Mean absolute error

If the oscilloscope does not recognize SPACE CR as first characters, or if Low-level exists on the RTS line longer than for about 2 seconds, then the oscilloscope exits the remote mode and sets the TxD Low for about 0.3s and thus generates a frame error. The oscilloscope answers with a RETURNCODE (0 CR LF) if it has recognized SPACE CR and has set the baud rate. The scope sends ‘ESC RMLK=0’ (ESC=1B hex.) and exits the Remote state if the button LOCAL is activated in Remote-ON state. The time between

not defined and the terminology is now almost useless. For example, the Bulletin of the American Meteorological Society, June 2006, p. 803, tells us that “. . . the thermohaline circulation, as measured in the Florida Straits, was near the long-term mean.” One is left to guess that the reference is probably to the volume transport, which the author could have stated, but if there is any information being conveyed about the heat or salt transports [which are by definition “thermohaline,” and require

considerations strongly suggest to use intraday data in order to obtain accurate volatility estimates. Under a continuous time arbitrage-free setup, the quadratic variations of the prices would allow us, in principle, to construct an approximately error free estimate of volatility by using data at the highest frequency available. Yet, empirical data at very short time scales differ in many ways from the arbitrage-free continuous time price processes. For foreign exchange rates, the main difference

use of pictures to convey ideas was, and still is a very effective form of communication, which we continue to use liberally in our modern societies. For example, if you see a red circle with a red diagonal line across it you will recognize this to mean that 5) ______. If there is a picture of a burning cigarette inside the circle, you understand the meaning immediately: Smoking is prohibited here. Pictures are very effective – and universal - form of communication. However, they are limited in their

Independence of r.v.s Independence of random vectors Mean independence Cumulative distribution function Probability mass function Marginal pmf Conditional pmf Probability density function Marginal pdf Conditional pdf Borel Paradox Stochastic ordering Support set Transformations of random variables . Transformation R1 → R1 Transformation R2 → R2 Convolution formulae Probability integral transformation Properties of random variables . . . . Expected value, mean Conditional expectation Two-way rule for expectations

problem mathematically is what really separates the real pros from the rest of the crowd. In fact even the humble PID contains those scary sounding calculus terms Integral and Derivative. This section aims to give you a feel for what these terms really mean. This will give you a real edge when understanding what’s going on in your controller. Derivatives Go into the control room of a process plant and ask the operator: “What’s the derivative of reactor 4’s pressure?” And the response will typically

Breusch and Godefrey Lagrange Multiplier : Box et Jenkins : Coopération Technique Belge : Difference Stationary : Heteroskedasticity and Autocorrelation Consistent : Mean Absolute Percentage Error : Moindres Carrés Ordinaires : Partial Autocorrelation Function : Processus Générateur de Données : Produit Intérieur Brut : Root Mean Square Error : Trend Stationary : Vector Autoregressive iii Avant propos Le contexte actuel d’ouverture tous azimuts, aussi bien dans le secteur des affaires commerciales

by local cults (cultus = veneration). "Canonisatio per viam cultus,” or canonization by popular veneration, became common, continuing through the Middle Ages. The unregulated saints market led regional Roman Catholic hierarchs to declare heresy and error (Kemp [1948, p. 11]).7 The attempted centralization of the saint-making process in the hierarchy of the Church began as early as the fifth Council of Carthage (401C.E.), when bishops were designated as the primary ecclesiastical authority to determine

applications Arthur CHARPENTIER 2.7.2 Le test du CU SUM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7.3 Le test BDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7.4 Le test RESE T (Regression error speci…cation test ) . . . . . . . . . . . . . . . 2.7.5 La procédure AUT OREG sous SAS=ET S . . . . . . . . . . . . . . . . . . . . 2.8 Application sur des données réelles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.1 Modélisation

Least Squares Date: 06/21/00 Time: 19:29 Included observations: 22 after adjusting end points Variable C LADPGR LREDUCGR LRSANTEGR R-squared Coefficient 3.219729 0.002606 -0.142340 0.193574 Std. Error 1.114356 0.043986 0.044247 0.024011 Ex pe rt PD F 0.798524 0.764945 0.034240 0.021103 45.22674 Mean dependent var Adjusted R-squared S.E. of regression S.D.dependentvar Akaike info criterion Schwarz criterion F-statistic Sum squared resid Log likelihood Durbin-Watson stat 1.241408 ~ Prob(F-statistic)