Leidsid 10 sarnast õppematerjali, mis on seotud failiga "Mitmene regressioonanalüüs ja mittelineaarne regressioonanalüüs". Need materjalid aitavad sul teemat sügavamalt mõista.
coefficients, anova, mhhv, round, mittelineaarne, plot, sqrt, length, curve, border, mitmene, select, names, paste, rinnaspindala, mean, mfcol, diameeter, sigmaKodutöö: Lineaarne regressioonanalüüs PD <- read.csv("puud15.CSV") # parameeter sep="," ja dec="." PD$d_k<-with(PD, ifelse(d2>0,(d1+d2)/2, d1)) PD.<-subset(PD, prt==642 & aasta==2001) PD.<-droplevels(PD.) plot(h~d_k,data=PD.) PD.H <- subset(PD., h>0 & hv>0) table(PD.H$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) 7.7585
R-Studio Kontrolltöö Mitu proovitükki on kogu andmestikus? #Mitu proovitükki on kogu andmestikus puud2015=read.csv("puud2015.csv",sep=";",dec=",") #Impordin andmed puud2015$D=ifelse(puud2015$D2>0,(puud2015$D1+puud2015$D2)/2,puud2015$D1) #Lisan tulba D length(table(puud2015$PRT)) #Vaatan mitu proovitükki on kogu andmestikus loetledes read > #Mitu proovitükki on kogu andmestikus > puud2015=read.csv("puud2015.csv",sep=";",dec=",") #Impordin andmed > puud2015$D=ifelse(puud2015$D2>0,(puud2015$D1+puud2015$D2)/2,puud2015$D1) #Lisan tulba D > length(table(puud2015$PRT)) #Vaatan mitu proovitükki on kogu andmestikus loetledes read [1] 229 VASTUS: Kogu andmestikus on 229 proovitükki. Mitu puud on sinu proovitükil? #Mitu puud on sinu proovitükil? PRT332=subset(puud2015,PRT=="332") #Teen eraldi andmestiku PRT332-st length(table(PRT332$PUU)) #Loetlen puude arvu PRT-l > #Mitu puud on sinu proovitükil? > PRT332=subset(puud2015,PRT=="332") #Teen eraldi andmestiku PRT332-st > leng
3 ELEKTRIAJAMITE ELEKTROONSED SÜSTEEMID 4 Valery Vodovozov, Dmitri Vinnikov, Raik Jansikene Toimetanud Evi-Õie Pless Kaane kujundanud Ann Gornischeff Käesoleva raamatu koostamist ja kirjastamist on toetanud SA Innove Tallinna Tehnikaülikool Elektriajamite ja jõuelektroonika instituut Ehitajate tee 5, Tallinn 19086 Telefon 620 3700 Faks 620 3701 http://www.ene.ttu.ee/elektriajamid/ Autoriõigus: Valery Vodovozov, Dmitri Vinnikov, Raik Jansikene TTÜ elektriajamite ja jõuelektroonika instituut, 2008 ISBN ............................ Kirjastaja: TTÜ elektriajamite ja jõuelektroonika instituut 3 Sisukord Tähised............................................................................................................................5 Sümbolid .....................
APSE ADA Programming Support Environment APT Address Pass Through + Advanced Parallel Technology + Automatically Programmed Tools A/R Accounts Receivable ARA AppleTalk Remote Access ARAG Antireflective-Antiglare ARAS Antireflective-Antistatic ARC Acoustic Resonance Control .ARC Archive (file name extension) ARCA Advanced RISC Computing Architecture ARCnet Attached Resource Computer Network .ARJ Compressed File (file name extension) [Jung] ARL Adjusted Ring Length ARLL Advanced Run Length Limited ARM Advanced RISC Machine (processor) + Annotated Reference Manual + Asynchronous Response Mode ARMA Association of Records Managers and Administrators ARP Address Resolution Protocol [Novell] ARPANET Advanced Research Projects Agency Network ARPL Adjust Requested Privilege Level ARS Activity Reporting System [Unisys] ART Adaptive Resonance Theory (algorithm) ARTA Apple Real Time Architecture ARTIC A Real-Time Interface Coprocessor [IBM]
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glycerol molecule in which the hydroxyl muscle cells. These bundles (also known as groups are esterified with three fatty acids. fasciculi) are surrounded by another sheath The melting point and the iodine number of of connective tissue, the perimysium. A thin lipid that is associated with the muscle is layer of connective tissue, the endomysium, determined by the chain length and the degree surrounds the muscle cells themselves. The of saturation of the fatty acids. Phospholipids endomysium lies above the muscle cell mem- (phosphoglycerides) are another type of brane (sarcolemma) and consists of a base- Chemistry and Biochemistry of Meat 7 ment membrane that is associated with an basis, they make up approximately 10–12%
up. As processor speeds increase, more functionality is pushed into the soft- ware. The key factors that you must consider to determine your throughput requirements are: Interrupts How often must the interrupts occur, and how much processing must be per- formed in each ISR (interrupt service routine)? What is the maximum allow- able latency for servicing an interrupt? Will interrupts need to be turned off for an extended length of time, and how will that affect the latency of other interrupts? You may find that you need two (or more) processors—one to handle high-speed interrupts with short latency requirements but low com- plexity processing needs, and another to handle low-rate interrupts with more complex processing requirements. Interfaces What must the system talk to? How will the data be passed around or get to the outside world? How much hardware support will there be for the inter-
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