2.' -4-5 I fo. - /30 + ;3, x + G: ~ / ~ ) ~ ov. Fd'r evt.'i') cutckf' ()y\e.._o OYLt dtt:vl. t'"'i ~ _) y =.5_21/2-+. 8"'- 2.

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "2.' -4-5 I fo. - /30 + ;3, x + G: ~ / ~ ) ~ ov. Fd'r evt.'i') cutckf' ()y\e.._o OYLt dtt:vl. t'"'i ~ _) y =.5_21/2-+. 8"'- 2."

Transcription

1 Statistics 100 Sample FINAL Instructions: I. WORK ALL PROBLEMS. Please, give details and explanations and SHOW ALL YOUR WORK so that partial credits can be given. 2. You may use four pages of notes, tables and a calculator but no other reference materials. 1. The following data were obtained from a random sample of eight oak trees on their age in years (x), and the diameter of their trunk in inches (y). x: 22 y: ( 4) a. Write down the regression model and explain its components. (10) y - /30 + ;3, x + G: ~ / ~ ) ~ ov Rrsl"'mJ~ Y-I"'tr-rUpt Slop'" &p(av1a.~e>v'j Ve-IA. VOcA.. b. Obtain the estimated least-squares regression equation, and explain the meaning of the estimated slope in the context of this problem. )( y ( ;< -x) (y-y) -)l... - ~ (X-X ( Y-Y) 22 ;to -2. -~ y 14 Ho lo IOU ~ 3'1 9 I ~" loo (X-x)( Y-'/) I ~.2. C> 2.' -4-5 I fo L... I'\ 2..'.20~ X= 1'12./ 8 == / 0./ 8S-2 2.S G, q b I _ ~ (_)(-x)( y-y )/ ~ c x -7<)"2. = T- 3 8 / 8 r:;; 2 - Y- b x - 2. fa - '8G:,~ 2.. ( 2.Lf) ~2. I I l A y =.5_21/2-+. 8"'- 2. x Fd'r evt.'i') cutckf' ()y\e.._o OYLt dtt:vl. t'"'i ~ _) OY1 C-<.VcY"aJL 17vt. t rv.. v'l It cl.ta me.. f < v w ''/I (t"! C-U.tl/l~. 8 (:, fo 2_ t'., ch t.e

2 Statistics 100 FINAL page2 (8) c. Test the null hypothesis that the correlation coefficient is equal to zero versus the alternative hypothesis that the correlation coefficient is positive at 0.01 level of significance and find the p-value f = 0 ) H,-q = p > o ; wht-u P =-- p 0 p. urv. ~ Y' = :2. { x-;t ){ y-y) - 7-?:. ~ ~, q ~ I l y ~lx-xjz.lly-'f)l yl89-) ({..,e;z.} t = Y-)/Cn-2-)/(1-rz.) ~.C}(pll (tp/cl- 'i<,tl) t OI = Wt.. Cctll'\ njrd Ht> at- 'ol ~.e.q.. 0 3,143 -}?-Vo.lt..u < OCJOS (8) d. Find the 95% prediction interval for the diameter of the trunk when the age of the tree is 30 years. Explain the meaning of this prediction interval. Th..<.,,loC>(1-~}'f. rr~d., t "'+. ~ Y +to( S \/1,_.L +()(!1-'X) 2. - A.- e ~ ~l' x.-:il.)'- o( =.(I s- ol..1 = ) df- = ~ t-. ()l..s- :: ~ ~ ~ -;-\'l./ _ ~ '+38 sscn.s1.d) = ~t.y-y)'-- ( ~c)(-3i...)ly-vh, ~ot.-j<) = t#12.- m =.<::2.1'ttos- Se- - J :SS(rHid)/(f'l-1..) = 2., q f = 'S. 7-// Z"''- L( 30) = 3 /, l'f 7-'?-. q 5' /. f rul.'e--f-, l/n i"'ftor u...i '-1 : 3 I I q q. 41- '( '2.. Cf IP so). J I +..L8 + C 3o - 2. W '\.. ' - ' y 8S~ (5) e. Find the coefficient of determination and explain its meaning. 1 q2.3t-.. o' tfv._ (/~~~ ""' +r-...,,,..1<. cltc. m..hy- Ct'VI ~ exp lai1111d J.,') r 'a; ~~~ JLJaftt?vii'1t f' wrtt. ay.

3 Statistics 100 FINAL page3 2. Researchers in an agricultural college want to test a new variety of corn seed, which they have developed. They got eight farmers to plant the new seed and another seven farmers to plant old seed. The number of bushels per acre each farmer obtained are: New Seed: 130 Old Seed: Is the new seed better than the old seed in terms of the yield? (3) a. State the null and alternative hypotheses. J-1 o ~ ~, -==- ft I-<, = h'lt.&i"', Yt'(.lcl ("" 1k.. n.t. w.l).u... d.... old v,,..,,, (7) b. Ass4me the yields for the new and old seeds are normal, test at 0.05 level of significance. 9, = 130 S :i. = S c. z., 'I-. :i a'") z. =- ~. s~ 1 g ~ '"2-11./:z 'L 4.'lll 10.l f-- + " t = (Yr-Yz_)-o f HA. t', > /!2.- Hl.- - s~ s~ -+n,..-i._ I "l 0 - I 2..3 = f:' t l.f.n10 _ '3.2.C.SY (3) c. Find the p-value. P-val 1.4. oos-< -p-v... \1.AA <. o l

4 Statistics 100 FINAL page4 (7) d. Suppose the distribution of the yields for the new and old seeds are not normal. State the null and alternative hypotheses and test at 0.05 level. Ho! 7.A.e """,...,~ ""'bt-t.s/.u../s./o..cn a.,._._ -tlv. /:>OVY\t.{-,,.,- b.,tt, ~~tt,., H,q, ~ '/ of!- f'"\lw ~J '"" T # < #< ty\gvl ft.e..,. 0 l.l /l-u c.i ~J7~,~~ f I "2.Gj I 2...~ S" l<,+k'l.= hi" hz I I '1.5 I 2. '& I 2. '-I 0? I 3. o 1'2..S I 2. ( q 1..(. 1 : SS". S 0 t<'"=,s S"~.S"-+ ~ :8(~) S" c;,, =S Co ~ U.s = ss-.s- V) '-I 3. LAI~ Cc.i"' r; J et t \-\ 0 aj o s- ~~ A digital instrument is used to test the acid level of a particular drug under three different temperatures. Six replicates of each experiment are run in the laboratory and the results are shown below: Temperature Replicate The between sum of square is 10,474 and the within sum of squares is 12,670. ( 4) a. Write down the analysis of variance model and explain its components. y,. p + c. + f, - l l) I ~ ~ ) I 1. 1-# obj. ff-tit VYI &a.,,.tl c( t[,.,...,.,,y...{ -rt. +Y"t'll. ~ rt\.tll"\ -(- H-. t-...-l'r.t.

5 Statistics 100 FINAL page5 (10) b. Construct the ANOV A table. Sot.( 'r"'-4.. d~ SS fv1.s F - -- Te.h'\ p. 2 l(j41-4 :)L 3 "f ~./ q '1'.. y-y-" V' 1'5" l2g-,7(j 84s I TeitA I I? 2.3 l'+lf I (5) c. Perform a global F test at 0.05 level and explain the result. Ms ( bdwq,,,.., F = t--15 (Witt.. I... ) - 5 L... '.l? 8 <+ s- d (.. == 2. Cl...,t.( I S- F. (Js = 3., ~ (3) d. Find the p... value for the F.. test. 0 (8) e. Construct a 95% confidence interval for the difference between means in temperatures 50 and 80. Explain the meaning of this confidence interval. ;Ju. looc1...q.)'/ ~ cacnt~, ""tt.v-v~\ t..., Y.f - Y_,,! +"'h ys;<~-+~"",\ +-, 6 2..r ==- 2.. I 3 I /Ju_ ct.r '/. ~ cj..~u. (1'-14.~3s:s-8(:.) t""li<'vvej Vi ± 2.l.?1{~4s-(1;-+{-~ 5" 2 ' g :? ~ '1 'i: 3 s ' 7 G. C/- y ~ ( 2 3 ' 0 l,&'1) '14 '5 '111.f) IA)(. CUR q S- /. ~ t-l~t- fatt-f 1/,-r_ di_//. in ~a"" Ct C.c'C.I ~ (rr -+e~pe.~~&ar-11 S-o a... d So (..., b<twu.n z:s.ol.&t\ e>i... J t;4.!s'i7<-t.

6 Statistics 100 FINAL page6 4. A study was conducted to observe the relationship between the emotional stability and talent. One huncl~d individuals were selected randomly and the following cross-classification was obtained. Emotional Stability Stable Unstable To-\--. I Talent ( ~ Artist 10.c.) 16 (IOolt\ 2. <.. Musical genius 20 '2.0. 4) 14 {I J, C.J 34 Math 30 ('2.1..() 10 (IC.) 4-o,,,..,.,,.., G:.o I 40 oo (2) a. State the null and alternative hypotheses in words. I-lo? G. mofi V"'l,J /l4,,,.l:til1+j ~ L',,,cle~~Jc...J- of -J.Qle.-if /-1,q : J-1 0 "" Yl uf tv-1,.4...t (10) b. Use the chi-squares procedure to test the hypothesis stated in (a) at 0.01 level. d. F; ( r -1 )l IL -I ) r- ( '3 - I ){ 2-1) = 2 \. X.01 - Cf.2. I W t. Ca n ne-'t Y- ~)! d \-\ u Cl/{. o I ~el_. (3) c. What is the p-value?. al< t-ve..lt,a..1 ( O 2. A Brief solution of the regression example given in Lecture 24: x-bar - 2, y-bar = 14 I: (x - i)2 = 8, I:(y-y) 2..; 144, I(x - x}(y-y) = 32. b1"= 4, ho == 6, Y" = 6 + 4x., SS(resid) = 16, Se= 1.633, SbJ =.5774, t = , r =.9428.

Executive Committee and Officers ( )

Executive Committee and Officers ( ) Gifted and Talented International V o l u m e 2 4, N u m b e r 2, D e c e m b e r, 2 0 0 9. G i f t e d a n d T a l e n t e d I n t e r n a t i o n a2 l 4 ( 2), D e c e m b e r, 2 0 0 9. 1 T h e W o r

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

APPH 4200 Physics of Fluids

APPH 4200 Physics of Fluids APPH 42 Physics of Fluids Problem Solving and Vorticity (Ch. 5) 1.!! Quick Review 2.! Vorticity 3.! Kelvin s Theorem 4.! Examples 1 How to solve fluid problems? (Like those in textbook) Ç"Tt=l I $T1P#(

More information

Exhibit 2-9/30/15 Invoice Filing Page 1841 of Page 3660 Docket No

Exhibit 2-9/30/15 Invoice Filing Page 1841 of Page 3660 Docket No xhibit 2-9/3/15 Invie Filing Pge 1841 f Pge 366 Dket. 44498 F u v 7? u ' 1 L ffi s xs L. s 91 S'.e q ; t w W yn S. s t = p '1 F? 5! 4 ` p V -', {} f6 3 j v > ; gl. li -. " F LL tfi = g us J 3 y 4 @" V)

More information

SOUTHWESTERN ELECTRIC POWER COMPANY SCHEDULE H-6.1b NUCLEAR UNIT OUTAGE DATA. For the Test Year Ended March 31, 2009

SOUTHWESTERN ELECTRIC POWER COMPANY SCHEDULE H-6.1b NUCLEAR UNIT OUTAGE DATA. For the Test Year Ended March 31, 2009 Schedule H-6.lb SOUTHWSTRN LCTRIC POWR COMPANY SCHDUL H-6.1b NUCLAR UNIT OUTAG DATA For the Test Year nded March 31, 29 This schedule is not applicable to SVvPCO. 5 Schedule H-6.1 c SOUTHWSTRN LCTRIC POWR

More information

H NT Z N RT L 0 4 n f lt r h v d lt n r n, h p l," "Fl d nd fl d " ( n l d n l tr l t nt r t t n t nt t nt n fr n nl, th t l n r tr t nt. r d n f d rd n t th nd r nt r d t n th t th n r lth h v b n f

More information

Colby College Catalogue

Colby College Catalogue Colby College Digital Commons @ Colby Colby Catalogues College Archives: Colbiana Collection 1871 Colby College Catalogue 1871-1872 Colby College Follow this and additional works at: http://digitalcommonscolbyedu/catalogs

More information

STAB27-Winter Term test February 18,2006. There are 14 pages including this page. Please check to see you have all the pages.

STAB27-Winter Term test February 18,2006. There are 14 pages including this page. Please check to see you have all the pages. STAB27-Winter 2006 Term test February 8,2006 Last Name: First Name: Student #: Tutorial Section / Room: Dayffime (Tutorial): INSTRUCTIONS Duration: hour, 45 minutes Statistical table(s) attached at the

More information

i;\-'i frz q > R>? >tr E*+ [S I z> N g> F 'x sa :r> >,9 T F >= = = I Y E H H>tr iir- g-i I * s I!,i --' - = a trx - H tnz rqx o >.F g< s Ire tr () -s

i;\-'i frz q > R>? >tr E*+ [S I z> N g> F 'x sa :r> >,9 T F >= = = I Y E H H>tr iir- g-i I * s I!,i --' - = a trx - H tnz rqx o >.F g< s Ire tr () -s 5 C /? >9 T > ; '. ; J ' ' J. \ ;\' \.> ). L; c\ u ( (J ) \ 1 ) : C ) (... >\ > 9 e!) T C). '1!\ /_ \ '\ ' > 9 C > 9.' \( T Z > 9 > 5 P + 9 9 ) :> : + (. \ z : ) z cf C : u 9 ( :!z! Z c (! $ f 1 :.1 f.

More information

APPH 4200 Physics of Fluids

APPH 4200 Physics of Fluids APPH 4200 Physics of Fluids Rotating Fluid Flow October 6, 2011 1.!! Hydrostatics of a Rotating Water Bucket (again) 2.! Bath Tub Vortex 3.! Ch. 5: Problem Solving 1 Key Definitions & Concepts Ω U Cylindrical

More information

Linear Correlation and Regression Analysis

Linear Correlation and Regression Analysis Linear Correlation and Regression Analysis Set Up the Calculator 2 nd CATALOG D arrow down DiagnosticOn ENTER ENTER SCATTER DIAGRAM Positive Linear Correlation Positive Correlation Variables will tend

More information

PHYS 232 QUIZ minutes.

PHYS 232 QUIZ minutes. / PHYS 232 QUIZ 1 02-18-2005 50 minutes. This quiz has 3 questions. Please show all your work. If your final answer is not correct, you will get partial credit based on your work shown. You are allowed

More information

4 8 N v btr 20, 20 th r l f ff nt f l t. r t pl n f r th n tr t n f h h v lr d b n r d t, rd n t h h th t b t f l rd n t f th rld ll b n tr t d n R th

4 8 N v btr 20, 20 th r l f ff nt f l t. r t pl n f r th n tr t n f h h v lr d b n r d t, rd n t h h th t b t f l rd n t f th rld ll b n tr t d n R th n r t d n 20 2 :24 T P bl D n, l d t z d http:.h th tr t. r pd l 4 8 N v btr 20, 20 th r l f ff nt f l t. r t pl n f r th n tr t n f h h v lr d b n r d t, rd n t h h th t b t f l rd n t f th rld ll b n

More information

Colby College Catalogue

Colby College Catalogue Colby College Digital Commons @ Colby Colby Catalogues College Archives: Colbiana Collection 1872 Colby College Catalogue 1872-1873 Colby College Follow this and additional works at: http://digitalcommonscolbyedu/catalogs

More information

AMS 315/576 Lecture Notes. Chapter 11. Simple Linear Regression

AMS 315/576 Lecture Notes. Chapter 11. Simple Linear Regression AMS 315/576 Lecture Notes Chapter 11. Simple Linear Regression 11.1 Motivation A restaurant opening on a reservations-only basis would like to use the number of advance reservations x to predict the number

More information

Agenda Rationale for ETG S eek ing I d eas ETG fram ew ork and res u lts 2

Agenda Rationale for ETG S eek ing I d eas ETG fram ew ork and res u lts 2 Internal Innovation @ C is c o 2 0 0 6 C i s c o S y s t e m s, I n c. A l l r i g h t s r e s e r v e d. C i s c o C o n f i d e n t i a l 1 Agenda Rationale for ETG S eek ing I d eas ETG fram ew ork

More information

Grilled it ems are prepared over real mesquit e wood CREATE A COMBO STEAKS. Onion Brewski Sirloin * Our signature USDA Choice 12 oz. Sirloin.

Grilled it ems are prepared over real mesquit e wood CREATE A COMBO STEAKS. Onion Brewski Sirloin * Our signature USDA Choice 12 oz. Sirloin. TT & L Gl v l q T l q TK v i f i ' i i T K L G ' T G!? Ti 10 (Pik 3) -F- L P ki - ik T ffl i zzll ik Fi Pikl x i f l $3 (li 2) i f i i i - i f i jlñ i 84 6 - f ki i Fi 6 T i ffl i 10 -i i fi & i i ffl

More information

r(j) -::::.- --X U.;,..;...-h_D_Vl_5_ :;;2.. Name: ~s'~o--=-i Class; Date: ID: A

r(j) -::::.- --X U.;,..;...-h_D_Vl_5_ :;;2.. Name: ~s'~o--=-i Class; Date: ID: A Name: ~s'~o--=-i Class; Date: U.;,..;...-h_D_Vl_5 _ MAC 2233 Chapter 4 Review for the test Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Find the derivative

More information

necessita d'interrogare il cielo

necessita d'interrogare il cielo gigi nei necessia d'inegae i cie cic pe sax span s inuie a dispiegaa fma dea uce < affeandi ves i cen dea uce isnane " sienzi dei padi sie veic dei' anima 5 J i f H 5 f AL J) i ) L '3 J J "' U J J ö'

More information

Knowledge Fusion: An Approach to Time Series Model Selection Followed by Pattern Recognition

Knowledge Fusion: An Approach to Time Series Model Selection Followed by Pattern Recognition LA-3095-MS Knwledge Fusin: An Appch t Tie Seies Mdel Selectin Fllwed by Ptten Recgnitin Ls Als N A T I O N A L L A B O R A T O R Y Ls Als Ntinl Lbty is peted by the Univesity f Clifni f the United Sttes

More information

(tnaiaun uaejna) o il?smitfl?^ni7wwuiinuvitgviisyiititvi2a-a a imaviitjivi5a^ qw^ww^i fiaa!i-j?s'u'uil?g'ijimqwuwiijami.wti. a nmj 1,965,333.

(tnaiaun uaejna) o il?smitfl?^ni7wwuiinuvitgviisyiititvi2a-a a imaviitjivi5a^ qw^ww^i fiaa!i-j?s'u'uil?g'ijimqwuwiijami.wti. a nmj 1,965,333. 0 fltu77jjiimviu«7mi^ gi^"ijhm?'ijjw?flfi^ V m 1 /14 il?mitfl?^i7wwuiinuvitgviiyiititvi2- imviitvi^ qw^ww^i fi!i-j?'u'uil?g'iqwuwiijmi.wti twwrlf^ imii2^

More information

'NOTAS"CRITICAS PARA UNA TEDRIA DE M BUROCRACIA ESTATAL * Oscar Oszlak

'NOTASCRITICAS PARA UNA TEDRIA DE M BUROCRACIA ESTATAL * Oscar Oszlak OVí "^Ox^ OqAÍ"^ Dcument SD-11 \ 'NOTAS"CRTCAS PARA UNA TEDRA DE M BUROCRACA ESTATAL * Oscr Oszlk * El presente dcument que se reprduce pr us exclusv de ls prtcpntes de curss de Prrms de Cpctcón, se h

More information

0 t b r 6, 20 t l nf r nt f th l t th t v t f th th lv, ntr t n t th l l l nd d p rt nt th t f ttr t n th p nt t th r f l nd d tr b t n. R v n n th r

0 t b r 6, 20 t l nf r nt f th l t th t v t f th th lv, ntr t n t th l l l nd d p rt nt th t f ttr t n th p nt t th r f l nd d tr b t n. R v n n th r n r t d n 20 22 0: T P bl D n, l d t z d http:.h th tr t. r pd l 0 t b r 6, 20 t l nf r nt f th l t th t v t f th th lv, ntr t n t th l l l nd d p rt nt th t f ttr t n th p nt t th r f l nd d tr b t n.

More information

Exam I CH 301H Fall '10

Exam I CH 301H Fall '10 Exam I CH 301H Fall '10 Vanden Bout Name: ---L\(...JO-E...;,l Carefully read all the problems (your exam should have 12 problems) Show all your work on numerical problems Clearly mark your answers If you

More information

Chapter 30 Design and Analysis of

Chapter 30 Design and Analysis of Chapter 30 Design and Analysis of 2 k DOEs Introduction This chapter describes design alternatives and analysis techniques for conducting a DOE. Tables M1 to M5 in Appendix E can be used to create test

More information

UNIVERSITY OF SWAZILAND MAIN EXAMINATION PAPER 2015 PROBABILITY AND STATISTICS ANSWER ANY FIVE QUESTIONS.

UNIVERSITY OF SWAZILAND MAIN EXAMINATION PAPER 2015 PROBABILITY AND STATISTICS ANSWER ANY FIVE QUESTIONS. UNIVERSITY OF SWAZILAND MAIN EXAMINATION PAPER 2015 TITLE OF PAPER PROBABILITY AND STATISTICS COURSE CODE EE301 TIME ALLOWED 3 HOURS INSTRUCTIONS ANSWER ANY FIVE QUESTIONS. REQUIREMENTS SCIENTIFIC CALCULATOR

More information

WALL D*TA PRINT-OUT. 3 nmth ZONE

WALL D*TA PRINT-OUT. 3 nmth ZONE T A B L E A 4. 3 G L A S S D A T A P R I N T - O U T H T C L».>qth» H e ig h t n u «b»r C L A S S D A T A P R I N T O U T it************************************ 1*q o v»rh # n g recm oi*ion*l orient n

More information

Statistics: CI, Tolerance Intervals, Exceedance, and Hypothesis Testing. Confidence intervals on mean. CL = x ± t * CL1- = exp

Statistics: CI, Tolerance Intervals, Exceedance, and Hypothesis Testing. Confidence intervals on mean. CL = x ± t * CL1- = exp Statistics: CI, Tolerance Intervals, Exceedance, and Hypothesis Lecture Notes 1 Confidence intervals on mean Normal Distribution CL = x ± t * 1-α 1- α,n-1 s n Log-Normal Distribution CL = exp 1-α CL1-

More information

264m. Raggengill Gilkerscleuch. Abington. 250m. Cottage. Iss. Mast. 246m. TER R AC E 240m OO KE TE H U N TE COLEBROOKE. Over Abington STATION.

264m. Raggengill Gilkerscleuch. Abington. 250m. Cottage. Iss. Mast. 246m. TER R AC E 240m OO KE TE H U N TE COLEBROOKE. Over Abington STATION. I 4 4 I I L KY t lttio F 9 ott v bito 4 4 F L ii 3 lui 1 p F L F I I 9 F L I LK i i tip i 9 6 v bito U l K L 6 ott bito i 5 1 5 9 i oo 8 4 6 otl it o ov b i o 116-3 ott 6 i i ollt u o v bito 4 lo i 6 v

More information

NO-Sl~l 966 RN INTERPRETiTION OF THE 02 AMGR ELECTRON SPECTRUNl(U) 1/1

NO-Sl~l 966 RN INTERPRETiTION OF THE 02 AMGR ELECTRON SPECTRUNl(U) 1/1 NOSl~l 966 RN INTERPRETiTION OF THE 02 AMGR ELETRON SPETRUNl(U) 1/1 MRSHINGTON UNIV MRSHINOTON D DEPT OF HEMISTRY UNSSIIEDH SANDE ET AL. OT 95 TR2S NSSSI4BSK9852 FO74 N 1 tgeorge UASIIE G? N L L. Q.. 1111111

More information

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c L i f e t i m e M a n a g e m e n t o f F l a-b s ah s e d S S D s U s i n g R e c o v e r-a y w a r e D y n a m i c T h r o t t l i n g S u n g j i n L e, e T a e j i n K i m, K y u n g h o, Kainmd J

More information

Geometry Chapter 8: Area Review PA Anchors: A3; B2; Cl. .1.t+~4 -~-J. ""T... Sl. J":..2.l.. -+-Jw. A =- A(~)'" ~ A :..!w-l-~

Geometry Chapter 8: Area Review PA Anchors: A3; B2; Cl. .1.t+~4 -~-J. T... Sl. J:..2.l.. -+-Jw. A =- A(~)' ~ A :..!w-l-~ Geometry Chapter 8: Area Review PA Anchors: A3; B2; Cl 1. Find the missing value given BCDA is a rectangle. Perimeter = 62 cm Area =? V:. d.t-\" ~vj B.--- ---,c 17 em ~ J. ':. ~). -:: - '0..., rj ~ -;:,

More information

ON THE DENSITY OF SOME SEQUENCES OF INTEGERS P. ERDOS

ON THE DENSITY OF SOME SEQUENCES OF INTEGERS P. ERDOS ON THE DENSITY OF SOME SEQUENCES OF INTEGERS P. ERDOS Let ai

More information

Intro to Linear Regression

Intro to Linear Regression Intro to Linear Regression Introduction to Regression Regression is a statistical procedure for modeling the relationship among variables to predict the value of a dependent variable from one or more predictor

More information

Intro to Linear Regression

Intro to Linear Regression Intro to Linear Regression Introduction to Regression Regression is a statistical procedure for modeling the relationship among variables to predict the value of a dependent variable from one or more predictor

More information

Humanistic, and Particularly Classical, Studies as a Preparation for the Law

Humanistic, and Particularly Classical, Studies as a Preparation for the Law University of Michigan Law School University of Michigan Law School Scholarship Repository Articles Faculty Scholarship 1907 Humanistic, and Particularly Classical, Studies as a Preparation for the Law

More information

ilpi4ka3 MuHucrepcrBo o6pasobahufl Huxeropollcnofi o Onacrrl rocyaapctnennofi rrorosofi s2015 ro4y fl? /CI.?G

ilpi4ka3 MuHucrepcrBo o6pasobahufl Huxeropollcnofi o Onacrrl rocyaapctnennofi rrorosofi s2015 ro4y fl? /CI.?G MuucpcB 6psBul uxpllci cl ilpk l? /C.?G ' uxui P' 06 YP [pcjb CCTB qb cypci KMqui KMcc uxpqcci 6cu 015 Y' B cstctb yktm 1 lpxk plbl' 'cypcsuii u "c1 6p:JbbM ppmmll Cp 6[ Sp: ybpx pkm MuucpcTB 6p:ux 1 yk

More information

p r * < & *'& ' 6 y S & S f \ ) <» d «~ * c t U * p c ^ 6 *

p r * < & *'& ' 6 y S & S f \ ) <» d «~ * c t U * p c ^ 6 * B. - - F -.. * i r > --------------------------------------------------------------------------- ^ l y ^ & * s ^ C i$ j4 A m A ^ v < ^ 4 ^ - 'C < ^y^-~ r% ^, n y ^, / f/rf O iy r0 ^ C ) - j V L^-**s *-y

More information

46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l pp n nt n th

46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l pp n nt n th n r t d n 20 0 : T P bl D n, l d t z d http:.h th tr t. r pd l 46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l

More information

STAT Chapter 11: Regression

STAT Chapter 11: Regression STAT 515 -- Chapter 11: Regression Mostly we have studied the behavior of a single random variable. Often, however, we gather data on two random variables. We wish to determine: Is there a relationship

More information

l I I I I I Mechanics M 1 Advanced/ Advanced Subsidiary Pearson Edexcel P46705A II II I I I I II PEARSON

l I I I I I Mechanics M 1 Advanced/ Advanced Subsidiary Pearson Edexcel P46705A II II I I I I II PEARSON Write your name here Su rn ame Other names Pearson Edexcel nternational Advanced Level r Centre Number l Mechanics M 1 Advanced/ Advanced Subsidiary Candidate Number "' Wednesday 8 June 2016 Morning Time:

More information

1. The Multivariate Classical Linear Regression Model

1. The Multivariate Classical Linear Regression Model Business School, Brunel University MSc. EC550/5509 Modelling Financial Decisions and Markets/Introduction to Quantitative Methods Prof. Menelaos Karanasos (Room SS69, Tel. 08956584) Lecture Notes 5. The

More information

Total Possible Points = 150 Points. 1) David has 980 yards of fencing and wishes to enclose a rectangular area. (2.5 points) + '3 b. 7 + Ib+3, tf-.

Total Possible Points = 150 Points. 1) David has 980 yards of fencing and wishes to enclose a rectangular area. (2.5 points) + '3 b. 7 + Ib+3, tf-. MA180 Professor Fred Katiraie Test IT Form A (Fall 2007) Name: Total Possible Points = 150 Points 1) David has 980 yards of fencing and wishes to enclose a rectangular area. (2.5 points) a) Express the

More information

MAC 1147 Final Exam Review

MAC 1147 Final Exam Review MAC 1147 Final Exam Review nstructions: The final exam will consist of 15 questions plu::; a bonus problem. Some questions will have multiple parts and others will not. Some questions will be multiple

More information

Nrer/ \f l xeaoe Rx RxyrZH IABXAP.qAATTAJI xvbbqaat KOMnAHT1. rvfiqgrrox 3Axl4 Pn br H esep fiyraap: qa/oq YnaaH6aarap xor

Nrer/ \f l xeaoe Rx RxyrZH IABXAP.qAATTAJI xvbbqaat KOMnAHT1. rvfiqgrrox 3Axl4 Pn br H esep fiyraap: qa/oq YnaaH6aarap xor 4 e/ f l ee R RyZH BXP.J vbb KOMnH1 vfig 3l4 Pn b H vlun @*,/capn/t eep fiyaap: a/ YnaaH6aaap eneaneee 6ana tyail CaHafiu cafigun 2015 Hu 35 nyaap l'p 6ana4caH "Xe4ee a aylzh abap gaan" XKailH gypeu"uzn

More information

Direction: This test is worth 250 points and each problem worth points. DO ANY SIX

Direction: This test is worth 250 points and each problem worth points. DO ANY SIX Term Test 3 December 5, 2003 Name Math 52 Student Number Direction: This test is worth 250 points and each problem worth 4 points DO ANY SIX PROBLEMS You are required to complete this test within 50 minutes

More information

i of 6. PERFORMING ORG. REPORT NUMBER Approved for public Dtatibuft Unlimited

i of 6. PERFORMING ORG. REPORT NUMBER Approved for public Dtatibuft Unlimited SECURITY CLASSIFICATION OF THIS PAGE (When Data Entered) REPORT DOCUMENTATION PAGE READ INSTRUCTIONS BEFORE COMPLETING FORM I. REPORT NUMBER 2. GOVT ACCESSION NO. 3. RECIPIENT'S CATALOG NUMBER WP-MDSGA-65-4

More information

(308 ) EXAMPLES. 1. FIND the quotient and remainder when. II. 1. Find a root of the equation x* = +J Find a root of the equation x 6 = ^ - 1.

(308 ) EXAMPLES. 1. FIND the quotient and remainder when. II. 1. Find a root of the equation x* = +J Find a root of the equation x 6 = ^ - 1. (308 ) EXAMPLES. N 1. FIND the quotient and remainder when is divided by x 4. I. x 5 + 7x* + 3a; 3 + 17a 2 + 10* - 14 2. Expand (a + bx) n in powers of x, and then obtain the first derived function of

More information

GOLDEN SEQUENCES OF MATRICES WITH APPLICATIONS TO FIBONACCI ALGEBRA

GOLDEN SEQUENCES OF MATRICES WITH APPLICATIONS TO FIBONACCI ALGEBRA GOLDEN SEQUENCES OF MATRICES WITH APPLICATIONS TO FIBONACCI ALGEBRA JOSEPH ERCOLANO Baruch College, CUNY, New York, New York 10010 1. INTRODUCTION As is well known, the problem of finding a sequence of

More information

o C *$ go ! b», S AT? g (i * ^ fc fa fa U - S 8 += C fl o.2h 2 fl 'fl O ' 0> fl l-h cvo *, &! 5 a o3 a; O g 02 QJ 01 fls g! r«'-fl O fl s- ccco

o C *$ go ! b», S AT? g (i * ^ fc fa fa U - S 8 += C fl o.2h 2 fl 'fl O ' 0> fl l-h cvo *, &! 5 a o3 a; O g 02 QJ 01 fls g! r«'-fl O fl s- ccco > p >>>> ft^. 2 Tble f Generl rdnes. t^-t - +«0 -P k*ph? -- i t t i S i-h l -H i-h -d. *- e Stf H2 t s - ^ d - 'Ct? "fi p= + V t r & ^ C d Si d n. M. s - W ^ m» H ft ^.2. S'Sll-pl e Cl h /~v S s, -P s'l

More information

Jsee x dx = In Isec x + tanxl + C Jcsc x dx = - In I cscx + cotxl + C

Jsee x dx = In Isec x + tanxl + C Jcsc x dx = - In I cscx + cotxl + C MAC 2312 Final Exam Review Instructions: The Final Exam will consist of 15 questions plus a bonus problem. All questions will be multiple choice, which will be graded partly on whether or not you circle

More information

30th 30th 29th 28th. 27th. Easte Rail. Howard. 25th. 2th. river Whiteriv Wh. Aca. 21st. 18th. 16th Anvil View. Prefontaine. Rif. Rifle.

30th 30th 29th 28th. 27th. Easte Rail. Howard. 25th. 2th. river Whiteriv Wh. Aca. 21st. 18th. 16th Anvil View. Prefontaine. Rif. Rifle. RFTA REGIOAL IYLE, EDETRIA AD TRAIT AE LA Ri Til vi imtt li ug tw 3 Rifl Rifl w li t ill ilt Rig F Ttti Auit iccl ti t l D 3 3 2 2 t R Et Ril il t 2t v i w 2 ci Ac t v 2 iv itiv F t w 2 24 Rifl ig cl cl

More information

(ril"s::lli '*Y, ,dr4{n. w.j. ",;:ii:{..._, I i,ai I. AOEP'IIICKOTO MyHI4TIUIIA.JTbHO O PAI,rOrrA nepmckoto KpA.fl TIOCTAHOBJTEHPIE

(rils::lli '*Y, ,dr4{n. w.j. ,;:ii:{..._, I i,ai I. AOEP'IIICKOTO MyHI4TIUIIA.JTbHO O PAI,rOrrA nepmckoto KpA.fl TIOCTAHOBJTEHPIE '*Y w.j TOCTOBJTEPE.MCTPU{ OEP'CKOTO MyTU.JTbO O PrOrr EPMCKOTO Kp.fl 26.02.20t3 e387 -l fo uecet Meellf [epe.rer 3eMeJrbbr ycrkb rrperr3ebr r pecrbjreg MferbM cembfl Mr ytbeprme r ctbjrerrem MrcTpr r

More information

Lecture 11: Simple Linear Regression

Lecture 11: Simple Linear Regression Lecture 11: Simple Linear Regression Readings: Sections 3.1-3.3, 11.1-11.3 Apr 17, 2009 In linear regression, we examine the association between two quantitative variables. Number of beers that you drink

More information

AGEC 621 Lecture 16 David Bessler

AGEC 621 Lecture 16 David Bessler AGEC 621 Lecture 16 David Bessler This is a RATS output for the dummy variable problem given in GHJ page 422; the beer expenditure lecture (last time). I do not expect you to know RATS but this will give

More information

A comparative study of ordinary cross-validation, r-fold cross-validation and the repeated learning-testing methods

A comparative study of ordinary cross-validation, r-fold cross-validation and the repeated learning-testing methods Biometrika (1989), 76, 3, pp. 503-14 Printed in Great Britain A comparative study of ordinary cross-validation, r-fold cross-validation and the repeated learning-testing methods BY PRABIR BURMAN Division

More information

ANCOVA. Psy 420 Andrew Ainsworth

ANCOVA. Psy 420 Andrew Ainsworth ANCOVA Psy 420 Andrew Ainsworth What is ANCOVA? Analysis of covariance an extension of ANOVA in which main effects and interactions are assessed on DV scores after the DV has been adjusted for by the DV

More information

2.57 when the critical value is 1.96, what decision should be made?

2.57 when the critical value is 1.96, what decision should be made? Math 1342 Ch. 9-10 Review Name SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. 9.1 1) If the test value for the difference between the means of two large

More information

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction F Dtto Roto Lr Alr F Roto C Y I Ursty O solto: tto o l trs s s ys os ot. Dlt to t to ltpl ws. F Roto Aotr ppro: ort y rry s tor o so E.. 56 56 > pot 6556- stol sp A st o s t ps to ollto o pots ts sp. F

More information

CHAPTER 11 Vector-Valued Functions

CHAPTER 11 Vector-Valued Functions CHAPTER Vector-Valued Functions Section. Vector-Valued Functions...................... 9 Section. Differentiation and Integration of Vector-Valued Functions.... Section. Velocit and Acceleration.....................

More information

Cy/ 9. Pau JCs1 ce_ Moort- ARE YOU A CHO? Yes. ><'- \VbrrIe Ha li 0Z-Z. IVY r 5. 4 v.; ( r e cep X Oaf' , (-24.

Cy/ 9. Pau JCs1 ce_ Moort- ARE YOU A CHO? Yes. ><'- \VbrrIe Ha li 0Z-Z. IVY r 5. 4 v.; ( r e cep X Oaf' , (-24. \X stmoniauniverstry Cy/ 9 Miscellaneous Hazardous Waste/ Lab Safety! Hazardous Communicatii DATE a01.3 TME 3o N - // Pau JCs1 ce_ Moort- f: Cg6a-ti- Zbei:Cif5f"e Pawl 11.41/.../ ' ARE YOU ARE YOU A >

More information

" M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2

 M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2 Notation and Equations for Final Exam Symbol Definition X The variable we measure in a scientific study n The size of the sample N The size of the population M The mean of the sample µ The mean of the

More information

,W.(.1*i x. Itr ::r:: i# A=,lrr. 'l ' rykfu. -[ *' 5 ]{, X/,i "# rrlrr,;- "h. K rn. f'e-s **1,:i' $ *' ##a. "+ c)4mls ( d)3.4 v * fr: lt-) t'..].

,W.(.1*i x. Itr ::r:: i# A=,lrr. 'l ' rykfu. -[ *' 5 ]{, X/,i # rrlrr,;- h. K rn. f'e-s **1,:i' $ *' ##a. + c)4mls ( d)3.4 v * fr: lt-) t'..]. General Physics II (PHYS 104) Exam 1: February 23,2012 Name: Multiple Choice (4 points each): Answer the following multiple choice questions. Clearly circle the response (or responses) that provides the

More information

( _ ~+-')(X+2.) ) _ (.Y.. + "~.Ct( M - )(-~ o + ~ - 0+ (-',2.) 17G 4-~ -.;t [-~/-4) U (-2,00)

( _ ~+-')(X+2.) ) _ (.Y.. + ~.Ct( M - )(-~ o + ~ - 0+ (-',2.) 17G 4-~ -.;t [-~/-4) U (-2,00) Algebra \I Pre AP Review Worksheet 11.1, 11.3, 11.6 Name ~+~ _ Use a sign chart to solve each inequality. Verify using a calculator. X2 +x-12 ~ 0 {-~ -'IJ 3 x+6 l. 2. --+1

More information

2 and F Distributions. Barrow, Statistics for Economics, Accounting and Business Studies, 4 th edition Pearson Education Limited 2006

2 and F Distributions. Barrow, Statistics for Economics, Accounting and Business Studies, 4 th edition Pearson Education Limited 2006 and F Distributions Lecture 9 Distribution The distribution is used to: construct confidence intervals for a variance compare a set of actual frequencies with expected frequencies test for association

More information

SEQUENTIAL TESTS FOR COMPOSITE HYPOTHESES

SEQUENTIAL TESTS FOR COMPOSITE HYPOTHESES [ 290 ] SEQUENTIAL TESTS FOR COMPOSITE HYPOTHESES BYD. R. COX Communicated by F. J. ANSCOMBE Beceived 14 August 1951 ABSTRACT. A method is given for obtaining sequential tests in the presence of nuisance

More information

Annoucements. MT2 - Review. one variable. two variables

Annoucements. MT2 - Review. one variable. two variables Housekeeping Annoucements MT2 - Review Statistics 101 Dr. Çetinkaya-Rundel November 4, 2014 Peer evals for projects by Thursday - Qualtrics email will come later this evening Additional MT review session

More information

W * 0 " 4,.' il i I fit A "; E. i I. tot.

W * 0  4,.' il i I fit A ; E. i I. tot. W * 0 il i I fit E. i I. tot. ",.' A "; Contents Questions Set Number Basic Indices Review of Index Laws Negative Indices Scientific Notation Answers Set Number Basic Indices Review of Index Laws Negative

More information

SAMPLING IN FIELD EXPERIMENTS

SAMPLING IN FIELD EXPERIMENTS SAMPLING IN FIELD EXPERIMENTS Rajender Parsad I.A.S.R.I., Library Avenue, New Delhi-0 0 rajender@iasri.res.in In field experiments, the plot size for experimentation is selected for achieving a prescribed

More information

CONTINUITY OF THE OPERATOR OF BEST UNIFORM APPROXIMATION BY BOUNDED ANALYTIC FUNCTIONS

CONTINUITY OF THE OPERATOR OF BEST UNIFORM APPROXIMATION BY BOUNDED ANALYTIC FUNCTIONS CONTINUITY OF THE OPERATOR OF BEST UNIFORM APPROXIMATION BY BOUNDED ANALYTIC FUNCTIONS M. PAPADIMITRAKIS Let T = {e id : 0 < 6 < In) and L 00 = L^iT), the space of all measurable essentially bounded complex-valued

More information

Analysis of Covariance. The following example illustrates a case where the covariate is affected by the treatments.

Analysis of Covariance. The following example illustrates a case where the covariate is affected by the treatments. Analysis of Covariance In some experiments, the experimental units (subjects) are nonhomogeneous or there is variation in the experimental conditions that are not due to the treatments. For example, a

More information

by Fdruary,2015 It may. kindly be eosured that a copy of deposit slip is supflied to this for All the Principals/HMs,

by Fdruary,2015 It may. kindly be eosured that a copy of deposit slip is supflied to this for All the Principals/HMs, .DBS(B) (10)/2014 3, /l 2' Oi.e he Depy Direr Hiher din Bilpr Diri Bilpr (). Tele phne /x 01978 2228 emil ddhebilpredinil.m '. Ded Bilpr 174001,rr he,. ' i i, lj by drry,201 All he rinipl/hm, Di Bilp"i)

More information

Regression Analysis. Table Relationship between muscle contractile force (mj) and stimulus intensity (mv).

Regression Analysis. Table Relationship between muscle contractile force (mj) and stimulus intensity (mv). Regression Analysis Two variables may be related in such a way that the magnitude of one, the dependent variable, is assumed to be a function of the magnitude of the second, the independent variable; however,

More information

18 BESSEL FUNCTIONS FOR LARGE ARGUMENTS. By M. Goldstein and R. M. Thaler

18 BESSEL FUNCTIONS FOR LARGE ARGUMENTS. By M. Goldstein and R. M. Thaler 18 BESSEL FUNCTIONS FOR LARGE ARGUMENTS Bessel Functions for Large Arguments By M. Goldstein R. M. Thaler Calculations of Bessel Functions of real order argument for large values of the argument can be

More information

Housing Market Monitor

Housing Market Monitor M O O D Y È S A N A L Y T I C S H o u s i n g M a r k e t M o n i t o r I N C O R P O R A T I N G D A T A A S O F N O V E M B E R İ Ī Ĭ Ĭ E x e c u t i v e S u m m a r y E x e c u t i v e S u m m a r y

More information

Collection of Formulae and Statistical Tables for the B2-Econometrics and B3-Time Series Analysis courses and exams

Collection of Formulae and Statistical Tables for the B2-Econometrics and B3-Time Series Analysis courses and exams Collection of Formulae and Statistical Tables for the B2-Econometrics and B3-Time Series Analysis courses and exams Lars Forsberg Uppsala University Spring 2015 Abstract This collection of formulae is

More information

III Illl i 111 III illlllill 111 Illlll

III Illl i 111 III illlllill 111 Illlll t - - - - - QUQMOLY"lJl) 7 M ft Fi t FQRMRT* ;ty- - / OC../^. l_.^... FIGURE: 4 III Illl i 111 III 111 111 illlllill 111 Illlll Areal \\Jct (p~~7 v~v t 42C81NW0009 eel COWIE 200 " - t ipa- - cs-a x s\-8.xjw

More information

Unit 6 - Introduction to linear regression

Unit 6 - Introduction to linear regression Unit 6 - Introduction to linear regression Suggested reading: OpenIntro Statistics, Chapter 7 Suggested exercises: Part 1 - Relationship between two numerical variables: 7.7, 7.9, 7.11, 7.13, 7.15, 7.25,

More information

Correlation. A statistics method to measure the relationship between two variables. Three characteristics

Correlation. A statistics method to measure the relationship between two variables. Three characteristics Correlation Correlation A statistics method to measure the relationship between two variables Three characteristics Direction of the relationship Form of the relationship Strength/Consistency Direction

More information

Contents 1. Contents

Contents 1. Contents Contents 1 Contents 1 One-Sample Methods 3 1.1 Parametric Methods.................... 4 1.1.1 One-sample Z-test (see Chapter 0.3.1)...... 4 1.1.2 One-sample t-test................. 6 1.1.3 Large sample

More information

Binary Logistic Regression

Binary Logistic Regression The coefficients of the multiple regression model are estimated using sample data with k independent variables Estimated (or predicted) value of Y Estimated intercept Estimated slope coefficients Ŷ = b

More information

Three Phase Asymmetrical Load Flow for Four-Wire Distribution Networks

Three Phase Asymmetrical Load Flow for Four-Wire Distribution Networks T Aytl Lo Flow o Fou-W Dtuto Ntwo M. Mo *, A. M. Dy. M. A Dtt o Eltl E, A Uvty o Toloy Hz Av., T 59, I * El: o8@yoo.o Att-- Mjoty o tuto two ul u to ul lo, yty to l two l ut. T tt o tuto yt ult y o ovt

More information

CHAPTER EVALUATING HYPOTHESES 5.1 MOTIVATION

CHAPTER EVALUATING HYPOTHESES 5.1 MOTIVATION CHAPTER EVALUATING HYPOTHESES Empirically evaluating the accuracy of hypotheses is fundamental to machine learning. This chapter presents an introduction to statistical methods for estimating hypothesis

More information

Linear Regression with 1 Regressor. Introduction to Econometrics Spring 2012 Ken Simons

Linear Regression with 1 Regressor. Introduction to Econometrics Spring 2012 Ken Simons Linear Regression with 1 Regressor Introduction to Econometrics Spring 2012 Ken Simons Linear Regression with 1 Regressor 1. The regression equation 2. Estimating the equation 3. Assumptions required for

More information

LSU Historical Dissertations and Theses

LSU Historical Dissertations and Theses Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1976 Infestation of Root Nodules of Soybean by Larvae of the Bean Leaf Beetle, Cerotoma Trifurcata

More information

Table of C on t en t s Global Campus 21 in N umbe r s R e g ional Capac it y D e v e lopme nt in E-L e ar ning Structure a n d C o m p o n en ts R ea

Table of C on t en t s Global Campus 21 in N umbe r s R e g ional Capac it y D e v e lopme nt in E-L e ar ning Structure a n d C o m p o n en ts R ea G Blended L ea r ni ng P r o g r a m R eg i o na l C a p a c i t y D ev elo p m ent i n E -L ea r ni ng H R K C r o s s o r d e r u c a t i o n a n d v e l o p m e n t C o p e r a t i o n 3 0 6 0 7 0 5

More information

ANDRI ADLER. Department of Iathelnatics. Chicago, IL U.S.A. ANDREY VOLODIN. Kazan University. Kazan Tatarstan. Russia

ANDRI ADLER. Department of Iathelnatics. Chicago, IL U.S.A. ANDREY VOLODIN. Kazan University. Kazan Tatarstan. Russia Internat. J. Math. & Math. Sci. VOL. 18 NO. (1995) 33-36 33 ON COMPLETE CONVERGENCE OF THE SUM OF A RANDOM NUMBER OF STABLE TYPE P RANDOM ELEMENTS ANDRI ADLER Department of Iathelnatics lllinfis Institute

More information

ON THE NUMBER OF NIVEN NUMBERS UP TO

ON THE NUMBER OF NIVEN NUMBERS UP TO ON THE NUMBER OF NIVEN NUMBERS UP TO Jean-Marie DeKoninck 1 D partement de Math matiques et de statistique, University Laval, Quebec G1K 7P4, Canada e-mail: jmdk@mat.ulaval.ca Nicolas Doyon Departement

More information

Lecture 26: Chapter 10, Section 2 Inference for Quantitative Variable Confidence Interval with t

Lecture 26: Chapter 10, Section 2 Inference for Quantitative Variable Confidence Interval with t Lecture 26: Chapter 10, Section 2 Inference for Quantitative Variable Confidence Interval with t t Confidence Interval for Population Mean Comparing z and t Confidence Intervals When neither z nor t Applies

More information

YORK UNIVERSITY. Faculty of Science and Engineering Faculty of Liberal Arts and Professional Studies MATH A Test #2.

YORK UNIVERSITY. Faculty of Science and Engineering Faculty of Liberal Arts and Professional Studies MATH A Test #2. YORK UNIVERSITY Faculty of Science and Engineering Faculty of Liberal Arts and Professional Studies MATH 317 6. A Test #2 June 27, 212 Surname (print): Given Name: Student No: Signature: INSTRUCTIONS:

More information

The Chi-Square Distributions

The Chi-Square Distributions MATH 03 The Chi-Square Distributions Dr. Neal, Spring 009 The chi-square distributions can be used in statistics to analyze the standard deviation of a normally distributed measurement and to test the

More information

STAT FINAL EXAM

STAT FINAL EXAM STAT101 2013 FINAL EXAM This exam is 2 hours long. It is closed book but you can use an A-4 size cheat sheet. There are 10 questions. Questions are not of equal weight. You may need a calculator for some

More information

Notes for Week 13 Analysis of Variance (ANOVA) continued WEEK 13 page 1

Notes for Week 13 Analysis of Variance (ANOVA) continued WEEK 13 page 1 Notes for Wee 13 Analysis of Variance (ANOVA) continued WEEK 13 page 1 Exam 3 is on Friday May 1. A part of one of the exam problems is on Predictiontervals : When randomly sampling from a normal population

More information

DEMAND ESTIMATION (PART III)

DEMAND ESTIMATION (PART III) BEC 30325: MANAGERIAL ECONOMICS Session 04 DEMAND ESTIMATION (PART III) Dr. Sumudu Perera Session Outline 2 Multiple Regression Model Test the Goodness of Fit Coefficient of Determination F Statistic t

More information

Unit5: Inferenceforcategoricaldata. 4. MT2 Review. Sta Fall Duke University, Department of Statistical Science

Unit5: Inferenceforcategoricaldata. 4. MT2 Review. Sta Fall Duke University, Department of Statistical Science Unit5: Inferenceforcategoricaldata 4. MT2 Review Sta 101 - Fall 2015 Duke University, Department of Statistical Science Dr. Çetinkaya-Rundel Slides posted at http://bit.ly/sta101_f15 Outline 1. Housekeeping

More information

The Chi-Square Distributions

The Chi-Square Distributions MATH 183 The Chi-Square Distributions Dr. Neal, WKU The chi-square distributions can be used in statistics to analyze the standard deviation σ of a normally distributed measurement and to test the goodness

More information

MA 351 Fall 2007 Exam #1 Review Solutions 1

MA 351 Fall 2007 Exam #1 Review Solutions 1 MA 35 Fall 27 Exam # Review Solutions THERE MAY BE TYPOS in these solutions. Please let me know if you find any.. Consider the two surfaces ρ 3 csc θ in spherical coordinates and r 3 in cylindrical coordinates.

More information

s e, which is large when errors are large and small Linear regression model

s e, which is large when errors are large and small Linear regression model Linear regression model we assume that two quantitative variables, x and y, are linearly related; that is, the the entire population of (x, y) pairs are related by an ideal population regression line y

More information