survey of zic levels i soils ad vegetables plated or sold i Beijig was coducted1 Fifty2two soil sam2 ples were collected f rom gardes ad fields used to grow vegetable plat s1 I additio, 97

10 varieties of 402 f resh vegetable samples were obtaied from vegetable stalls, supermarket s ad wholesale outlet s1 Zic cocetratios were measured usig flame atomic absorptio spectrometry1 Zic cocetratios i soils raged f rom 2419 to mg kg - 1, wit h arit hmetic, me2 dia, geometric ad Box2Cox meas of 79129, 63181, 7017 ad mg kg - 1, respec2 tively1 Compared with the backgroud zic cocetratios of soils f rom Beijig, t here ap2 peared a sigificat accumulatio of zic i soils collected from fields that produced vegeta2 bles1 Zic cocetratios i t he edible plat portios raged f rom to 2516 mg kg - 1 f resh weight, wit h arithmetic, media ad Box2Cox meas of 3111, 2124 ad 2155 mg kg 21 f resh weight, respectively1 I all of the samples ad vegetable varieties, zic was less t ha t he Tolerace Limit of Zic i Foods ( TL CF) of 100 mg kg - 1 f resh weight for p ulse ad 20 mg kg - 1 for ot her vegetables1 The TL CF is t he maximum permissible cocetratio of zic i vegetables t hat will be cosumed by people1 The highest level of zic detected i a vege2 table plat was 2516 mg kg - 1, which was measured i a gree soybea sample1 Statistical aalysis showed t hat t he zic co cet ratio i leaf vegetables was sigificat higher t ha t hat of gourd ad fruit vegetable1 Ad t he zic cocetratio i vegetables f rom other places of Chia was sigificatly higher t ha t he co cet ratio of local vegetables, but t here was o sigificatly differece betwee field2grow vegetables ad t hose plated i a greeho use1 Result s of hierarchical cluster aalysis o the zic biococetratio factor (BCF) i vegetables idicated t hat t he plat s sampled could be separated ito t hree group s based o BCF1 Beas roud trellis (V i g a u g uicul at a), the first group, had t he highest BCFs, ad t he followig is t he secod group, icludig Chiese cabbage ( B rassica peki esi s), Pakchoi ( B rassica chi esis) ad radish ( R a p haus), had higher zic BCFs while Chiese gree oio, chili ( Ca psicum auum) cucumber ( Cucumis sati v us), eggplat ( S ol aum sp1), tomato (L ycopersico esculet um) ad wax gourd ( B ei acasa his pi da) had lower zic BCFs1 The average igestio rate of zic from vegetables was 4104 mg/ idividual/ day for people of Beijig, makig up 2214 % of t he quatity demaded (18 mg/ idividual/ day) ad 71 4 % of No2Observed2Adver se Effect2Level ( NOA EL ) 1 Co sumig vegetables wit h ele2 vated zic cocetratios may ot pose a healt h risk to local residet s1 Key words :Zic ; Beijig ; vegetables ; soil ; bioaccumulatio ; huma healt h risk ; pollutat2 resistat plat s

(Intentional blank page) Please remove this page and make both-sided copy from the next page.

(Intentional blank page) Please remove this page and make both-sided copy from the next page. (Itetioal blak page) Please remove this page ad make both-sided copy from the ext page. Statistical Data Aalysis by Excel for Impact Evaluatio (Basic Course) Text Histogram, average & stadard deviatio

More information

Error & Uncertainty. Error. More on errors. Uncertainty. Page # The error is the difference between a TRUE value, x, and a MEASURED value, x i :

Error & Uncertainty. Error. More on errors. Uncertainty. Page # The error is the difference between a TRUE value, x, and a MEASURED value, x i : Error Error & Ucertaity The error is the differece betwee a TRUE value,, ad a MEASURED value, i : E = i There is o error-free measuremet. The sigificace of a measuremet caot be judged uless the associate

More information

Chapter 13, Part A Analysis of Variance and Experimental Design

Chapter 13, Part A Analysis of Variance and Experimental Design Slides Prepared by JOHN S. LOUCKS St. Edward s Uiversity Slide 1 Chapter 13, Part A Aalysis of Variace ad Eperimetal Desig Itroductio to Aalysis of Variace Aalysis of Variace: Testig for the Equality of

More information

GEO GRA P HICAL RESEA RCH

GEO GRA P HICAL RESEA RCH 24 5 2005 9 GEO GRA P HICAL RESEA RCH Vol124, No15 Sept1, 2005 1, 2,3, 1,2, 1, 1, 1 (11, 100080 ; 21, 100101 ; 31, 100101) :,,,, 6193 10 9 m 3 / a, 41319 m 3 / a, 471 6 % 431 4 % 910 %,,,,,, : ; ; ; :

More information

Analysis of water quality status in culturing waters in Kaozhou Bay based upon GIS

Analysis of water quality status in culturing waters in Kaozhou Bay based upon GIS 28 5 2004 10 JOURNAL OF FISHERIES OF CHINA Vol. 28, No. 5 Oct., 2004 :1000-0615 2004) 05-0522 - 07 GIS,,,,,,,, 510300) : GIS, 1999 2 ) 8 ),,,,,,,,,, 2 3,,,, : GIS) ; ; ; :S959 :A Aalysis of water quality

More information

GEO GRA P HICAL RESEA RCH

GEO GRA P HICAL RESEA RCH 26 3 2007 5 GEO GRA P HICAL RESEA RCH Vol126, No13 May, 2007 1,2, 3, 4 (11, 100049 ; 21, 100080 ; 31, 100101 ; 41, 832000) :,, ;,, : 1992 2003,,, : ; ; ; : 100020585 (2007) 0320590209,, U,, [ 1 8 ],, [9

More information

ANALYSIS OF EXPERIMENTAL ERRORS

ANALYSIS OF EXPERIMENTAL ERRORS ANALYSIS OF EXPERIMENTAL ERRORS All physical measuremets ecoutered i the verificatio of physics theories ad cocepts are subject to ucertaities that deped o the measurig istrumets used ad the coditios uder

More information

MidtermII Review. Sta Fall Office Hours Wednesday 12:30-2:30pm Watch linear regression videos before lab on Thursday

MidtermII Review. Sta Fall Office Hours Wednesday 12:30-2:30pm Watch linear regression videos before lab on Thursday Aoucemets MidtermII Review Sta 101 - Fall 2016 Duke Uiversity, Departmet of Statistical Sciece Office Hours Wedesday 12:30-2:30pm Watch liear regressio videos before lab o Thursday Dr. Abrahamse Slides

More information

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio

More information

Comparing your lab results with the others by one-way ANOVA

Comparing your lab results with the others by one-way ANOVA Comparig your lab results with the others by oe-way ANOVA You may have developed a ew test method ad i your method validatio process you would like to check the method s ruggedess by coductig a simple

More information

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND.

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. XI-1 (1074) MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. R. E. D. WOOLSEY AND H. S. SWANSON XI-2 (1075) STATISTICAL DECISION MAKING Advaced

More information

Air Flow Distribution in the Sales Area of a Supermarket

Air Flow Distribution in the Sales Area of a Supermarket Air Flow Distributio i the Sales Area of a Supermarket Xiumu Fag Chualiag Sog Jiaig Zhao Zhaoju Wag Doctoral Associate Professor Professor Cadidate professor School of Muicipal ad Evirometal Eg. Harbi

More information

Provläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE

Provläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE TECHNICAL REPORT CISPR 16-4-3 2004 AMENDMENT 1 2006-10 INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE Amedmet 1 Specificatio for radio disturbace ad immuity measurig apparatus ad methods Part 4-3:

More information

Measures of Spread: Variance and Standard Deviation

Measures of Spread: Variance and Standard Deviation Lesso 1-6 Measures of Spread: Variace ad Stadard Deviatio BIG IDEA Variace ad stadard deviatio deped o the mea of a set of umbers. Calculatig these measures of spread depeds o whether the set is a sample

More information

Simple Linear Regression

Simple Linear Regression Simple Liear Regressio 1. Model ad Parameter Estimatio (a) Suppose our data cosist of a collectio of pairs (x i, y i ), where x i is a observed value of variable X ad y i is the correspodig observatio

More information

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals 7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses

More information

Chapter 2 Descriptive Statistics

Chapter 2 Descriptive Statistics Chapter 2 Descriptive Statistics Statistics Most commoly, statistics refers to umerical data. Statistics may also refer to the process of collectig, orgaizig, presetig, aalyzig ad iterpretig umerical data

More information

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series

More information

Discrete Orthogonal Moment Features Using Chebyshev Polynomials

Discrete Orthogonal Moment Features Using Chebyshev Polynomials Discrete Orthogoal Momet Features Usig Chebyshev Polyomials R. Mukuda, 1 S.H.Og ad P.A. Lee 3 1 Faculty of Iformatio Sciece ad Techology, Multimedia Uiversity 75450 Malacca, Malaysia. Istitute of Mathematical

More information

(# x) 2 n. (" x) 2 = 30 2 = 900. = sum. " x 2 = =174. " x. Chapter 12. Quick math overview. #(x " x ) 2 = # x 2 "

(# x) 2 n. ( x) 2 = 30 2 = 900. = sum.  x 2 = =174.  x. Chapter 12. Quick math overview. #(x  x ) 2 = # x 2 Chapter 12 Describig Distributios with Numbers Chapter 12 1 Quick math overview = sum These expressios are algebraically equivalet #(x " x ) 2 = # x 2 " (# x) 2 Examples x :{ 2,3,5,6,6,8 } " x = 2 + 3+

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Evaluation on Ecological Risks of Soil Heavy Metals in a Certain Area of Sichuan by Improved Fuzzy Mathematics Method

Evaluation on Ecological Risks of Soil Heavy Metals in a Certain Area of Sichuan by Improved Fuzzy Mathematics Method Joural of Geosciece ad Eviromet Protectio, 204, 2, 28-35 Published Olie April 204 i SciRes. http://www.scirp.org/joural/gep http://dx.doi.org/0.4236/gep.204.22005 Evaluatio o Ecological Risks of Soil Heavy

More information

The improvement of the volume ratio measurement method in static expansion vacuum system

The improvement of the volume ratio measurement method in static expansion vacuum system Available olie at www.sciecedirect.com Physics Procedia 32 (22 ) 492 497 8 th Iteratioal Vacuum Cogress The improvemet of the volume ratio measuremet method i static expasio vacuum system Yu Hogya*, Wag

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Interval Estimation (Confidence Interval = C.I.): An interval estimate of some population parameter is an interval of the form (, ),

Interval Estimation (Confidence Interval = C.I.): An interval estimate of some population parameter is an interval of the form (, ), Cofidece Iterval Estimatio Problems Suppose we have a populatio with some ukow parameter(s). Example: Normal(,) ad are parameters. We eed to draw coclusios (make ifereces) about the ukow parameters. We

More information

Comparison of Minimum Initial Capital with Investment and Non-investment Discrete Time Surplus Processes

Comparison of Minimum Initial Capital with Investment and Non-investment Discrete Time Surplus Processes The 22 d Aual Meetig i Mathematics (AMM 207) Departmet of Mathematics, Faculty of Sciece Chiag Mai Uiversity, Chiag Mai, Thailad Compariso of Miimum Iitial Capital with Ivestmet ad -ivestmet Discrete Time

More information

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference EXST30 Backgroud material Page From the textbook The Statistical Sleuth Mea [0]: I your text the word mea deotes a populatio mea (µ) while the work average deotes a sample average ( ). Variace [0]: The

More information

Exponential and Trigonometric Functions Lesson #1

Exponential and Trigonometric Functions Lesson #1 Epoetial ad Trigoometric Fuctios Lesso # Itroductio To Epoetial Fuctios Cosider a populatio of 00 mice which is growig uder plague coditios. If the mouse populatio doubles each week we ca costruct a table

More information

TRACEABILITY SYSTEM OF ROCKWELL HARDNESS C SCALE IN JAPAN

TRACEABILITY SYSTEM OF ROCKWELL HARDNESS C SCALE IN JAPAN HARDMEKO 004 Hardess Measuremets Theory ad Applicatio i Laboratories ad Idustries - November, 004, Washigto, D.C., USA TRACEABILITY SYSTEM OF ROCKWELL HARDNESS C SCALE IN JAPAN Koichiro HATTORI, Satoshi

More information

Bayesian Methods: Introduction to Multi-parameter Models

Bayesian Methods: Introduction to Multi-parameter Models Bayesia Methods: Itroductio to Multi-parameter Models Parameter: θ = ( θ, θ) Give Likelihood p(y θ) ad prior p(θ ), the posterior p proportioal to p(y θ) x p(θ ) Margial posterior ( θ, θ y) is Iterested

More information

Some properties of Boubaker polynomials and applications

Some properties of Boubaker polynomials and applications Some properties of Boubaker polyomials ad applicatios Gradimir V. Milovaović ad Duša Joksimović Citatio: AIP Cof. Proc. 179, 1050 (2012); doi: 10.1063/1.756326 View olie: http://dx.doi.org/10.1063/1.756326

More information

MATHEMATICS Paper 2 22 nd September 20. Answer Papers List of Formulae (MF15)

MATHEMATICS Paper 2 22 nd September 20. Answer Papers List of Formulae (MF15) NANYANG JUNIOR COLLEGE JC PRELIMINARY EXAMINATION Higher MATHEMATICS 9740 Paper d September 0 3 Ho Additioal Materials: Cover Sheet Aswer Papers List of Formulae (MF15) READ THESE INSTRUCTIONS FIRST Write

More information

Inversion of Earthquake Rupture Process:Theory and Applications

Inversion of Earthquake Rupture Process:Theory and Applications Iversio of Earthquake Rupture Process:Theory ad Applicatios Yu-tai CHEN 12 * Yog ZHANG 12 Li-sheg XU 2 1School of the Earth ad Space Scieces, Pekig Uiversity, Beijig 1871 2Istitute of Geophysics, Chia

More information

DS 100: Principles and Techniques of Data Science Date: April 13, Discussion #10

DS 100: Principles and Techniques of Data Science Date: April 13, Discussion #10 DS 00: Priciples ad Techiques of Data Sciece Date: April 3, 208 Name: Hypothesis Testig Discussio #0. Defie these terms below as they relate to hypothesis testig. a) Data Geeratio Model: Solutio: A set

More information

Dr. Maddah ENMG 617 EM Statistics 11/26/12. Multiple Regression (2) (Chapter 15, Hines)

Dr. Maddah ENMG 617 EM Statistics 11/26/12. Multiple Regression (2) (Chapter 15, Hines) Dr Maddah NMG 617 M Statistics 11/6/1 Multiple egressio () (Chapter 15, Hies) Test for sigificace of regressio This is a test to determie whether there is a liear relatioship betwee the depedet variable

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P FEBRUARY/MARCH 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 9 pages, diagram sheet ad iformatio sheet. Please tur over Mathematics/P DBE/Feb.

More information

Comparing Two Populations. Topic 15 - Two Sample Inference I. Comparing Two Means. Comparing Two Pop Means. Background Reading

Comparing Two Populations. Topic 15 - Two Sample Inference I. Comparing Two Means. Comparing Two Pop Means. Background Reading Topic 15 - Two Sample Iferece I STAT 511 Professor Bruce Craig Comparig Two Populatios Research ofte ivolves the compariso of two or more samples from differet populatios Graphical summaries provide visual

More information

Final Examination Solutions 17/6/2010

Final Examination Solutions 17/6/2010 The Islamic Uiversity of Gaza Faculty of Commerce epartmet of Ecoomics ad Political Scieces A Itroductio to Statistics Course (ECOE 30) Sprig Semester 009-00 Fial Eamiatio Solutios 7/6/00 Name: I: Istructor:

More information

GUIDELINES ON REPRESENTATIVE SAMPLING

GUIDELINES ON REPRESENTATIVE SAMPLING DRUGS WORKING GROUP VALIDATION OF THE GUIDELINES ON REPRESENTATIVE SAMPLING DOCUMENT TYPE : REF. CODE: ISSUE NO: ISSUE DATE: VALIDATION REPORT DWG-SGL-001 002 08 DECEMBER 2012 Ref code: DWG-SGL-001 Issue

More information

There is no straightforward approach for choosing the warmup period l.

There is no straightforward approach for choosing the warmup period l. B. Maddah INDE 504 Discrete-Evet Simulatio Output Aalysis () Statistical Aalysis for Steady-State Parameters I a otermiatig simulatio, the iterest is i estimatig the log ru steady state measures of performace.

More information

Estimating Confidence Interval of Mean Using. Classical, Bayesian, and Bootstrap Approaches

Estimating Confidence Interval of Mean Using. Classical, Bayesian, and Bootstrap Approaches Iteratioal Joural of Mathematical Aalysis Vol. 8, 2014, o. 48, 2375-2383 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ijma.2014.49287 Estimatig Cofidece Iterval of Mea Usig Classical, Bayesia,

More information

SOLUTIONS y n. n 1 = 605, y 1 = 351. y1. p y n. n 2 = 195, y 2 = 41. y p H 0 : p 1 = p 2 vs. H 1 : p 1 p 2.

SOLUTIONS y n. n 1 = 605, y 1 = 351. y1. p y n. n 2 = 195, y 2 = 41. y p H 0 : p 1 = p 2 vs. H 1 : p 1 p 2. STAT 400 UIUC Practice Problems # SOLUTIONS Stepaov Dalpiaz The followig are a umber of practice problems that may be helpful for completig the homework, ad will likely be very useful for studyig for exams..

More information

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010 Pearso Educatio, Ic. Comparig Two Proportios Comparisos betwee two percetages are much more commo tha questios about isolated percetages. Ad they are more

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

Chapter If n is odd, the median is the exact middle number If n is even, the median is the average of the two middle numbers

Chapter If n is odd, the median is the exact middle number If n is even, the median is the average of the two middle numbers Chapter 4 4-1 orth Seattle Commuity College BUS10 Busiess Statistics Chapter 4 Descriptive Statistics Summary Defiitios Cetral tedecy: The extet to which the data values group aroud a cetral value. Variatio:

More information

General IxJ Contingency Tables

General IxJ Contingency Tables page1 Geeral x Cotigecy Tables We ow geeralize our previous results from the prospective, retrospective ad cross-sectioal studies ad the Poisso samplig case to x cotigecy tables. For such tables, the test

More information

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm Joural of ad Eergy Egieerig, 05, 3, 43-437 Published Olie April 05 i SciRes. http://www.scirp.org/joural/jpee http://dx.doi.org/0.436/jpee.05.34058 Study o Coal Cosumptio Curve Fittig of the Thermal Based

More information

Activity 3: Length Measurements with the Four-Sided Meter Stick

Activity 3: Length Measurements with the Four-Sided Meter Stick Activity 3: Legth Measuremets with the Four-Sided Meter Stick OBJECTIVE: The purpose of this experimet is to study errors ad the propagatio of errors whe experimetal data derived usig a four-sided meter

More information

Available online at ScienceDirect. Procedia Engineering 121 (2015 )

Available online at   ScienceDirect. Procedia Engineering 121 (2015 ) Available olie at www.sciecedirect.com ScieceDirect Procedia Egieerig 121 (2015 ) 151 157 9th Iteratioal Symposium o Heatig, Vetilatio ad Air Coditioig (ISHVAC) ad the 3rd Iteratioal Coferece o Buildig

More information

Statistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons

Statistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons Statistical Aalysis o Ucertaity for Autocorrelated Measuremets ad its Applicatios to Key Comparisos Nie Fa Zhag Natioal Istitute of Stadards ad Techology Gaithersburg, MD 0899, USA Outlies. Itroductio.

More information

Chapter 6 Sampling Distributions

Chapter 6 Sampling Distributions Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to

More information

Let us give one more example of MLE. Example 3. The uniform distribution U[0, θ] on the interval [0, θ] has p.d.f.

Let us give one more example of MLE. Example 3. The uniform distribution U[0, θ] on the interval [0, θ] has p.d.f. Lecture 5 Let us give oe more example of MLE. Example 3. The uiform distributio U[0, ] o the iterval [0, ] has p.d.f. { 1 f(x =, 0 x, 0, otherwise The likelihood fuctio ϕ( = f(x i = 1 I(X 1,..., X [0,

More information

Analysis of Experimental Measurements

Analysis of Experimental Measurements Aalysis of Experimetal Measuremets Thik carefully about the process of makig a measuremet. A measuremet is a compariso betwee some ukow physical quatity ad a stadard of that physical quatity. As a example,

More information

Bayesian and E- Bayesian Method of Estimation of Parameter of Rayleigh Distribution- A Bayesian Approach under Linex Loss Function

Bayesian and E- Bayesian Method of Estimation of Parameter of Rayleigh Distribution- A Bayesian Approach under Linex Loss Function Iteratioal Joural of Statistics ad Systems ISSN 973-2675 Volume 12, Number 4 (217), pp. 791-796 Research Idia Publicatios http://www.ripublicatio.com Bayesia ad E- Bayesia Method of Estimatio of Parameter

More information

MISCELLANEOUS SEQUENCES & SERIES QUESTIONS

MISCELLANEOUS SEQUENCES & SERIES QUESTIONS MISCELLANEOUS SEQUENCES & SERIES QUESTIONS Questio (***+) Evaluate the followig sum 30 r ( 2) 4r 78. 3 MP2-V, 75,822,200 Questio 2 (***+) Three umbers, A, B, C i that order, are i geometric progressio

More information

Estimation for Complete Data

Estimation for Complete Data Estimatio for Complete Data complete data: there is o loss of iformatio durig study. complete idividual complete data= grouped data A complete idividual data is the oe i which the complete iformatio of

More information

Stat 139 Homework 7 Solutions, Fall 2015

Stat 139 Homework 7 Solutions, Fall 2015 Stat 139 Homework 7 Solutios, Fall 2015 Problem 1. I class we leared that the classical simple liear regressio model assumes the followig distributio of resposes: Y i = β 0 + β 1 X i + ɛ i, i = 1,...,,

More information

Sample Size Determination (Two or More Samples)

Sample Size Determination (Two or More Samples) Sample Sie Determiatio (Two or More Samples) STATGRAPHICS Rev. 963 Summary... Data Iput... Aalysis Summary... 5 Power Curve... 5 Calculatios... 6 Summary This procedure determies a suitable sample sie

More information

Modification of Arrhenius Model for Numerical Modelling of Turbulent Flames

Modification of Arrhenius Model for Numerical Modelling of Turbulent Flames J. Basic. Appl. Sci. Res., (5)480-486, 01 01, TextRoad Publicatio ISSN 090-4304 Joural of Basic ad Applied Scietific Research www.textroad.com Modificatio of Arrheius Model for Numerical Modellig of Turbulet

More information

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n.

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n. ST 305: Exam 3 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad the basic

More information

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010, 2007, 2004 Pearso Educatio, Ic. Comparig Two Proportios Read the first two paragraphs of pg 504. Comparisos betwee two percetages are much more commo

More information

Statistical Intervals for a Single Sample

Statistical Intervals for a Single Sample 3/5/06 Applied Statistics ad Probability for Egieers Sixth Editio Douglas C. Motgomery George C. Ruger Chapter 8 Statistical Itervals for a Sigle Sample 8 CHAPTER OUTLINE 8- Cofidece Iterval o the Mea

More information

Chapter 12 Correlation

Chapter 12 Correlation Chapter Correlatio Correlatio is very similar to regressio with oe very importat differece. Regressio is used to explore the relatioship betwee a idepedet variable ad a depedet variable, whereas correlatio

More information

(2011)

(2011) 31 4 Vol.31 No. 4 2011 4 ECONOMIC GEOGRAPHY Apr. 2011 1000-8462(2011)04-0591 - 08 1 2 3 4 1 2 4 4 1. 100101 2. 100101 3. 100039 4., 830011 11985 2005 2 3 4 20 F502 A [8] [1] [2] [3] [4] [9-13] [4] [14]

More information

GRADE 12 SEPTEMBER 2015 MATHEMATICS P1

GRADE 12 SEPTEMBER 2015 MATHEMATICS P1 NATIONAL SENIOR CERTIFICATE GRADE 1 SEPTEMBER 015 MATHEMATICS P1 MARKS: 150 TIME: 3 hours *MATHE1* This questio paper cosists of 10 pages, icludig a iformatio sheet. MATHEMATICS P1 (EC/SEPTEMBER 015) INSTRUCTIONS

More information

Median and IQR The median is the value which divides the ordered data values in half.

Median and IQR The median is the value which divides the ordered data values in half. STA 666 Fall 2007 Web-based Course Notes 4: Describig Distributios Numerically Numerical summaries for quatitative variables media ad iterquartile rage (IQR) 5-umber summary mea ad stadard deviatio Media

More information

Multiple Comparisons Examples STAT 314

Multiple Comparisons Examples STAT 314 Multiple Comparisos Examples STAT 31 Problem umbers match those from the ANOVA Examples hadout. 8. Four brads of flashlight batteries are to be compared by testig each brad i five flashlights. Twety flashlights

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak

More information

Chapter 1 (Definitions)

Chapter 1 (Definitions) FINAL EXAM REVIEW Chapter 1 (Defiitios) Qualitative: Nomial: Ordial: Quatitative: Ordial: Iterval: Ratio: Observatioal Study: Desiged Experimet: Samplig: Cluster: Stratified: Systematic: Coveiece: Simple

More information

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all!

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all! ENGI 44 Probability ad Statistics Faculty of Egieerig ad Applied Sciece Problem Set Solutios Descriptive Statistics. If, i the set of values {,, 3, 4, 5, 6, 7 } a error causes the value 5 to be replaced

More information

DISTRIBUTION LAW Okunev I.V.

DISTRIBUTION LAW Okunev I.V. 1 DISTRIBUTION LAW Okuev I.V. Distributio law belogs to a umber of the most complicated theoretical laws of mathematics. But it is also a very importat practical law. Nothig ca help uderstad complicated

More information

Chapter 6. Sampling and Estimation

Chapter 6. Sampling and Estimation Samplig ad Estimatio - 34 Chapter 6. Samplig ad Estimatio 6.. Itroductio Frequetly the egieer is uable to completely characterize the etire populatio. She/he must be satisfied with examiig some subset

More information

Table 12.1: Contingency table. Feature b. 1 N 11 N 12 N 1b 2 N 21 N 22 N 2b. ... a N a1 N a2 N ab

Table 12.1: Contingency table. Feature b. 1 N 11 N 12 N 1b 2 N 21 N 22 N 2b. ... a N a1 N a2 N ab Sectio 12 Tests of idepedece ad homogeeity I this lecture we will cosider a situatio whe our observatios are classified by two differet features ad we would like to test if these features are idepedet

More information

INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS

INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS UNIVERSITY OF EAST ANGLIA School of Ecoomics Mai Series UG Examiatio 05-6 INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS ECO-400Y Time allowed: 3 hours Aswer ALL questios. Show all workig icludig

More information

Number of fatalities X Sunday 4 Monday 6 Tuesday 2 Wednesday 0 Thursday 3 Friday 5 Saturday 8 Total 28. Day

Number of fatalities X Sunday 4 Monday 6 Tuesday 2 Wednesday 0 Thursday 3 Friday 5 Saturday 8 Total 28. Day LECTURE # 8 Mea Deviatio, Stadard Deviatio ad Variace & Coefficiet of variatio Mea Deviatio Stadard Deviatio ad Variace Coefficiet of variatio First, we will discuss it for the case of raw data, ad the

More information

Confidence Interval for one population mean or one population proportion, continued. 1. Sample size estimation based on the large sample C.I.

Confidence Interval for one population mean or one population proportion, continued. 1. Sample size estimation based on the large sample C.I. Cofidece Iterval for oe populatio mea or oe populatio proportio, cotiued 1. ample size estimatio based o the large sample C.I. for p ˆ(1 ˆ) ˆ(1 ˆ) From the iterval ˆ p p Z p ˆ, p Z p p L legh of your 100(1

More information

BIOSTATISTICS. Lecture 5 Interval Estimations for Mean and Proportion. dr. Petr Nazarov

BIOSTATISTICS. Lecture 5 Interval Estimations for Mean and Proportion. dr. Petr Nazarov Microarray Ceter BIOSTATISTICS Lecture 5 Iterval Estimatios for Mea ad Proportio dr. Petr Nazarov 15-03-013 petr.azarov@crp-sate.lu Lecture 5. Iterval estimatio for mea ad proportio OUTLINE Iterval estimatios

More information

INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS

INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS UNIVERSITY OF EAST ANGLIA School of Ecoomics Mai Series UG Examiatio 04-5 INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS ECO-400Y Time allowed: 3 hours Aswer ALL questios. Show all workig icludig

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

Read through these prior to coming to the test and follow them when you take your test.

Read through these prior to coming to the test and follow them when you take your test. Math 143 Sprig 2012 Test 2 Iformatio 1 Test 2 will be give i class o Thursday April 5. Material Covered The test is cummulative, but will emphasize the recet material (Chapters 6 8, 10 11, ad Sectios 12.1

More information

n but for a small sample of the population, the mean is defined as: n 2. For a lognormal distribution, the median equals the mean.

n but for a small sample of the population, the mean is defined as: n 2. For a lognormal distribution, the median equals the mean. Sectio. True or False Questios (2 pts each). For a populatio the meas is defied as i= μ = i but for a small sample of the populatio, the mea is defied as: = i= i 2. For a logormal distributio, the media

More information

(6) Fundamental Sampling Distribution and Data Discription

(6) Fundamental Sampling Distribution and Data Discription 34 Stat Lecture Notes (6) Fudametal Samplig Distributio ad Data Discriptio ( Book*: Chapter 8,pg5) Probability& Statistics for Egieers & Scietists By Walpole, Myers, Myers, Ye 8.1 Radom Samplig: Populatio:

More information

Assessment of extreme discharges of the Vltava River in Prague

Assessment of extreme discharges of the Vltava River in Prague Flood Recovery, Iovatio ad Respose I 05 Assessmet of extreme discharges of the Vltava River i Prague M. Holický, K. Jug & M. Sýkora Kloker Istitute, Czech Techical Uiversity i Prague, Czech Republic Abstract

More information

2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2

2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2 Chapter 8 Comparig Two Treatmets Iferece about Two Populatio Meas We wat to compare the meas of two populatios to see whether they differ. There are two situatios to cosider, as show i the followig examples:

More information

Interval Intuitionistic Trapezoidal Fuzzy Prioritized Aggregating Operators and their Application to Multiple Attribute Decision Making

Interval Intuitionistic Trapezoidal Fuzzy Prioritized Aggregating Operators and their Application to Multiple Attribute Decision Making Iterval Ituitioistic Trapezoidal Fuzzy Prioritized Aggregatig Operators ad their Applicatio to Multiple Attribute Decisio Makig Xia-Pig Jiag Chogqig Uiversity of Arts ad Scieces Chia cqmaagemet@163.com

More information

PREDICTION OF REVERBERATION TIME IN RECTANGULAR ROOMS WITH NON UNIFORMLY DISTRIBUTED ABSORPTION USING A NEW FORMULA

PREDICTION OF REVERBERATION TIME IN RECTANGULAR ROOMS WITH NON UNIFORMLY DISTRIBUTED ABSORPTION USING A NEW FORMULA PREDICTION OF REVERBERATION TIME IN RECTANGULAR ROOM WITH NON UNIFORMLY DITRIBUTED ABORPTION UING A NEW FORMULA PAC REFERENCE: 43.55.Br Neubauer, Reihard O. Ig.-Büro Neubauer VDI Theresiestr. 8 D-85049

More information

Application of the Zhe Yin s Gene Inherits Law

Application of the Zhe Yin s Gene Inherits Law Ope Joural of Geetics, 2014, 4, 434-438 Published Olie December 2014 i SciRes. http://www.scirp.org/joural/ojge http://dx.doi.org/10.4236/ojge.2014.46041 Applicatio of the Zhe Yi s Gee Iherits Law Zhe

More information

University of California, Los Angeles Department of Statistics. Hypothesis testing

University of California, Los Angeles Department of Statistics. Hypothesis testing Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Elemets of a hypothesis test: Hypothesis testig Istructor: Nicolas Christou 1. Null hypothesis, H 0 (claim about µ, p, σ 2, µ

More information

GRADE 11 NOVEMBER 2012 MATHEMATICS P1

GRADE 11 NOVEMBER 2012 MATHEMATICS P1 Provice of the EASTERN CAPE EDUCATION NATIONAL SENIOR CERTIFICATE GRADE 11 NOVEMBER 01 MATHEMATICS P1 MARKS: 150 TIME: 3 hours This questio paper cosists of 14 pages, icludig a iformatio sheet ad a page

More information

Chapter two: Hypothesis testing

Chapter two: Hypothesis testing : Hypothesis testig - Some basic cocepts: - Data: The raw material of statistics is data. For our purposes we may defie data as umbers. The two kids of umbers that we use i statistics are umbers that result

More information

Random Variables, Sampling and Estimation

Random Variables, Sampling and Estimation Chapter 1 Radom Variables, Samplig ad Estimatio 1.1 Itroductio This chapter will cover the most importat basic statistical theory you eed i order to uderstad the ecoometric material that will be comig

More information

Molecular Mechanisms of Gas Diffusion in CO 2 Hydrates

Molecular Mechanisms of Gas Diffusion in CO 2 Hydrates Supportig Iformatio Molecular Mechaisms of Gas Diffusio i CO Hydrates Shuai Liag, * Deqig Liag, Negyou Wu,,3 Lizhi Yi, ad Gaowei Hu,3 Key Laboratory of Gas Hydrate, Guagzhou Istitute of Eergy Coversio,

More information

The random version of Dvoretzky s theorem in l n

The random version of Dvoretzky s theorem in l n The radom versio of Dvoretzky s theorem i l Gideo Schechtma Abstract We show that with high probability a sectio of the l ball of dimesio k cε log c > 0 a uiversal costat) is ε close to a multiple of the

More information

Live Line Measuring the Parameters of 220 kv Transmission Lines with Mutual Inductance in Hainan Power Grid

Live Line Measuring the Parameters of 220 kv Transmission Lines with Mutual Inductance in Hainan Power Grid Egieerig, 213, 5, 146-151 doi:1.4236/eg.213.51b27 Published Olie Jauary 213 (http://www.scirp.org/joural/eg) Live Lie Measurig the Parameters of 22 kv Trasmissio Lies with Mutual Iductace i Haia Power

More information

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 4 Solutions [Numerical Methods]

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 4 Solutions [Numerical Methods] ENGI 3 Advaced Calculus or Egieerig Facult o Egieerig ad Applied Sciece Problem Set Solutios [Numerical Methods]. Use Simpso s rule with our itervals to estimate I si d a, b, h a si si.889 si 3 si.889

More information

Observation of Landau levels on nitrogen-doped flat graphite. surfaces without external magnetic fields

Observation of Landau levels on nitrogen-doped flat graphite. surfaces without external magnetic fields Supplemetary Iformatio Observatio of Ladau levels o itroge-doped flat graphite surfaces without exteral magetic fields Takahiro Kodo,, oghui Guo, Taishi Shikao, Tetsuya Suzuki, Masataka Sakurai, Susumu

More information

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ STATISTICAL INFERENCE INTRODUCTION Statistical iferece is that brach of Statistics i which oe typically makes a statemet about a populatio based upo the results of a sample. I oesample testig, we essetially

More information

Topic 5 [434 marks] (i) Find the range of values of n for which. (ii) Write down the value of x dx in terms of n, when it does exist.

Topic 5 [434 marks] (i) Find the range of values of n for which. (ii) Write down the value of x dx in terms of n, when it does exist. Topic 5 [44 marks] 1a (i) Fid the rage of values of for which eists 1 Write dow the value of i terms of 1, whe it does eist Fid the solutio to the differetial equatio 1b give that y = 1 whe = π (cos si

More information

POD-Based Analysis of Dynamic Wind Load Effects on a Large Span Roof

POD-Based Analysis of Dynamic Wind Load Effects on a Large Span Roof POD-Based Aalysis of Dyamic Wid Load Effects o a Large Spa Roof Xi-yag Ji, Yi Tag ad Hai Ji 3 Professor, Wid Egieerig Research Ceter, Chia Academy of Buildig Research, Beiig 3, Chia, ixiyag@cabrtech.com

More information

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A)

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A) REGRESSION (Physics 0 Notes, Partial Modified Appedix A) HOW TO PERFORM A LINEAR REGRESSION Cosider the followig data poits ad their graph (Table I ad Figure ): X Y 0 3 5 3 7 4 9 5 Table : Example Data

More information