anova(Md,Mdhhv) anova(Mdhv,Mdhhv) anova(Mdh,Mdhhv) par(mfcol=c(1,1), las=1) plot(Md) # 2: Näslundi funkts. PD.KU<-subset(PD.1,pl=="KU") par(mar=c(4.7,4.7,1,1)) plot(h~d_k, PD.KU, xlim=c(0,35),ylim=c(0,20),xlab="Diameeter, cm", ylab="Kõrgus, m") abline(v=seq(0,40,10),lty=3,col="grey75") abline(h=seq(0,25,5),lty=3,col="grey75") # abijooned #lineariseerides Mnsld.1 <- lm(I(d_k/(h-1.3)^(1/3))~d_k, PD.KU) summary(Mnsld.1) PD.KU$h.nsld<- 1.3+(PD.KU$d_k/ (Mnsld.1$coefficients[1]+Mnsld.1$coefficients[2]*PD.KU$d_k))^3 with(PD.KU, sqrt(sum((h-h.nsld)^2)/(length(h)-2))) curve(1.3+(x/(Mnsld.1$coefficients[1]+Mnsld.1$coefficients[2]*x))^3, from=0, to=35, col="red",add=T,lwd=2,lty=2) rect(19,1,30,6,border=2,lty=2,col=0) ac1 <- round(Mnsld.1$coefficients,3) valem1 <- substitute(h==1.3+(frac(d, b0+b1*d))^3, list(b0=ac1[1],b1=ac1[2])) text(20,4,valem1,pos=4) s1 = round(with(PD.KU, sqrt(sum((h-h.nsld)^2)/(length(h)-2))),2)
table(PD.H$pl) PD.KU<-subset(PD.H, pl=="KU") par(mar=c(4.5,4.5,1,1)) plot(NULL,xlim=c(0,40),ylim=c(0,25),xlab="diameeter, cm", ylab="kõrgus, m") abline(v=seq(0,40,10),lty=3,col="grey75") abline(h=seq(0,25,5),lty=3,col="grey75") # abijooned points(h~d_k,data=subset(PD.KU),lwd=1) with(subset(PD., pl=="KU"),rug(d_k)) 1. Sirge h=a+b*d M1 <- lm(h~d_k, data=PD.KU) summary(M1) D<-0:40 M1.pred <- predict(M1,newdata=data.frame(d_k=D)) lines(D,M1.pred, col="red") coefficients(M1)[1] coefficients(M1)[2] # dobavit' p-value v tablicu v vide * summary(M1)$adj.r.squared summary(M1)$sigma # sqrt(sum(M1$residuals^2)/(length(M1$residuals)-2)) AIC(M1) > coefficients(M1)[1] (Intercept) 7.758512 > coefficients(M1)[2] # dobavit' p-value v tablicu v vide * d_k 0.5027412 > summary(M1)$adj.r.squared [1] 0.7619479 > summary(M1)$sigma # sqrt(sum(M1$residuals^2)/(length(M1$residuals)-2)) [1] 1.823029 > AIC(M1) [1] 177.6237 2. Ruutparabool h=a+b*d+c*d^2
SUMMARY OUTPUT Regression Statistics Multiple R 0,997117 R Square 0,994242 Adjusted R Square 0,993923 Standard Error 0,050087 Observations 20 ANOVA df SS MS F Significance F Regression 1 7,797748 7,797748 3108,303 1,3E021 Residual 18 0,045156 0,002509 Total 19 7,842905 Coefficients Standard Error t Stat Pvalue Lower 95%Upper 95% Lower 95,0% Upper 95,0% Intercept 0,02403 0,023267 1,03285 0,315351 0,07291 0,024851 0,07291 0,024851 X Variable 10,02166 0,000388 55,7522 1,3E021 0,02247 0,02084 0,02247 0,02084 SUMMARY OUTPUT Regression Statistics Multiple R 0,997434 R Square 0,994874 Adjusted R Square
R Square 0,8090315055 Adjusted R Square 0,7963002725 Standard Error 5,684885887 Observations 145 ANOVA df SS MS F Regression 9 18483,362517209 2053,706946357 63,5469877603 Residual 135 4362,9202190334 32,3179275484 Total 144 22846,282736242 Coefficients Standard Error t Stat P-value Intercept 45,2325050445 4,5122271955 10,0244298625 5,0841941E-018 X2 54,0929506547 23,6488063569 2,2873438024 0,023730954 X3 -5,1644389493 1,0898051827 -4,7388643688 5,3785349E-006 X4 -4,5777656267 0,5803114144 -7,8884638711 9,2683831E-013
Andmete sisestamiseks märkida ära: Analyze Existing Data In the Windows Clipboard Variable Names: from first row . Algandmete tabel ilmub ekraanile. 6. Menüüst SPECIAL - Advanced regression - Ridge Regression ja anda ette andmed: Dependent Variable: y Independent Variable: x1, x2, x3 ja teostada arvutused (vastata OK). 7. Ridge Regression'i nupurea nupuga 'Tabular Options' käivitada menüü ja valida Regression Coefficients: tekivad tabel ja graafik. Korrigeerida tabel ja graafik (pealkirjad, telgede nimetused, selgitavad tekstid - Graphics Options). 8. Graafiku korrigeerimiseks valida hiire parempoolse nupuga käsk 'Pane Options' ja valida Unstandardized Coefficients. 9. Võimalik ka korrigeerida graafikut (Graphics Options) 10. Tabel ja graafik paigutada eraldi lehtedele. Venitada nad suuremaks. 11
Standard Error 811,7647244022 Observations 37 ANOVA df SS MS F Regression 4 5452896,128908 1363224 2,068745 Residual 32 21086782,96908 658962 Total 36 26539679,09799 Coefficients Standard Error t Stat P-value Intercept 316,8447162072 515,1894957149 0,615006 0,5429 X1 0,3616205503 0,2486257171 1,454478 0,155553 X2 325,0756415193 419,6871545439 0,774567 0,444282 X3 -1,1237269925 1,0313144227 -1,089607 0,284024
864918694 Adjusted R Square 0.856476112 Standard Error 197.413872 Observations 18 ANOVA df SS MS Regression 1 3992595.82144818 3992595.821 Residual 16 623555.789662928 38972.23685 Total 17 4616151.61111111 Coefficients Standard Error t Stat Intercept 3076.114111 99.2070711068 31.00700461 Väärtus jooksevhindades, miljoni-0.097903113 0.0096726727 -10.12161951 F Significance F 102.447181 2.3209959E-008 P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0% 1.0199E-015 2865.80451528 3286.423707 2865.804515 3286.4237068 2.3210E-008 -0.1184082627 -0.07739796 -0.118408263 -0.077397963 punktide arv
8890910566 Standard Error 1989.3430527166 Observations 15 ANOVA df SS MS F Significance F Regression 1 1347945730.06 1347945730.06 340.60658825 1.044501E-010 Residual 14 55404800.9395 3957485.78139 Total 15 1403350531 Coefficients Standard Error t Stat P-value Lower 95% Intercept 0 Err:512 Err:512 Err:512 Err:512 Töötajate arv (tuh) 235 13 18 0 208 SUMMARY OUTPUT Regression Statistics Multiple R 0.9848934499 R Square 0.9700151077 Adjusted R Square 0.8907855006 Standard Error 1799
𝑖=1 𝑢 is called the algebraic normal form of function 𝑓. The monomial ∏𝑛𝑖=1 𝑥𝑖 𝑖 is often denoted by 𝑥 𝑢 . The µ𝑓 is also a Boolean function of 𝑛 variables, which is called the Möbius transformation of 𝑓. The coefficients of ANF satisfy the following equation: µ𝑓 = ∑ 𝑓(𝑥) 𝑥≤𝑢 𝑢∈𝔽 𝑛2 where the sum is in 𝔽2 and 𝑥 ≤ 𝑢 if and only if 𝑥𝑖 ≤ 𝑢 𝑖 for all 1 ≤ 𝑖 ≤ 𝑛 . Let us compute the coefficients 𝑎𝑢 of the function 𝑓(𝑥1 , 𝑥2 , 𝑥3 ) shown in Table 1 above:
ANOVA (DISPERSIOON ANALÜÜS) df SS ESS Regression 1 981245 RSS Residual 3 15875 TSS Total 4 997120 Coefficients Standard Error Intercept -488,5 246,1495141846 Perede arv (X) 0,443 0,0325320355 Sa1 RESIDUAL OUTPUT Observation Predicted Autode arv Y Residuals 1 2612,5 -92,5 2 2834 26 3 3055,5 -35,5
In steady state, a constant flow rate and agitation was applied until temperatures stayed constant, then were the readings taken. In unsteady state, bath was heated up and then cooled down with cold water running through the submerged coil and temperature readings were taken every five minutes. A theoretical overall heat transfer coefficient was calculated using different sources of literature and then they were compared to experimental coefficients. The difference is explained based on the conditions of equations and experimental setup. The results showed that overall heat transfer coefficient changed as expected, it was bigger with higher flow rates, which directly results in higher Reynolds numbers, and higher agitation rates. Some anomalies did occur, but they can be explained with atypical details in experimental setup. 3 4
Standard Error 1,327431 Observations 35 ANOVA Significance df SS MS F F Regression 1 107,3533 107,3533 60,92444 5,39E-09 Residual 33 58,14841 1,762073 Total 34 165,5017 Coefficients Standard Error t Stat P-value Lower 95% Intercept 5,502895 0,707134 7,781965 5,75E-09 4,064219 d 0,452119 0,057924 7,805411 5,39E-09 0,334272 Regr. Võrrand h=5,5029+0,4521*d Jah. Regressioonivõrrand on sama mis graafikul. Regressioonivõrrand on usaldatav. 22. 1 Jääkstandardhälve ja kõrguse standardhälve Jääkstandardhälve iseloomustab funktsioontunnuse keskmist erinevust regressioonijoonest
Standard Error 240384591,86 mudeli standardviga Korrigeeritud determinatsioonikordaja on Observations 50 vaatluspunktide arv 1,98%. ANOVA df SS MS F Significance F Regression 1 1,15015E+017 1,2E+017 1,990401 0,1647474991 Residual 48 2,77367E+018 5,8E+016 Total 49 2,88868E+018 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 94575011,592 37913746,956 2,494478 0,01611 18344315,5246 1,7E+008 X1 8,3099697405 5,8901885958 1,410816 0,164747 -3,5330479689 20,15299 Y ja X1 hajuvusdiagrmm 40000000 30000000 X1 Paarisregressioonivõrra
mille diameeter on 15 cm ja kõrgus 16 m? 30. SUMMARY OUTPUT Regression Statistics Multiple R 0.733264 R Square 0.537676 Adjusted R Sq 0.511991 Standard Error 0.721537 Observations 20 ANOVA df SS MS F Significance F Regression 1 10.89841 10.89841 20.9337 0.000235 Residual 18 9.371086 0.520616 Total 19 20.2695 Coefficients Standard Error t Stat P-value Lower 95%Upper 95%Lower 95,0% Upper 95,0% Intercept 3.850082 0.444476 8.662071 7.8E-008 2.916272 4.783891 2.916272 4.783891 X Variable 1 0.284736 0.062233 4.575336 0.000235 0.15399 0.415482 0.15399 0.415482 12 Dkesk
Standard Error 0,585169098 Observations 16 ANOVA df SS MS F Significance F Regression 1 16,68045478 16,68045 48,71302735 6,45445E-06 Residual 14 4,793920222 0,342423 Total 15 21,474375 Upper Coefficients Standard Error t Stat P-value Lower 95% 95% Intercept 3,902516762 0,531490252 7,342593 3,66132E-06 2,762583548 5,04245 X Variable 1 0,409323758 0,05864681 6,979472 6,45445E-06 0,283538861 0,5351087 27) Kuna P value on väiksem kui 0,05 siis regressioonvõrrand on usaldatav. 28) Saadud võrrandi jääkstandardhälve on 0,59m. Kõrguse standardhälve on1,20m. Jääkstandardhälve e
Adjusted R Square 0,0522780054 Standard Error 14,0519899745 Observations 30 ANOVA df SS MS F Significance F Regression 1 513,330843841 513,330844 2,599690801 0,1181007 Residual 28 5528,83582283 197,458422 Total 29 6042,16666667 Coefficients Standard Error t Stat P-value Lower 95% Intercept 11,6756701836 27,0953075954 0,43091115 0,669832231 -43,8265507 Gümn_keskmHinne 10,0798045001 6,2516011193 1,61235567 0,118100702 -2,72601971 Korrelatsioonikordaja SEOS tr=
Regression 1 3,2827547444 vabaliige -2,0057989331 Residual 8 0,5024741556 tõus 0,0588035857 Total 9 3,7852289 Coefficients Standard Error vabaliige Intercept -2,005799 1,6744575692 tõus a Läbimüük, tuh. kr 0,0588036 0,0081338557 mudelit otsitakse kujul y= ax + b a) Tootmisvarude sõltuvust läbimüügist kirjeldab mudel Y=0,059X - 2,006,
ANOVA df SS MS Regression 1 257.66725 257.66725 Residual 39 272.2308 6.9802769 Total 40 529.89805 Coefficients Standard Error t Stat Intercept 73.944794 0.8677696 85.212474 X Variable 1 0.0001347 2.22E-005 6.0756575 parameetrid a ja b käsitsi a ja b funktsiooniga a 73.944794 a 73.944794 b 0
a näitab, kui palju muutub y ühe x-ühiku muutumise korral (iga aastaga sissetulek väheneb 3,57 võrra) Oluline on R2 ehk kui suure osa kogu ennustatava muutuja variatiivsusest kirjeldab ära prediktor. ANOVA tabelis ennekõike oluline p-väärtus <0,05, mis näitab, kas mudel on statistiliselt oluline. Koefitsentide tabeli põhjal saab ehitada regressioonivõrrandi (Uuring nr 2:) Coefficients Mode Unstandardized Standard Error Standardized t p l H₁ (Intercept) 331.581 1.883 176.120 <.001 AGE_R -1.021 0.044 -0.345 -23.115 <.001 Vanuse regressioonikordaja ehk tõus on -1,02 ehk kui vanus suureneb ühe ühiku võrra, väheneb probleemilahendusoskus 1,02 punkti võrra. Standardiseeritud ühikutes on tõus -0,345 ehk kui vanus suureneb ühe ühiku võrra,
9253 .0628 .0018 F level or tolerance or VIN insufficient for further computation. Summary Table Action Vars Wilks' Step Entered Removed In Lambda Sig. Label 1 T76 1 .64533 .0014 Meie orgis edutatakse haid tootajaid 2 T73 2 .60007 .0046 Saan ise planeerida oma tood Classification Function Coefficients (Fisher's Linear Discriminant Functions) FIRMA = 1 2 3 T73 2.010843 1.397240 1.471168 T76 1.164367 .7263430 2.074298 (constant) -5.026450 -2.865955 -6.383697 Canonical Discriminant Functions Pct of Cum Canonical After Wilks'
Multiple R 0,948422877 R Square 0,899505953 Adjusted R Square 0,896460679 Standard Error 1160,052615 Observations 35 ANOVA df SS MS F Significance F Regression 1 397496237,381 397496237,4 295,37766127 4,969708E-018 Residual 33 44408828,2678 1345722,069 Total 34 441905065,649 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0%Upper 95,0% Intercept 429,600229 245,692413075 1,748528673 0,0896708332 -70,264741368 929,4651993 -70,26474 929,4651993 X Variable 1 0,73444368 0,0427336191 17,18655467 4,96971E-018 0,6475014786 0,821385881 0,647501 0,821385881 90% Usaldusvahemik Usaldusvahemik: Alumine piir Ülemine piir
Funktsionaalsete ja anatoomiliste andmete integreerimine mudeli määratlemine ja sobitamine (model specification & fitting) statistiline järeldamine ja tulemuste analüüs (inference & results interrogation) GLM · y = a set of outcome variables · x = a set of pre-program variables or covariates · b0 = the set of intercepts (value of each y when each x=0) · b = a set of coefficients, one each for each x · e = error ICA · ICA on tehnika, et eraldada lineaarselt segatud/summeritud signaalid. · Ülemise kõvera lineaar kombinatsioon A - 2*B. · Alumise kõvera lineaar kombinatsioon 1.73*A +3.41*B. · Meetodi puudus: ei saa taastuda algse signaali amplituudi Andmete töötlemine · Etapid: eeltöötlus (preprocessing) Ruumiline ja ajaline eeltöötlus Ruumiline normimine
Standard Error 219,67192353 Observations 135 ANOVA df SS MS F Significance F Regression 4 14233112 3558278 73,7379 1,69E-032 Residual 130 6273248 48255,75 Total 134 20506360 Coefficients Standard Error t Stat P-value Lower 95% Intercept -1916,899217 238,9411 -8,022475 5,30E-013 -2389,616 x1 30,890119453 5,350517 5,773296 5,42E-008 20,30476 x2 6,6876682557 2,381831 2,807785 0,005758 1,975501 x3 11,040861678 2,719814 4,059418 8,44E-005 5,660035 x4 1548,4425989 121,5685 12,7372 1,27E-024 1307,934
10. Protseduur Regression SUMMARY OUTPUT Regression Statistics Multiple R 0.940941045 R Square 0.885370049 Adjusted R Square 0.881276123 Standard Error 1401.579976 Observations 30 ANOVA df SS Regression 1 424835227.446 Residual 28 55003940.021 Total 29 479839167.467 Coefficients Standard Error Intercept 5767.471447 740.827303296 X Variable 1 1.341186029 0.0912003793 11. Kasutatud materjalide loetelu 1 Kodutöö E4 juhend statistika_kodutoo_juhend_2017_kaug.pdf 2 korrelatsioonikordajad.xls 3 punkthinnangud.xlsx 4 regressioonanalyys2.xls 5 Kriitiliste punktide jaotus tabelhttp://kontromat.ru/?page_id=4200
R Square 0,420956641 Adjusted R Square 0,410233616 Standard Error 6,868962883 Observations 56 ANOVA df SS Regression 1 1852,261841 Residual 54 2547,863159 Total 55 4400,125 Coefficients Standard Error Intercept -52,48722507 18,75379388 PIKKUS 0,689402893 0,110030499 PIKKUS 170 161 massid on seotud? 183
45,1587 63,5791 Regression 1 45,15871 1 2 2,83268E-13 0,71027 Residual 159 112,9339 6 Total 160 158,0926 Standard Upper Lower Up Coefficients Error t Stat P-value Lower 95% 95% 95,0% 95, 5,75229 0,98533696 0,9853369 2,015 Intercept 1,500529739 0,260858 4 4,4E-08 9 2,015723 7 7,97365 1,25255564 1,2525556 2,077
g., mechanics, style, organization) of IEA are not fixed, but rather are constructed as a function of the domains assessed in the rating rubric. The weights for prox variables associated with these domains are predicted based on human ratings, and then are combined with the score calculated for content. Reliability and Validity,Because AES models often formed by using more than two raters, studies that have evaluated inter-rater agreement have usually showed that the agreement coefficients between the computer and human raters is at least as high or higher than among human raters themselves (Elliot, 2003; Landauer et al., 2003; Page & Petersen, 1995). All AES engines have obtained exact agreements with humans as high as the mid-80's and adjacent agreements in the mid-high 90's--slightly higher than the agreement coefficients for trained human raters. Several validity studies have suggested that AES engines tap the same construct as that being evaluated by human raters
0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 SUMMARY OUTPUT Regression Statistics Multiple R 0,4266894 R Square 0,1820639 Adjusted R Square 0,1679615 Standard Error 36,134229 Observations 60 ANOVA df SS MS F Significance F Regression 1 16856,6 16856,6 12,91018 0,000675 Residual 58 75729,59 1305,683 Total 59 92586,18 Coefficients Standard Error t Stat P-value Lower 95%Upper 95% Intercept 351,73616 9,447673 37,22993 3,5E-042 332,8246 370,6477 tinglik aeg t -0,967852 0,269366 -3,593074 0,000675 -1,507047 -0,428657 RESIDUAL OUTPUT Observation Predicted Lahutused Residuals Aproksimeerimisviga 1 350,76831 -78,76831 78,76831 3,479296
Observations 28 ANOVA df SS MS F Significance F Regression 1 161,5251 161,5251 20,67651 0,000111 Residual 26 203,1123 7,812011 urem kui 0,05 Total 27 364,6374 Coefficients Standard Error t Stat P-value Lower 95%Upper 95% dardhälve ehk lineaarse regressioonmudeli Intercept -0,645581 3,899638 -0,165549 0,869792 -8,661401 7,370239 X Variable 1 1,176576 0,258751 4,547143 0,000111 0,644706 1,708445 Chart Title 24
ennustavas muutujas statistiliselt oluline erinevus ehk ütleb, milliste muutujate lisamisel paraneb mudeli ennustusvõime Kui Overall Statistics näitab Sig on suurem kui .05 = prediktorite mudelisse lisamine ie parandaks mudeli ennustusvõimet statistiliselt oluliselt. Block 1: Esimesed kaks tabelit annavad ülevaate mudeli üldisest sobivusest. a) Esimesest tabelist Omnibus Tests of Model Coefficients näeme, kas meie mudel on statistiliselt oluline. Kui p-väärtus on alla 0.05, siis on mudel statistiliselt oluline. Kui kasutame prediktorite sisestamiseks Enter meetodit, siis on need väärtused identsed Omnibus Tests of Model Coefficients Chi-square df Sig. Step 26,143 7 ,000 Step 1 Block 26,143 7 ,000
0, 222697 Norra Jens Stoltenberg 15 349,1 Programmis Gretl peale mudeli hindamist Parameetrite hinnangute usalduspiirid Analysis->Confidence intervals for coefficients usaldatavusega 95% SKP = 14,0n - 54,7 houthakker.gdt
Regression 9 3403,864 111,0805 1,45893E-88 Residual 290 30,64322 Väga usaldusväärne Total 299 Upper Coefficients t Stat P-value Lower 95% Upper 95% Lower 95,0% 95,0% Intercept 13,35620356 8,842238 9,32E-17 10,38326958 16,32913755 10,38326958 16,32913755 Piimalehmad, keskmine arv 0,002010385 0,225657 0,821627 -0,015524144 0,019544915 -0,015524144 0,019544915 Koresööda kogus looma kohta 0,000243501 2,08012 0,038393 1,3104E-05 0,000473898 1,3104E-05 0,000473898
jne) saab esitada ühe fiktiivse tunnuse abil. · Kui kvalitatiivsel seletaval tunnusel n erinevat väärtust, siis vabaliige ja n-1 kaheväärtuselist fiktiivset tunnust; · vastasel juhul perfektne multikollineaarsus! · Väärtus, mille fiktiivne tunnus mudelis puudub: baasväärtus, referentsväärtus (base, control, reference category) · Fiktiivsete tunnuste kordajad: diferentsiaalsed vabaliikmed (differential intercept coefficients) · Ökonomeetria pakettides fiktiivse tunnuse nimetus tihti indicator variable ehk indikaatortunnus. · Fiktiivne tunnus, sest tegelikult ühe ja sama tunnuse erinevad väärtused 59. Diferentsiaalsete vabaliikmete tõlgendamine. 60. Kitsendused parameetritele, kitsendatud ja kitsendamata mudel. Üht fiktiivset tunnust tervest komplektist EI TOHI eemaldada. Üks fiktiivne tunnus ei ole eraldi tunnus, vaid üks väärtus. Terve tunnus on fiktiivsete tunnuste komplekt! Eemaldada
Multiple R 0,62 R Square 0,38 Adjusted R Square 0,37 Standard Error 13,13 Observations 68 ANOVA df SS MS F Regression 1 6944,307600653 6944,308 40,25841 Residual 66 11384,56004641 172,4933 Total 67 18328,86764706 Coefficients Standard Error t Stat P-value Intercept -121,6820381 30,2451763004 -4,023188 0,00015 PIKKUS 1,1082989106 0,1746741303 6,344952 2,3E-008 Regressioonivõrrand lahti kirjutatuna leitud kordajate väärtusi kasutades mass= -121,68 + 1,108 x pikkus Prognoos 170 cm pikkuse tudengi kehamassile leitud regressioonivõrrandi põhjal Arvutused: mass 66,73 Vastus täislausega: Keskmiselt võiks 170cm pikk tudeng kaaluda 66, 73 kg.
In [33] frequency-domain image processing is used to separate periodic structures in the image (the fabric weave/knit pattern) from non-periodic structures in the image (the pills). 17 The authors propose that for two-dimensional discrete wavelet transform (2DDWT) analysis of un- pilled fabric images, where the wavelet scale is close to the fabric inter-yarn pitch, the distribution of detail coefficients will have a relatively small standard deviation. On the other hand, when the amount of pilling increases, also the standard deviation will increase as the pills introduce variations into the image that disrupt the underlying pattern of the fabric structure. Referring, for instance, to Figure 17, taken from [33], it can be noticed that for fabrics with pilling grade equal to 1 (in the paper indicated with the letter i) a lower value for standard deviation (i.e. the coefficient in the
DAasta_1 0,00000 1,0000 0,75000 0 DAasta_2 0,00000 1,0000 0,75000 0 DAasta_3 0,00000 1,0000 0,75000 0 Brutopalk 385,49 752,38 170,84 0 Linlased 0,30552 0,88913 0,25311 0 Korgharitud 0,098137 0,31453 0,055168 0 Mehed 0,47906 0,56832 0,033345 0 Lisa 5. Esialgse mudeli korrelatsioonikordajate maatriks Correlation coefficients, using the observations 1 - 60 5% critical value (two-tailed) = 0,2542 for n = 60 Brutopalk Linlased Korgharitud Mehed DAasta_1 1,0000 0,2120 0,4201 0,1335 -0,6028 Brutopalk 1,0000 0,5873 -0,2164 0,0662 Linlased 1,0000 -0,2023 0,0538 Korgharitud
buffers to make embalming fluid. Formaldehyde is also used in many hospitals and laboratories to preserve tissue specimens. Physical-chemical date: CAS number: 50-00-0 IUPAC name: Methanal Structure formula: HCHO Physical characteristics: Colorless gas (normal.), Odor: Pungent, suffocating odor; highly irritating , Density: 0.815 g/mL (8153 g/cm³ (20°C) ) Molar mass 30.03 g mol-1 s, Solubility: Freshwater at 20°C Very soluble; up to 55% Partition coefficients: Log Pow=0.35 (25°C), Log Koc= 1.567 Melting and boiling point: Tm=-92°C,Tb=-19°C Autoignition temperature: 300 °C Henry's law constant at 25°C H: 0,034 Pa*m3/mol TOXICOKINETICS The toxicokinetics of formaldehyde after inhalation, oral, or dermal exposure has been reported in several species by many investigators. The toxicokinetics in all of the animals studied is similar across species lines. Formaldehyde is an essential metabolic intermediate in all cells. It is produced
26 kõrgem ANOVA EHF 1 663.3 12418.7 3.69 3.37 sorditu df EHF 1 646.4 9801.1 3.81 3.41 sorditu Regression 1 EHF 1 552.7 9090.1 3.92 3.33 esimene Residual 103 EHF 1 592.0 13483.3 3.86 3.30 esimene Total 104 EHF 1 622.4 8910.1 4.58 3.61 sorditu EHF 1 547.1 9786.0 3.24 3.51 esimene Coefficients EHF 1 527.5 7873.4 3.22 3.37 sorditu Intercept 3328.288 EHF 1 543.2 7205.5 3.64 3.52 sorditu Mass 11.10614 EHF 1 614.8 14277.2 4.00 3.69 kõrgem EHF 1 578.2 7391.1 3.48 3.17 esimene EHF 1 518.1 10421.3 4.16 3.51 esimene EHF 1 614.6 12426.3 3.96 3.56 esimene EHF 1 617.8 11767.5 3.45 3.48 esimene EHF 1 590.1 12283.9 3.02 2
199 2 3 2 Observatio 68.000 3 1 2 2 2 2 ANOVA 2 1 1 df SS MS 5 2 3 Regression 2 73.561 36.781 5 5 3 Residual 65 93.439 1.438 6 3 3 Total 67 167.000 4 2 3 1 1 1 Coefficients Standard Error t Stat 3 1 3 Intercept -0.011 0.514 -0.021 2 4 2 X1 0.180 0.110 1.633 2 1 2 X2 1.401 0.234 5.976 6 5 3 2 1 2 5 4 3 Mudel on olulisuse nivool p=0,05 statistiliselt oluline. 6 1 2 2 2 1
Andmeanalüüs MS Exceli abil - regressioonanalüüs Residual 30 818.0121 27.2671 Prognoosijäägid Total 31 4589.8750 Kokku Regressioonivõrrandi kordajate analüüs Coefficients Standard t Stat P-value Lower 95% Upper 95% Parameetri Error t-statistik Parameetri Alumine Ülemine hinnang Hinnangu olulisuse 95%-line 95%-line statndardviga tõenäosus usalduspiir usalduspiir
the same direction to the cathode ● The neutral molecules are transported at a speed of EOF, but not separated. ● Anions migrate more slowly as they are attracted to the anode, but move under the influence of EOF toward the cathode. Migration time is determined by a characteristic constant of ion mobility and electroosmotic flow coefficients: , where V velocity migration; L length of capillary (the detector); t m time of migration; E electric field strength; u ion mobility; μ ion the coefficient of EOF. EOF
Adjusted R Square 0,991 Standard Error 0,063 Observations 180 ANOVA df SS MS F Signif F Regression 5 76,102 15,220 3811,568 1,02E-175 Residual 174 0,695 0,004 Total 179 76,797 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept -0,054 0,030 -1,811 0,072 -0,113 0,005 Inglise k 0,001 0,008 0,143 0,886 -0,015 0,018 Emakeel 0,278 0,009 32,330 0,000 0,261 0,295 Ajalugu 0,263 0,009 30,064 0,000 0,246 0,281
lation around the setpoint. These waveforms are typical for a system with an underdamped response. The ideal goal for most PID systems is to achieve a crit- ically damped response, like that shown in Figure 5.12. Here, the system rises rapidly to the setpoint but does not overshoot or oscillate when the setpoint is reached. Practical Considerations Although a PID loop can compensate for varying loads, it still must be tuned. Tuning is the process of selecting the parameters (coefficients) of the three terms. That is, how much of the integral and derivative terms should be added to the G ¥ e term, and how large should G be? There are a number of ways to adjust these values, such as the Ziegler/Nichols method. The primary dif- Output Control Methods 117 Figure 5.12 Critically damped system. ficulty in adjusting the parameters of the PID loop is that adjusting one
viimase aasta jooksul viimase aasta jooksul ANOVA viimase kuu jooksul df SS viimase aasta jooksul Regression 1 2470,0218199111 rohkem kui aasta tagasi Residual 55 1591,9080046504 viimase kuu jooksul Total 56 4061,9298245614 rohkem kui aasta tagasi viimase aasta jooksul Coefficients Standard Error viimase kuu jooksul Intercept 79,3727035251 10,4263453444 viimase aasta jooksul JALANR 2,3213629579 0,2512871575 viimase 10 päeva jooksul viimase kuu jooksul viimase kuu jooksul Lineaarne regressioonivõrrand viimase kuu jooksul Pikkus = a + b×Jalanumber viimase aasta jooksul Pikkus = 79,3727 + 2,321363 x Jalanumber rohkem kui aasta tagasi viimase kuu jooksul
regression equations simultaneously (Bollen 1989, analysis to reduce measurement error by having multiple indicators per latent variable, overall testing of the model fit Hair et al. 1998: 584). rather than coefficients individually, the ability to test models · Structural equation modeling may be used as with multiple dependent variables, the ability to model mediating variables, the ability to model error terms." powerful alternative for instance to multiple
impingement heat transfer environment. Air Freezing Methods When freezing products thicker than 20 mm, Air is by far the most widely used method of the benefits of impingement freezing can still freezing meat, as it is economical, hygienic, be achieved; however, the surface heat trans- and relatively noncorrosive to equipment. fer coefficients later in the freezing process The big advantage of air systems is their ver- should be reduced to balance the overall satility, especially when there is a require- process efficiency. The process is also very ment to freeze a variety of irregularly shaped attractive for products that require very rapid products or individual products. However, surface freezing and chilling. relatively low rates of heat transfer are attained from product surfaces in air systems.
2009 Investigation of Sick Buildings –Toxic Moulds. Indoor Built Environ 2010;19;1:40–47. IOM Institute of Medicine 2004. Damp Indoor Spaces and Health. National Academy of Sciences. Washington D.C: National Academies Press. ISO EN 7730. Moderate thermal environments – Determination of the PMV and PPD indices and specification of the conditions for thermal comfort, 1994. ISO/FDIS 13789. Thermal performance of buildings -- Transmission and ventilation heat transfer coefficients -- Calculation method. Jaakkola, J.J.K., Heinonen, O.P., Seppänen, O. 1989. Sick building syndrome, sensation of dryness and thermal comfort in relation to room temperature in an office building: need for individual control of temperature. Environmental International 1989; 15: 163-168. Janssens A, Hens H. Interstitial condensation due to air leakage: a sensitivity analysis. Journal of Thermal Envelope and Building Science 2003;27(1):15–29. Johansson, P., Samuelson, I., Ekstrand-Tobin, A
df SS Regression 1 52.1176756006304 Residual 21 25.5988461385001 Total 22 77.7165217391305 Coefficients Standard Error Intercept 64.8352590293184 2.2718864688193 d usutavalt erinevad X Variable 1 -0.691578754169526 0.10576687465833 Tabel 1 30 25 Proteiin kuivaines %
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