PRINT PRINT "AVERAGED OPERATING CONDITIONS FOR "jtstname* PRINT " m M U S m U W M H H W M N M M a n i H N M W M H M M M '

Size: px
Start display at page:

Download "PRINT PRINT "AVERAGED OPERATING CONDITIONS FOR "jtstname* PRINT " m M U S m U W M H H W M N M M a n i H N M W M H M M M '"

Transcription

1 4 r.y NEX I' V I F D a t a "A" 5570 THEN P R I N T "FINAL..1UTAL.S F t i». d e n y A v i n a s f PHASDEN 1dm / ( A v a t i r m s f 1a 60) 5500 P R I N T U S IN G "DDDD. DD, 1 X " s Y - 1, A v m a f 1 ow/ < Y - 1 ), T o t m a n s, A v v e l a v e ' ( Y - 1), A v p r e s. 1 a v e / ( Y - l ). A v a l r m s f l a 6 0 / ( Y - t ). A v v e l 6a v a / <Y - l ), A v p r e s 6 a v e / ( Y - l ). P h a s d e n y 5590 P R I N T " PRINT PR IN TE R IS 1 IF Dat:a = "A" T H E N G O T O INPUT " D O Y O U W A N T (A ) L L D A T A O R ( S ) E L E C T E D D A T A?....D a t a l IF Dat 1 1 <. "A" T H E N G O T O P R I N T E R IS 701 PRINT PRINT "AVERAGED OPERATING CONDITIONS FOR "jtstname PRINT " m M U S m U W M H H W M N M M a n i H N M W M H M M M ' PRUT COMPRESSORS: ":Compre'3 "soperat:" PRINT " O P E R A T O R : A T M O S P R E S : " ; Pr er.at m: "mmh G " P U N T "TEST DATE I : Date» " Pt^INT " T E S T NAME: TIME IN T E R V A L: " : Ti h i ; " M i n u t a m " " ; T tnam e;" T OTAL TEST TIME " ; T 2 ( P p )! 1 MINUTES" PRINT PRINT PRINT " L I N E TEMP C E L C " ; A v t m p a v e / ( Y - l ) PRINT " L I N E 1R 8 URI» " ; A v p r e s 1 a v s / <Y - l ) PRINT I E E D P R E S S U R E "S A v p r e & f a v e / ( Y - l ) PRINT " P R E S 2 (BMD1I " i A v p r e «2 n v e / ( Y - l ) PRINT " P R E S (BND2) ;A v p r e < s 3 a v e / (Y - l ) PRINT " P R E S (LINE)»"; A v p r e s 4 a v e / (Y-l PRINT " P R E S (BND3) ;A v p r e s S a v e / (Y PRINT " P R E S (BND4)»"! Avpr e s 6 a v e / ( Y i); T C P R E S 6» E N D 4" PRINT " P R E S 11<ND5) «" ( A v p r e s 7 a v e / (Y - l ) PRINT PRINT " P R E S S U R E D R O P L I N E K P A «";A v d e l p a v e / ( Y - 1) ; A v d e l b n d l a v e / (Y-l) PRINT " P R E S S U R E D R O P B N D 1 KPA PRINT " P R E S S U R E DROT- 6 N D 2 K P A "5 A vdel b n d 2 a v e / (Y-l) PRINT " P R E S S U R E D R G P B N D 3 K P A = " i A v d e l b n d 3 a v «/ (Y - l ) PRINT PRINT " V E L L I N " l A v v» n v «/ ( Y - l ) ; A v v e l a v e / ( Y 1 ) PRINT "VEL. F E E D E R (H M D 1) " j A v v e l 1 ' a v e / ( Y - l PR INI " V E L " " ; A v v e l3 a v e /(Y -l PRINT " V E L (BND2) " ; A v v e l 4,a v e / ( Y - t PRINT " V E L (LINE) IDND3) ";A v v e ls a v e / ( Y - l PRINT "VEL PRINT "VEL (D N D 4 ) "; A v v e l 6 a v e / ( Y - l PRINT " V E L ; Avvfsl 7 a v e / ( Y - l (DNDS) PI! INT PRINT "AVE MAG!: Ft O W R A T E K G / M I N " J A v m a s f l o w / ( Y - t ) PRINT 'AIR M A S S F L O W R A T E K G / M IN» " i A v a i r r n s f 1 a 6 0 / ( Y - l ) PRINT "PIIA3E D E N S I T Y ; Avm asf 1 aw/ ( A v a ir in s f 1 a 6 0 ) PRINT "T O TAL M A S S U O U V E Y E D KG " " Totm a' PRINT INPUT " C A L C U. A T E M O R E M A S S F L O W RATE S " I ", M s s ft IF M» h F» «" Y " T H E N II M ««f < >"N" T H E N G O T O 10 P RINT " P R O G R A M F I N I S H E D " M A S S S T O R A G E IS ":H P U X, 7 0 0, 0 " END S? ! i 36b / ' E CIH r

2 Vr -.V Vf Vf Vr V f VfVfVf Vf VfVfVfVf Vf v.' Vf >,.' V. Mr DIST USERID 001FRDP Vr Vf Vf VfVnVvfV/f Vf V'Vf Vf Vr.. i f Vf Vf V f Vf Vr Vf Vf File name: PLOTANG File type: SCRIPT File mode: AI This output was produced by the command: LASER PLOTANG SCRIPT AI ( S E T 7 NODUP COP 6 on 05/05/88 at 11:42:58 This file is on disk 001FRD and was last updated on 05/05/88 at 11:42:43

3 P R O G R A M M E TO PLOT O V E R L A Y E D G R A P H S OF ALL T R I B O L O G I C A L RESULTS; O P T I O N S D E V I C E = G D ' ) M 6 8 C D O M N IC K N A M E = A L T ; GOPTIONS DEV'CE=IBM3T79; DATA SCT07; S ET t, TA. TC; IF T E S T T C > 5 2 8t T Y P E T C - S C ; A D E R O T C = L O G ( A D E R O T C 1; V E L T C = L O G (V E L T C ); D A T A S6; SET D A T A. T C ; IF T E S T T & T Y P E T C ^ S 6 ; A D E R O T C - L O C < A D E R O T C ); V E L T C = L O G ( V E L T C ); D A T A T6; SET D A T A. T C ; IF T E S T T C > 5 2 & T Y P E T C = ' T 6 ; A D E R O T C = L O G ( A D E R O T C ); VELTC=LOC(VELTC); D A T A G6; S ET D A T A. T C ; IF T E S T T C > 5 2 8c T Y P E T C = ' G 6 ; A D E R O T C = L O C ( A D E R O T C ); V E L T C = L O G ( V L L T C ); D A T A GTO; SET DATA.TC; IT T t S T T C > 5 2 fc T Y P E T C = 'G T O 1 ; A D E R O T C = L O C ( A D E R O T C ); V E L T C = L O G ( V E L T C ); D A T A GT5; SET D A T A. T C ; IF T E S T T C > 5 2 & T Y P E T C = ' G T 5 ' ; A D E R O T C = L O C ( A D E R O T C ); V E L T C = L O G ( VELTC); D A T A E30; SET DATA.TC; IF T E S T T C > 5 2 & T Y P E T C = 'E 3 0 ' ; A D E P O T C = L O G ( A P E R O T C ); VELTC=LOG(VELTC); D A T A MS; SET DATA.MS; IF T E S T M S > 5 2 ; A D E R O M S = L O G ( A O E R O M S ); VELMS=LOG(VELMS); / PROC PRINT DATA PROC PRINT DATA PROC PRINT DATA PROC PRINT DATA PROC PRINT DATA PROC P R I M DATA = = = = = = SCT07; S6; T6: G6; G10; GT5;

4 an /- \f\. PROC P R I N T D A T A = E30; PROC P R I N T J A T A = MS; / DATA EROANG; M E R G E S C (R E N A M E ' ( E R O I C - E R O S C ANC1C=ANGSC107)) S6 (RFNAME=(EROFC=EROS6 ANG1C=ANGS6)) T 6 (R E N A M E = ( E R O T C = E R O T 6 A N G 1C = A N G T 6 ) ) C6 (RENAME= (E R O TC=EROG6 A N G T C = A N G G 6 )) CIO (RENAME=(EROlC-EK0G10 ANCTC=ANGC10)) C 1 5 (R E N A M E - ( E R O T C = E R O G 1 5 A N r TC = A N O G 1 5 ) ) f 30 ( R E N A M E = ( E R 0 I C = E R 0 E 3 0 ANCTC=ANCE30)) MS: T I T L E 1 E R O S I O N V S I M P A C T A N C L E ; T I T L E? 'PLOTANG'; S Y M B 0 L 1 V = S Q U A R E U R U L=1 C OL ; S Y M B 0 L 2 V = P L U S l= R Q i. - 2 C = B L ; S Y M B 0 L 3 V = T R I A N G L E l = R Q 1= 3 C = B L ; SYMBOL! V = X I= R Q L = U C = B L ; S Y M B O L S V = S T A R l=rq L= 5 C=BL; S Y M B O L S V - D I A M O N D l=rq L=6 C=BL; S Y M B 0 L 7 V = Y t= H Q L = 7 C=BI ; L A B E L E R O S C I O /= 1 E R O S 1O N M I C R O N S / T O N ' ; LABEL ANGSC107=' IMPACT ANGLE'; PROC GPLOT; PLOT E R O S C A N G S C 107=1 ER0S6ANGS6=2 ER0T6»ANGT6=3 EROG6ANGG6= 4 ER0G15ANGG15=5 EROE30ANGE30=6 ER0MS»ANCMS=7/0VERLAY; RU N ;

5 / / I \n </ i f v~v.. ;< DIST USERID OOIFRDP File name: PL32B3 File ty, ; SAS File moc Al This output was produced by the command: LASER PL32B3 SAS Al ( SET7 NODUP on 20/05/88 at 15:50:06 This file is on disk 001FRD and was last updated on 13/07/87 at 14:26:43 r \ -4

6 C M s ' H L E o I f ' NDATA^D^SK PL.32B3 D A T A A; D A T A A; INFILL INDATA; INPUT Z; D A T A B; Y= US DO X=-20 OUTPUT; END; T O 2 0 B Y 10; DO X = T O OUTPUT; END; 50 BY DO X =-60 TO outp u t ; END; 10; 6 0 BY 15 = -70 T O OUTPUT; END; 10; 60 BY D O x := T O OUTPUT; END; 10; 7 0 B Y 10; o x := - V O T O OUTPUT; END; m x 5 = T O OUTPUT; END; 70 B Y 10; 60 BY no " x 5 = -60 TO.jU T P U T ; EN D ; 10; 60 BY DO X = TO OUTPUT; END; DO X ^ -20 T O OUTPUT; EN D ; 10; 5 0 B Y 0; 2 0 B Y 10; K E E P X Y; D A T A C; M E R G E A B; / PROC C3CRID; GR10 X ^ Z / S P U N U NAXIS2=25; / PROC G3D; I'lTl!'- M v '10 M M G R I D, A C T U A L D A T A S C A T T E R X Y = Z / Z T I C K N U M = 10 POINTS, 8/7/87 ; - 1 «

7 i ' t /' ZMIN = H ZMAX = 6 S H A P E = 'POINT' TILT = 45 NOAXIS NONLEDLE R O T A TE =CO; RUN; - 2 -

8 VnVVrVrVrVr V V.VVrVr VrVr VrVrVr i'rvrvr VrVrVrVr VrVr Vr Vr Vr Vr Vr DIST USERID 001FRDP Vr Vr Vr.'r Vr Vr VrVr Vr Vr > Vr.'. -.r Vr Vr Vr Vr '. Vr Vr Vr Tile name: PNEUT6 File type: SAS File mode: AI This output was produced by the command: LASER PNEUT6 SAS AI ( SET7 NODUP COP 6 on 05/05/88 at 11:43:47 This file is on disk 001FRD and was last updated on 13/01/88 at 13:00:31

9 DATA P N U E ; SET D A T A. T C ; IF T Y P E T C - 1 T6 ; KEEP A D E R O T C A N C T C V E L T C / PIIASTC ; AI)ER O T C = L O G A D E R O T C ) i A N C T C - L O C ( A N C T C ); VELTC=LOG(VELTC); P H A S T C - L O C I PIIASTC); M f R T C = L O G ( M F R T C ); / P R O C r.orr RANK; PROC S T E P W I S E ; MODEL A D E ROTC=VELTC MODELRADEROTC -VELTC O U I P O T O U t= D A I A. P R E D P=EROPRED IJ95=UP95 195=L0W95; PIIASTC A N C T C / MAXR; PIIASTC A N C T C / P R C L I ;

10 irir ir ic ir ir vv VnVVrVrVnY. YVrttVnV ir ir ir ir ir iii t i t i r i t i t i t i t i t i t i t i t i irir ir ir ir ir ir ir ir ir ir i t ir ir ir ir ir i t i t ir ir ir ir ir ir ir w ttiririririr ir ir ir ir ir iiir ir ir i i iririririr i t.yfnyvnvvrrt ir ir. ' i r i r i. t iri.-iriririr w DIST USERID OOIFRDP i t ir ir ir i t ir ir iririririr i t i t ir ir ir ir ir ir irir ir ir ir ir i; irir ir ir ir iririr ir ir iriririr ir ir ir ir ir ir ir iriririririr irir ir ir i t i t i t ir ir ir ir ir i t irir ir ir ir ir ir ir VnV VnVVrV-'.-.V irir ir ir ir ir rtvnw.'iwrrt ftvny.'«> Vlr VrVrVn'n. Vr VciV File name: PNUET6 File type: LISTING File mode: A1 This output was produced by the command: LASER PNUET6 LISTING A1 ( SET7 NODUP COP 6 on 05/05/88 at 11:46:00 This file is on disk 001FRD and was last updated on 13/01/88 at 12:59:06

11 4 12:58 WE D N E S D A Y, SAS MEAN VARIABLE MFRTC VELTC ANCTC PIIASTC ADEROTC 20 2M D STO DEV SUM ( ) r> P E A R S O N C O R R E L A T I O N C O E F F I C I E N T S / PROrt > VELTC VELTC PHASIC / VELTC ADEROTC / MFRTC ADEROTC 0.589/ / PIIASTC ADEROTC VELTC MFRTC VELTC ADEROTC Ml R T C PHASIC ADEROTC C VELTC 0. j PIIASTC MFRTC l, / PIIASTC PIIASTC A D E R O 1C i 1 o. oooo ( Z RI U N D E R H O : R H O «0 / M = 2 0 MFRTC MFRTC T MINIMUM JANUARY 13, 1988 MAXIMUM ,

12 12:58 WEDNESDAY, SAS M A X I M U M P.-SQUARE STEP 1 VARIABLE DF 1 22 regression ERROR TOTAL 23 INTERCEPT PHASTC STEP INTERCEPT MFRTC PHASTC II SQUARES B VALUE STD ERROR M U U IS T H E B E S T PROB>F C(P) MEAN SQUARE F PROB>F O.OOCI F PROB>F TYPE II SS It , 2 V A R I A B L E M O O E L FOUND. R SQUARE ENTERED SUM OF SQUARES MEAN SQUARE F PROB>T DF j B VALUE STD ERROR O U B O U N D S ON C O N D I T I O N N U M B E R : T HE ABOVE M O D E L PROB>F I I SS ) VARIABLE MFRTC REGRESSION FRROR TOTAL R SQUARE SUM OF B O U N D S ON C O N D I T I O N NUMBER: 3 F 1 V A R I A B L E MODE I FOUND. 2 T H E ABOVE M ODEL , INTERCEPT PHASTC TYPE = 1 DF STEP 2 0 M. 7«79U99'» VARIABLE ANCTC ENTERED REGRESSION ERROR TOTAL C(P) MEAN SQUARE STD ERROR IS T H E B E S T FOR D E P E N D E N T V A R I A B L E A C E R O T C SUM OF S Q U A R E S 0 VALUE B O U N D S ON C O N D I T I O N NUMBER: IKE ABOVE MODEL R SQUARE PHASTC ENTERED IMPROVEMENT IS T H E B E S T VARIABLE MODEL J F O UND. I I SS F PROB>F TYPE JANUARY 13, 1988

13 / />v. r, fr :' :' 'V v ' e r s' '/. y ;. ' ; // > :.-s v, 12: 5 8 W E D N E S D A Y, SAS MAXIMUM R-SQUARE STEP 4 VARIABLE V E L T C REGRESSION ERROR TOTAL OF S U M OF S Q U A R E S 4 19? '! B VALUE INt E R C E P T MFRTC VEL1C PHASIC ANOTC ItU O / B O U N D S O N C O N D 1 1 IO N N U M B E R : THE A B O V E M O DEL R SQUARE EN1ERED IS T H E B E S T IMPROVEMENT ( ( V A R I A B L E M O D E L TOUND. C(P) STD ERROR , FOR D E P E N D E N T V A R I A B L E A D E R O T C mean squaiu; TYPE F PROB>F PROB>T I I SS ? JANUARY 13,

14 12:58 W E D N ESDAY, SAS l)ep V A R I A B L E : ADLROTC JANUARY 1 3, A N A L Y S I S OF V A R I A N C E DF 'I r.ource MODI I IRROR C rolal ROOT MSC 1)1 l> Mt A N C. V. S U M OF SQUARES MEAN SQUARE /1 F VALUE PR0B>F I 6 H R-SQUARE ADJ R-SQ P A R A M E T E R ESi I M A T E S VARIABLE INTERCEP Ml R T C VELTC PHASIC ACTUAL ObS i o IB PIUDIC1 VALUE J PARAMETER ESTIMATE STANDARD ERROR T F O R HO: PARAMETERS l ( OF S I D ERR PREDICT \ ' LOWER957. PREDICT UPPIR95X PREDICT RESIDUAL S 10 ERR RESIDUAL i 6058 I.58 /(I PROB > IT I STUDENT RESIDUAL D COOK'S «#«

15 12:58 W E U N E S D A Y, SAS ACTUAL 22.? S U M O F R E S ID U A L S SU M OF S Q U A R E D R E S I D U A L S P R E D I C T E D R E S I D SS (PRESS) PREDICT V A L IE S T D ERR PREDICT E I LOWER95S PREDICT UPPER95% PREDK ' RESIDUAL S T D E RR RESIDUAL STUDENT RESIDUAL JANUARY -2-1-U 1 2 I I 13, 1988 COOK'S

16 Author Freinkel D M (David M) Name of thesis Experimental Investigation Into The Wear Resistance Of Tungsten Carbide-cobalt Liners In A Full Scale Pneumatic Conveying Rig PUBLISHER: Universy of the Wwatersrand, Johannesburg 2013 LEGAL NOTICES: Copyright Notice: All materials on the U n i ve r si t y o f t h e Wi tw a te r s r an d, J o h a n n e sb u rg Lib r a r y webse are protected by South African copyright law and may not be distributed, transmted, displayed, or otherwise published in any format, whout the prior wrten permission of the copyright owner. Disclaimer and Terms of Use: Provided that you maintain all copyright and other notices contained therein, you may download material (one machine readable copy and one print copy per page) for your personal and/or educational non-commercial use only. The Universy of the Wwatersrand, Johannesburg, is not responsible for any errors or omissions and excludes any and all liabily for any errors in or omissions from the information on the Library webse.

SCATTER DIAGRAM ESTIMATED VS OBSERVED RUN OFF AT STATION A37 LUPANE RAINFALL STATION

SCATTER DIAGRAM ESTIMATED VS OBSERVED RUN OFF AT STATION A37 LUPANE RAINFALL STATION 35000 SCATTER DIAGRAM ESTIMATED VS OBSERVED RUN OFF AT STATION A37 LUPANE RAINFALL STATION 30000 25000 20000 - ui 15000 10000 5000 5000 10000 15000 20000 25000 OBSERVED RUNOFF x looocum 30000 35000 Figure

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

Failure Time of System due to the Hot Electron Effect

Failure Time of System due to the Hot Electron Effect of System due to the Hot Electron Effect 1 * exresist; 2 option ls=120 ps=75 nocenter nodate; 3 title of System due to the Hot Electron Effect ; 4 * TIME = failure time (hours) of a system due to drift

More information

* 7 *? f 7 T e & d. ** r : V ; - r.. ' V * - *. V. 1. fb & K try *n 0AC/A+J-* ' r * f o m e F o ^ T *?, * / / L * o : ± r ' *

* 7 *? f 7 T e & d. ** r : V ; - r.. ' V * - *. V. 1. fb & K try *n 0AC/A+J-* ' r * f o m e F o ^ T *?, * / / L * o : ± r ' * AM 19 4 i i I i J f j. * 7 *? f 7 T e & d -w?5*.? 'f & G s V J 'f / Z > / y ^ K f i r r t s K f S r 7 4 M C r v l * / ' f c : 0 * * < - / l r P r ~ r o s d «4 r ** r : V ; - r.. ' V * - *. V. 1 *f*rotrrrtc,

More information

Abode. When buried, lly wlmm the Ceremony was performed.

Abode. When buried, lly wlmm the Ceremony was performed. Abode. When buried, lly wlmm the Ceremony was performed. 13UKIAL'' in tli i vhom the Ceremony t was performed. lly whom the Ceremony m performed. s t f ' ' J '7lM ****u ttfa-atou* No. 'js? '(fliii. No.

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

Oil'll. 't Or) [xdl^i^, CtJiMr^ ~t x. tbu to#*a) rf. 3*^^1IlSr>' r e u <i^-^j O. , y r v u \ r t o < x * ^ v t a ^ c? ] % & y^lcji-*'**'* (» &>~r~

Oil'll. 't Or) [xdl^i^, CtJiMr^ ~t x. tbu to#*a) rf. 3*^^1IlSr>' r e u <i^-^j O. , y r v u \ r t o < x * ^ v t a ^ c? ] % & y^lcji-*'**'* (» &>~r~ Oil'll A l r x a t i i i r a B r a l l t f ( E m m t t t t t w. / P.O. Box 2, B e r g v l e i. D i s t r i c t J o h a n n e s b u r g. Ph o n e 4 5-2 4 6 9. R e f. N o. A I ( * J - i i ^ c J,,, JOHANNESBURG.

More information

Handout 1: Predicting GPA from SAT

Handout 1: Predicting GPA from SAT Handout 1: Predicting GPA from SAT appsrv01.srv.cquest.utoronto.ca> appsrv01.srv.cquest.utoronto.ca> ls Desktop grades.data grades.sas oldstuff sasuser.800 appsrv01.srv.cquest.utoronto.ca> cat grades.data

More information

Page 19. Abode. f a t ce ^i/c ^ rt l u. h c t. lia /tt*. fi.fccufftu. ct^ f f te m itu. / t /c a n t «//, StJ A :/. 7^ f t v f * r * r/m rr//,,

Page 19. Abode. f a t ce ^i/c ^ rt l u. h c t. lia /tt*. fi.fccufftu. ct^ f f te m itu. / t /c a n t «//, StJ A :/. 7^ f t v f * r * r/m rr//,, Page 19. B A P T ISM ^ sfjlem nized in the Parish of f t iil the Gmmty of * i" the Year JfL Baptized. Christian Name. Parents Name. Abode. or wa» performed. C tlth L f a t ce ^i/c ^ ip 'ltir a C f ^. C

More information

ft *6h^{tu>4^' u &ny ^ 'tujj Uyiy/Le^( ju~ to^a)~^/hh-tjsou/l- U r* Y o ^ s t^ ' / { L d (X / f\ J -Z L A ^ C (U 'o # j& tr & [)aj< l# 6 j< ^

ft *6h^{tu>4^' u &ny ^ 'tujj Uyiy/Le^( ju~ to^a)~^/hh-tjsou/l- U r* Y o ^ s t^ ' / { L d (X / f\ J -Z L A ^ C (U 'o # j& tr & [)aj< l# 6 j< ^ U r* c tir ( ^ y ^ J O ity r * U h i Y o ^ s t^ ' / { L d (X / f\ J -Z L A ^ C (U 'o # j& tr & [)aj< l# 6 j< ^ / T 3 & * ^!L y ^ 4 ~M j UA- A. d~ t => v t s -U mia O ^ V U e,jl - 4 y ^ i o u ~ e jl u O

More information

EXST Regression Techniques Page 1. We can also test the hypothesis H :" œ 0 versus H :"

EXST Regression Techniques Page 1. We can also test the hypothesis H : œ 0 versus H : EXST704 - Regression Techniques Page 1 Using F tests instead of t-tests We can also test the hypothesis H :" œ 0 versus H :" Á 0 with an F test.! " " " F œ MSRegression MSError This test is mathematically

More information

! J*UC4j u<s.< U l*4 3) U /r b A a ti ex Ou rta + s U fa* V. H lu< Y ^i«iy /( c * i U O rti^ ^ fx /i«w

! J*UC4j u<s.< U l*4 3) U /r b A a ti ex Ou rta + s U fa* V. H lu< Y ^i«iy /( c * i U O rti^ ^ fx /i«w p ) X 6 @ / [ j t [ l C ^ h u M q f» -» - * / ---- ---------------- ------ - Muuuka y Um tq/z/h*. Ik M tu ^ t j a s ^ #/ Jjfif*.! J*UC4j u

More information

ssh tap sas913, sas

ssh tap sas913, sas B. Kedem, STAT 430 SAS Examples SAS8 ===================== ssh xyz@glue.umd.edu, tap sas913, sas https://www.statlab.umd.edu/sasdoc/sashtml/onldoc.htm Multiple Regression ====================== 0. Show

More information

T-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum

T-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum T-test: means of Spock's judge versus all other judges 1 The TTEST Procedure Variable: pcwomen judge1 N Mean Std Dev Std Err Minimum Maximum OTHER 37 29.4919 7.4308 1.2216 16.5000 48.9000 SPOCKS 9 14.6222

More information

Volledige beskrywinj; van bcwysstukkc/ciendom Detailed description of exhibits/property [S.O. 328 (7) (b) en/and S.O.

Volledige beskrywinj; van bcwysstukkc/ciendom Detailed description of exhibits/property [S.O. 328 (7) (b) en/and S.O. j b lj- S A P I 1. Jaarlikse volgnommer 3. Verwysing O.B. Ref. No. V] U t f.c, M R C R Volledige beskrywinj; van bcwysstukkcciendom Detailed description of exhibitsproperty [S.O. 328 (7) (b) enand S.O.

More information

4 W. Girl W a y f a r e r s A s s o c i a t i o n, P. 0. B o x 97, D e a r Madam, R E : U S S OF L E A K S H A L L :

4 W. Girl W a y f a r e r s A s s o c i a t i o n, P. 0. B o x 97, D e a r Madam, R E : U S S OF L E A K S H A L L : fl C H IL D R E N S AID SOCIETY. KIN D ER HU LPVER EN IG IN G. (A F F I L I A T E D W IT H T H E N A T I O N A L C O U N C I L F O R C H I L D W E L F A R E.) (A A N G E S L U I T B Y D IE N A S IO N A

More information

PINE STREET. DURBAN. Issued by It Action Committe of African National Congress & Nattl Indian Congress

PINE STREET. DURBAN. Issued by It Action Committe of African National Congress & Nattl Indian Congress M A Y IB U Y E - A F R IK A! UFIKE EMHLANGANWENI OMKHULU! RED SQUARE PINE STREET. DURBAN. NGO SONTO 31st AUGUST NGO 2. 30 Ntabama Issued by It Action Committe of African National Congress & Nattl Indian

More information

Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D

Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D Lesson eight What are characteristics of chemical reactions? Science Constructing Explanations, Engaging in Argument and Obtaining, Evaluating, and Communicating Information ENGLISH LANGUAGE ARTS Reading

More information

&GGL S. Vol.» I>l OCESE: S t. H e l e n a PARISH: St. Paul REGI STER : feorla L. D A T E D :

&GGL S. Vol.» I>l OCESE: S t. H e l e n a PARISH: St. Paul REGI STER : feorla L. D A T E D : &GGL S. Vol.» I>l OCESE: S t. H e l e n a PARISH: St. Paul REGI STER : feorla L. D A T E D : ocuhfewt M UE.LLUK in ipp CP - i f *'' ' * ' *" J*L-...- r 'ij ~ > ~ ----- - " ------- «its t & s ->;,72 V^Vw**^fV

More information

ThP Small of Appl.es. and Miafling PSEaona could be seen to. constitute the spectrum of white male identity in South Africa-

ThP Small of Appl.es. and Miafling PSEaona could be seen to. constitute the spectrum of white male identity in South Africa- ThP Small of Appl.es. and Miafling PSEaona could be seen to constitute the spectrum of white male identity in South Africa- the first in terms of depicting the consistency of Apartheid ideology in perpetuating

More information

Overview Scatter Plot Example

Overview Scatter Plot Example Overview Topic 22 - Linear Regression and Correlation STAT 5 Professor Bruce Craig Consider one population but two variables For each sampling unit observe X and Y Assume linear relationship between variables

More information

Assume the bending moment acting on DE is twice that acting on AB, i.e Nmm, and is of opposite sign.

Assume the bending moment acting on DE is twice that acting on AB, i.e Nmm, and is of opposite sign. 232 End cf Do run Assume the bending moment acting on DE is twice that acting on AB, i.e. 138 240 Nmm, and is of opposite sign. n n Distance between BM couple = ^ x 432 = 288 mm o Magnitude of forces of

More information

in tlio Year Ono thousand Qtgbtb hundred tns&

in tlio Year Ono thousand Qtgbtb hundred tns& BURIALS in the Parish of in tho Gounty in tlio Year Ono thousand Qtgbtb hundred tns& BURI Abodo, Whon Buried, Dy whom Uio Ceremony w m Performed. f / fyhz y BURIALS h y tho^nriah of S i7a c ^ _ hundred

More information

Table 1: Fish Biomass data set on 26 streams

Table 1: Fish Biomass data set on 26 streams Math 221: Multiple Regression S. K. Hyde Chapter 27 (Moore, 5th Ed.) The following data set contains observations on the fish biomass of 26 streams. The potential regressors from which we wish to explain

More information

v f e - w e ^ C c ^ ^ o e s r v i c e ^ ^.

v f e - w e ^ C c ^ ^ o e s r v i c e ^ ^. * * >>'.* v. K *. V-Vv, : *. v - ' *.. v ;,-:-v -; v r ^ y c - : -, - r r : r * < : ~ : >, -. ^ * "--* * - * -ui.t-l.2,u?.:^_ a..., : w.--j.i: :-.; rr - a i r /-».. i v ' ^ 4. * '... ' J \ - ;/>»-

More information

Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D

Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D Lesson seven What is a chemical reaction? Science Constructing Explanations, Engaging in Argument and Obtaining, Evaluating, and Communicating Information ENGLISH LANGUAGE ARTS Reading Informational Text,

More information

1 A Review of Correlation and Regression

1 A Review of Correlation and Regression 1 A Review of Correlation and Regression SW, Chapter 12 Suppose we select n = 10 persons from the population of college seniors who plan to take the MCAT exam. Each takes the test, is coached, and then

More information

EXST7015: Estimating tree weights from other morphometric variables Raw data print

EXST7015: Estimating tree weights from other morphometric variables Raw data print Simple Linear Regression SAS example Page 1 1 ********************************************; 2 *** Data from Freund & Wilson (1993) ***; 3 *** TABLE 8.24 : ESTIMATING TREE WEIGHTS ***; 4 ********************************************;

More information

BE640 Intermediate Biostatistics 2. Regression and Correlation. Simple Linear Regression Software: SAS. Emergency Calls to the New York Auto Club

BE640 Intermediate Biostatistics 2. Regression and Correlation. Simple Linear Regression Software: SAS. Emergency Calls to the New York Auto Club BE640 Intermediate Biostatistics 2. Regression and Correlation Simple Linear Regression Software: SAS Emergency Calls to the New York Auto Club Source: Chatterjee, S; Handcock MS and Simonoff JS A Casebook

More information

B A P T IS M S solemnized. Hn the County of

B A P T IS M S solemnized. Hn the County of B A P T IS M S solemnized. Hn the County of Biptixcw Child * Parent* Name. By wlmiu the wa* performed. Page 7. B A P T IS M S solemnized in the Parish of in the County of CTV-^. ~ -" >. Child's Christian

More information

STATISTICS 110/201 PRACTICE FINAL EXAM

STATISTICS 110/201 PRACTICE FINAL EXAM STATISTICS 110/201 PRACTICE FINAL EXAM Questions 1 to 5: There is a downloadable Stata package that produces sequential sums of squares for regression. In other words, the SS is built up as each variable

More information

Lecture 1 Linear Regression with One Predictor Variable.p2

Lecture 1 Linear Regression with One Predictor Variable.p2 Lecture Linear Regression with One Predictor Variablep - Basics - Meaning of regression parameters p - β - the slope of the regression line -it indicates the change in mean of the probability distn of

More information

3IZL15E OF LEBANESE LIGTIT'T'fij.

3IZL15E OF LEBANESE LIGTIT'T'fij. 201. XI. 3IZL15E OF LEBANESE LIGTIT'T'fij. ihe foiegojng investigations clearly indicate that any p r o l o n g future of a Lebanese Lignite Industry cannot be seriously contemplated. Development of the

More information

( O-P.-S. U o \. NAVRAE REGISTER %»«i» A. v 1 c. rc a 2. V., h KO... .,'1:0 VAN El. (a) Stasicreeksnommcr (a) Station serial number

( O-P.-S. U o \. NAVRAE REGISTER %»«i» A. v 1 c. rc a 2. V., h KO... .,'1:0 VAN El. (a) Stasicreeksnommcr (a) Station serial number ( O-P.-S. U o \. V., h KO... (a) Stasicreeksnommcr (a) Station serial number *ur SU!C (b) Datum van ontvangs (b) Date of receipt r-\ -r JJ2A a. a...7... s -... I V f e?. *:or dat J :^r : ddkument n c ^

More information

Chapter 8 (More on Assumptions for the Simple Linear Regression)

Chapter 8 (More on Assumptions for the Simple Linear Regression) EXST3201 Chapter 8b Geaghan Fall 2005: Page 1 Chapter 8 (More on Assumptions for the Simple Linear Regression) Your textbook considers the following assumptions: Linearity This is not something I usually

More information

Topic 20: Single Factor Analysis of Variance

Topic 20: Single Factor Analysis of Variance Topic 20: Single Factor Analysis of Variance Outline Single factor Analysis of Variance One set of treatments Cell means model Factor effects model Link to linear regression using indicator explanatory

More information

Stat 302 Statistical Software and Its Applications SAS: Simple Linear Regression

Stat 302 Statistical Software and Its Applications SAS: Simple Linear Regression 1 Stat 302 Statistical Software and Its Applications SAS: Simple Linear Regression Fritz Scholz Department of Statistics, University of Washington Winter Quarter 2015 February 16, 2015 2 The Spirit of

More information

Topic 28: Unequal Replication in Two-Way ANOVA

Topic 28: Unequal Replication in Two-Way ANOVA Topic 28: Unequal Replication in Two-Way ANOVA Outline Two-way ANOVA with unequal numbers of observations in the cells Data and model Regression approach Parameter estimates Previous analyses with constant

More information

EE290H F05. Spanos. Lecture 5: Comparison of Treatments and ANOVA

EE290H F05. Spanos. Lecture 5: Comparison of Treatments and ANOVA 1 Design of Experiments in Semiconductor Manufacturing Comparison of Treatments which recipe works the best? Simple Factorial Experiments to explore impact of few variables Fractional Factorial Experiments

More information

3 Variables: Cyberloafing Conscientiousness Age

3 Variables: Cyberloafing Conscientiousness Age title 'Cyberloafing, Mike Sage'; run; PROC CORR data=sage; var Cyberloafing Conscientiousness Age; run; quit; The CORR Procedure 3 Variables: Cyberloafing Conscientiousness Age Simple Statistics Variable

More information

STEEL PIPE NIPPLE BLACK AND GALVANIZED

STEEL PIPE NIPPLE BLACK AND GALVANIZED Price Sheet Effective August 09, 2018 Supersedes CWN-218 A Member of The Phoenix Forge Group CapProducts LTD. Phone: 519-482-5000 Fax: 519-482-7728 Toll Free: 800-265-5586 www.capproducts.com www.capitolcamco.com

More information

Lesson Ten. What role does energy play in chemical reactions? Grade 8. Science. 90 minutes ENGLISH LANGUAGE ARTS

Lesson Ten. What role does energy play in chemical reactions? Grade 8. Science. 90 minutes ENGLISH LANGUAGE ARTS Lesson Ten What role does energy play in chemical reactions? Science Asking Questions, Developing Models, Investigating, Analyzing Data and Obtaining, Evaluating, and Communicating Information ENGLISH

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

4 Instrumental Variables Single endogenous variable One continuous instrument. 2

4 Instrumental Variables Single endogenous variable One continuous instrument. 2 Econ 495 - Econometric Review 1 Contents 4 Instrumental Variables 2 4.1 Single endogenous variable One continuous instrument. 2 4.2 Single endogenous variable more than one continuous instrument..........................

More information

In Class Review Exercises Vartanian: SW 540

In Class Review Exercises Vartanian: SW 540 In Class Review Exercises Vartanian: SW 540 1. Given the following output from an OLS model looking at income, what is the slope and intercept for those who are black and those who are not black? b SE

More information

Chapter 8 Quantitative and Qualitative Predictors

Chapter 8 Quantitative and Qualitative Predictors STAT 525 FALL 2017 Chapter 8 Quantitative and Qualitative Predictors Professor Dabao Zhang Polynomial Regression Multiple regression using X 2 i, X3 i, etc as additional predictors Generates quadratic,

More information

PubH 7405: REGRESSION ANALYSIS SLR: DIAGNOSTICS & REMEDIES

PubH 7405: REGRESSION ANALYSIS SLR: DIAGNOSTICS & REMEDIES PubH 7405: REGRESSION ANALYSIS SLR: DIAGNOSTICS & REMEDIES Normal Error RegressionModel : Y = β 0 + β ε N(0,σ 2 1 x ) + ε The Model has several parts: Normal Distribution, Linear Mean, Constant Variance,

More information

Gregory Carey, 1998 Regression & Path Analysis - 1 MULTIPLE REGRESSION AND PATH ANALYSIS

Gregory Carey, 1998 Regression & Path Analysis - 1 MULTIPLE REGRESSION AND PATH ANALYSIS Gregory Carey, 1998 Regression & Path Analysis - 1 MULTIPLE REGRESSION AND PATH ANALYSIS Introduction Path analysis and multiple regression go hand in hand (almost). Also, it is easier to learn about multivariate

More information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

Statistics 5100 Spring 2018 Exam 1

Statistics 5100 Spring 2018 Exam 1 Statistics 5100 Spring 2018 Exam 1 Directions: You have 60 minutes to complete the exam. Be sure to answer every question, and do not spend too much time on any part of any question. Be concise with all

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

Topic 18: Model Selection and Diagnostics

Topic 18: Model Selection and Diagnostics Topic 18: Model Selection and Diagnostics Variable Selection We want to choose a best model that is a subset of the available explanatory variables Two separate problems 1. How many explanatory variables

More information

General Linear Model (Chapter 4)

General Linear Model (Chapter 4) General Linear Model (Chapter 4) Outcome variable is considered continuous Simple linear regression Scatterplots OLS is BLUE under basic assumptions MSE estimates residual variance testing regression coefficients

More information

a. The least squares estimators of intercept and slope are (from JMP output): b 0 = 6.25 b 1 =

a. The least squares estimators of intercept and slope are (from JMP output): b 0 = 6.25 b 1 = Stat 28 Fall 2004 Key to Homework Exercise.10 a. There is evidence of a linear trend: winning times appear to decrease with year. A straight-line model for predicting winning times based on year is: Winning

More information

INSTRUCTIONS: CHEM Exam I. September 13, 1994 Lab Section

INSTRUCTIONS: CHEM Exam I. September 13, 1994 Lab Section CHEM 1314.05 Exam I John I. Gelder September 13, 1994 Name TA's Name Lab Section Please sign your name below to give permission to post, by the last 4 digits of your student I.D. number, your course scores

More information

unadjusted model for baseline cholesterol 22:31 Monday, April 19,

unadjusted model for baseline cholesterol 22:31 Monday, April 19, unadjusted model for baseline cholesterol 22:31 Monday, April 19, 2004 1 Class Level Information Class Levels Values TRETGRP 3 3 4 5 SEX 2 0 1 Number of observations 916 unadjusted model for baseline cholesterol

More information

STOR 455 STATISTICAL METHODS I

STOR 455 STATISTICAL METHODS I STOR 455 STATISTICAL METHODS I Jan Hannig Mul9variate Regression Y=X β + ε X is a regression matrix, β is a vector of parameters and ε are independent N(0,σ) Es9mated parameters b=(x X) - 1 X Y Predicted

More information

The General Linear Model. April 22, 2008

The General Linear Model. April 22, 2008 The General Linear Model. April 22, 2008 Multiple regression Data: The Faroese Mercury Study Simple linear regression Confounding The multiple linear regression model Interpretation of parameters Model

More information

The General Linear Model. November 20, 2007

The General Linear Model. November 20, 2007 The General Linear Model. November 20, 2007 Multiple regression Data: The Faroese Mercury Study Simple linear regression Confounding The multiple linear regression model Interpretation of parameters Model

More information

Topic 17 - Single Factor Analysis of Variance. Outline. One-way ANOVA. The Data / Notation. One way ANOVA Cell means model Factor effects model

Topic 17 - Single Factor Analysis of Variance. Outline. One-way ANOVA. The Data / Notation. One way ANOVA Cell means model Factor effects model Topic 17 - Single Factor Analysis of Variance - Fall 2013 One way ANOVA Cell means model Factor effects model Outline Topic 17 2 One-way ANOVA Response variable Y is continuous Explanatory variable is

More information

ECON3150/4150 Spring 2016

ECON3150/4150 Spring 2016 ECON3150/4150 Spring 2016 Lecture 4 - The linear regression model Siv-Elisabeth Skjelbred University of Oslo Last updated: January 26, 2016 1 / 49 Overview These lecture slides covers: The linear regression

More information

Beechwood Music Department Staff

Beechwood Music Department Staff Beechwood Music Department Staff MRS SARAH KERSHAW - HEAD OF MUSIC S a ra h K e rs h a w t r a i n e d a t t h e R oy a l We ls h C o l le g e of M u s i c a n d D ra m a w h e re s h e ob t a i n e d

More information

SPECIAL TOPICS IN REGRESSION ANALYSIS

SPECIAL TOPICS IN REGRESSION ANALYSIS 1 SPECIAL TOPICS IN REGRESSION ANALYSIS Representing Nominal Scales in Regression Analysis There are several ways in which a set of G qualitative distinctions on some variable of interest can be represented

More information

Course Econometrics I

Course Econometrics I Course Econometrics I 3. Multiple Regression Analysis: Binary Variables Martin Halla Johannes Kepler University of Linz Department of Economics Last update: April 29, 2014 Martin Halla CS Econometrics

More information

Practice 2SLS with Artificial Data Part 1

Practice 2SLS with Artificial Data Part 1 Practice 2SLS with Artificial Data Part 1 Yona Rubinstein July 2016 Yona Rubinstein (LSE) Practice 2SLS with Artificial Data Part 1 07/16 1 / 16 Practice with Artificial Data In this note we use artificial

More information

Outline. Review regression diagnostics Remedial measures Weighted regression Ridge regression Robust regression Bootstrapping

Outline. Review regression diagnostics Remedial measures Weighted regression Ridge regression Robust regression Bootstrapping Topic 19: Remedies Outline Review regression diagnostics Remedial measures Weighted regression Ridge regression Robust regression Bootstrapping Regression Diagnostics Summary Check normality of the residuals

More information

o V fc rt* rh '.L i.p -Z :. -* & , -. \, _ * / r s* / / ' J / X - -

o V fc rt* rh '.L i.p -Z :. -* & , -. \, _ * / r s* / / ' J / X - - -. ' ' " / ' * * ' w, ~ n I: ».r< A < ' : l? S p f - f ( r ^ < - i r r. : '. M.s H m **.' * U -i\ i 3 -. y$. S 3. -r^ o V fc rt* rh '.L i.p -Z :. -* & --------- c it a- ; '.(Jy 1/ } / ^ I f! _ * ----*>C\

More information

Comparison of a Population Means

Comparison of a Population Means Analysis of Variance Interested in comparing Several treatments Several levels of one treatment Comparison of a Population Means Could do numerous two-sample t-tests but... ANOVA provides method of joint

More information

ECON Introductory Econometrics. Lecture 5: OLS with One Regressor: Hypothesis Tests

ECON Introductory Econometrics. Lecture 5: OLS with One Regressor: Hypothesis Tests ECON4150 - Introductory Econometrics Lecture 5: OLS with One Regressor: Hypothesis Tests Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 5 Lecture outline 2 Testing Hypotheses about one

More information

Outline. Topic 22 - Interaction in Two Factor ANOVA. Interaction Not Significant. General Plan

Outline. Topic 22 - Interaction in Two Factor ANOVA. Interaction Not Significant. General Plan Topic 22 - Interaction in Two Factor ANOVA - Fall 2013 Outline Strategies for Analysis when interaction not present when interaction present when n ij = 1 when factor(s) quantitative Topic 22 2 General

More information

CHEM Come to the PASS workshop with your mock exam complete. During the workshop you can work with other students to review your work.

CHEM Come to the PASS workshop with your mock exam complete. During the workshop you can work with other students to review your work. It is most beneficial to you to write this mock midterm UNDER EXAM CONDITIONS. This means: Complete the midterm in 1.5 hours. Work on your own. Keep your notes and textbook closed. Attempt every question.

More information

Marks for each question are as indicated in [] brackets.

Marks for each question are as indicated in [] brackets. Name Student Number CHEMISTRY 140 FINAL EXAM December 10, 2002 Numerical answers must be given with appropriate units and significant figures. Please place all answers in the space provided for the question.

More information

sociology 362 regression

sociology 362 regression sociology 36 regression Regression is a means of studying how the conditional distribution of a response variable (say, Y) varies for different values of one or more independent explanatory variables (say,

More information

CLASS TEST GRADE 11. PHYSICAL SCIENCES: CHEMISTRY Test 4: Matter and materials 1

CLASS TEST GRADE 11. PHYSICAL SCIENCES: CHEMISTRY Test 4: Matter and materials 1 CLASS TEST GRADE PHYSICAL SCIENCES: CHEMISTRY Test 4: Matter and materials MARKS: 45 TIME: hour INSTRUCTIONS AND INFORMATION. Answer ALL the questions. 2. You may use non-programmable calculators. 3. You

More information

Element Cube Project (x2)

Element Cube Project (x2) Element Cube Project (x2) Background: As a class, we will construct a three dimensional periodic table by each student selecting two elements in which you will need to create an element cube. Helpful Links

More information

Section Least Squares Regression

Section Least Squares Regression Section 2.3 - Least Squares Regression Statistics 104 Autumn 2004 Copyright c 2004 by Mark E. Irwin Regression Correlation gives us a strength of a linear relationship is, but it doesn t tell us what it

More information

^l,2-, 3. f 2_ ' >v., 4. b/-e. of- fee. -^3-5- I70(fa = )&*>$)

^l,2-, 3. f 2_ ' >v., 4. b/-e. of- fee. -^3-5- I70(fa = )&*>$) & ^l,2-, 3 f 2_ ' s\ >v., 4 b/-e of- fee O = )&*>$) -^3-5- I70(fa \J QQQ ~E - V-?' = f rff ^ if -j- 41 -Ofc -Ofc v OH OH ST/VT H SoJU^, 55-3 2. ^ T_ 2, _ ^ * ^3W^(M7 2. X 2.! Z. d-f- S-5- /V Z 33?> 5YJ

More information

Multicollinearity Exercise

Multicollinearity Exercise Multicollinearity Exercise Use the attached SAS output to answer the questions. [OPTIONAL: Copy the SAS program below into the SAS editor window and run it.] You do not need to submit any output, so there

More information

SAS Commands. General Plan. Output. Construct scatterplot / interaction plot. Run full model

SAS Commands. General Plan. Output. Construct scatterplot / interaction plot. Run full model Topic 23 - Unequal Replication Data Model Outline - Fall 2013 Parameter Estimates Inference Topic 23 2 Example Page 954 Data for Two Factor ANOVA Y is the response variable Factor A has levels i = 1, 2,...,

More information

http://orcid.org/0000-0001-8820-8188 N r = 0.88 P x λ P x λ d m = d 1/ m, d m i 1...m ( d i ) 2 = c c i m d β = 1 β = 1.5 β = 2 β = 3 d b = d a d b = 1.0

More information

Problem Set #5-Key Sonoma State University Dr. Cuellar Economics 317- Introduction to Econometrics

Problem Set #5-Key Sonoma State University Dr. Cuellar Economics 317- Introduction to Econometrics Problem Set #5-Key Sonoma State University Dr. Cuellar Economics 317- Introduction to Econometrics C1.1 Use the data set Wage1.dta to answer the following questions. Estimate regression equation wage =

More information

Chapter 2 Inferences in Simple Linear Regression

Chapter 2 Inferences in Simple Linear Regression STAT 525 SPRING 2018 Chapter 2 Inferences in Simple Linear Regression Professor Min Zhang Testing for Linear Relationship Term β 1 X i defines linear relationship Will then test H 0 : β 1 = 0 Test requires

More information

Section I. Define or explain the following terms (3 points each) 1. centered vs. uncentered 2 R - 2. Frisch theorem -

Section I. Define or explain the following terms (3 points each) 1. centered vs. uncentered 2 R - 2. Frisch theorem - First Exam: Economics 388, Econometrics Spring 006 in R. Butler s class YOUR NAME: Section I (30 points) Questions 1-10 (3 points each) Section II (40 points) Questions 11-15 (10 points each) Section III

More information

Chapter 1 Linear Regression with One Predictor

Chapter 1 Linear Regression with One Predictor STAT 525 FALL 2018 Chapter 1 Linear Regression with One Predictor Professor Min Zhang Goals of Regression Analysis Serve three purposes Describes an association between X and Y In some applications, the

More information

Booklet of Code and Output for STAC32 Final Exam

Booklet of Code and Output for STAC32 Final Exam Booklet of Code and Output for STAC32 Final Exam December 7, 2017 Figure captions are below the Figures they refer to. LowCalorie LowFat LowCarbo Control 8 2 3 2 9 4 5 2 6 3 4-1 7 5 2 0 3 1 3 3 Figure

More information

Econometrics Homework 1

Econometrics Homework 1 Econometrics Homework Due Date: March, 24. by This problem set includes questions for Lecture -4 covered before midterm exam. Question Let z be a random column vector of size 3 : z = @ (a) Write out z

More information

Software Process Models there are many process model s in th e li t e ra t u re, s om e a r e prescriptions and some are descriptions you need to mode

Software Process Models there are many process model s in th e li t e ra t u re, s om e a r e prescriptions and some are descriptions you need to mode Unit 2 : Software Process O b j ec t i ve This unit introduces software systems engineering through a discussion of software processes and their principal characteristics. In order to achieve the desireable

More information

C o r p o r a t e l i f e i n A n c i e n t I n d i a e x p r e s s e d i t s e l f

C o r p o r a t e l i f e i n A n c i e n t I n d i a e x p r e s s e d i t s e l f C H A P T E R I G E N E S I S A N D GROWTH OF G U IL D S C o r p o r a t e l i f e i n A n c i e n t I n d i a e x p r e s s e d i t s e l f i n a v a r i e t y o f f o r m s - s o c i a l, r e l i g i

More information

Ci ho/'i i - ' o t» M3 fvcjfa 7 i V v t i Srtt-nfrH rt>?ny frs)2

Ci ho/'i i - ' o t» M3 fvcjfa 7 i V v t i Srtt-nfrH rt>?ny frs)2 f ) P 2 i f % * f o e * * 3. j W ^ - 3. ^ H r 0 * " T ^ T c z r r - c r f S _ M f i / y s j v ^ r w r j T r* r> ~ f f 2)V/»/t7 f>?c f J f 7 ' i * f **Sfin7 TO Ik in jfift S ij)6" e f A bvfiritnfi:- ijd

More information

c. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f

c. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f Essential Skills Chapter f ( x + h) f ( x ). Simplifying the difference quotient Section. h f ( x + h) f ( x ) Example: For f ( x) = 4x 4 x, find and simplify completely. h Answer: 4 8x 4 h. Finding the

More information

STAT 3A03 Applied Regression Analysis With SAS Fall 2017

STAT 3A03 Applied Regression Analysis With SAS Fall 2017 STAT 3A03 Applied Regression Analysis With SAS Fall 2017 Assignment 5 Solution Set Q. 1 a The code that I used and the output is as follows PROC GLM DataS3A3.Wool plotsnone; Class Amp Len Load; Model CyclesAmp

More information

S U E K E AY S S H A R O N T IM B E R W IN D M A R T Z -PA U L L IN. Carlisle Franklin Springboro. Clearcreek TWP. Middletown. Turtlecreek TWP.

S U E K E AY S S H A R O N T IM B E R W IN D M A R T Z -PA U L L IN. Carlisle Franklin Springboro. Clearcreek TWP. Middletown. Turtlecreek TWP. F R A N K L IN M A D IS O N S U E R O B E R T LE IC H T Y A LY C E C H A M B E R L A IN T W IN C R E E K M A R T Z -PA U L L IN C O R A O W E N M E A D O W L A R K W R E N N LA N T IS R E D R O B IN F

More information

Topic 29: Three-Way ANOVA

Topic 29: Three-Way ANOVA Topic 29: Three-Way ANOVA Outline Three-way ANOVA Data Model Inference Data for three-way ANOVA Y, the response variable Factor A with levels i = 1 to a Factor B with levels j = 1 to b Factor C with levels

More information

Pooled Regression and Dummy Variables in CO$TAT

Pooled Regression and Dummy Variables in CO$TAT Pooled Regression and Dummy Variables in CO$TAT Jeff McDowell September 19, 2012 PRT 141 Outline Define Dummy Variable CER Example Using Linear Regression The Pattern CER Example Using Log-Linear Regression

More information

Two-factor studies. STAT 525 Chapter 19 and 20. Professor Olga Vitek

Two-factor studies. STAT 525 Chapter 19 and 20. Professor Olga Vitek Two-factor studies STAT 525 Chapter 19 and 20 Professor Olga Vitek December 2, 2010 19 Overview Now have two factors (A and B) Suppose each factor has two levels Could analyze as one factor with 4 levels

More information

Topic 25 - One-Way Random Effects Models. Outline. Random Effects vs Fixed Effects. Data for One-way Random Effects Model. One-way Random effects

Topic 25 - One-Way Random Effects Models. Outline. Random Effects vs Fixed Effects. Data for One-way Random Effects Model. One-way Random effects Topic 5 - One-Way Random Effects Models One-way Random effects Outline Model Variance component estimation - Fall 013 Confidence intervals Topic 5 Random Effects vs Fixed Effects Consider factor with numerous

More information

IES 612/STA 4-573/STA Winter 2008 Week 1--IES 612-STA STA doc

IES 612/STA 4-573/STA Winter 2008 Week 1--IES 612-STA STA doc IES 612/STA 4-573/STA 4-576 Winter 2008 Week 1--IES 612-STA 4-573-STA 4-576.doc Review Notes: [OL] = Ott & Longnecker Statistical Methods and Data Analysis, 5 th edition. [Handouts based on notes prepared

More information

Outline Topic 21 - Two Factor ANOVA

Outline Topic 21 - Two Factor ANOVA Outline Topic 21 - Two Factor ANOVA Data Model Parameter Estimates - Fall 2013 Equal Sample Size One replicate per cell Unequal Sample size Topic 21 2 Overview Now have two factors (A and B) Suppose each

More information

PROOF/ÉPREUVE ISO INTERNATIONAL STANDARD. Space environment (natural and artificial) Galactic cosmic ray model

PROOF/ÉPREUVE ISO INTERNATIONAL STANDARD. Space environment (natural and artificial) Galactic cosmic ray model INTERNATIONAL STANDARD ISO 15390 First edition 2004-##-## Space environment (natural and artificial) Galactic cosmic ray model Environnement spatial (naturel et artificiel) Modèle de rayonnement cosmique

More information