Independent variables number

Number of independent equations Number of degrees of freedom Number of independent variables Number of zeros of function Pressure upstream of nozzle in flapper/nozzle system Pressures applied to limbs of manometer tube or pressures downstream and upstream of orifice plate Distillation column pressure Pressure in feedback bellows of pneumatic controller Frictional drag per unit cross-sectional area of manometer tube... 

Therefore, the null hypothesis Hoi must be rejected. Instead, its negation Hi ( The dependent variable total time of project duration (TTpd) is influenced by the independent variable number of persons involved (Ajp) ) is confirmed. Furthermore, the expectation that a proper estimation of the duration of each activity can abbreviate TTpd is not fulfilled instead, the null hypothesis H02 is confirmed. 

Number of Student Groups Number of Independent Variables Number of Categories in Each Variable Appropriate Statistical Test... 

Thus, only two of the five quantities Itl lJ-l-Utl-UlMfi lare independent. We choose the number of down spins [i] and nearest-neighbour pairs of down spins [ii] as the independent variables. Adding and subtracting the above two equations. 

On a given lattice of N sites, one number from the set A, N ] and another from the set A, Agg, A g] detennine the rest. We choose A and A as the independent variables. Assuming only nearest-neighbonr interactions, the energy of a given configuration... 

In applying Simpson s rule, over the interval [a, i>] of the independent variable, the interval is partitioned into an even number of subintervals and three consecutive points are used to determine the unique parabola that covers the area of the first... 

The Least Squares or Best-fit Line. The simplest type of approximating curve is a straight line, the equation of which can be written as in form number 1 above. It is customary to employ the above definition when X is the independent variable and Y is the dependent variable. 

The isotherm under test is then re-drawn as a t-plot, i.e. a curve of the amount adsorbed plotted against t rather than against p/p° the change of independent variable from p/p° to t is effected by reference to the standard t-curve. If the isotherm under test is identical in shape with the standard, the t-plot must be a straight line passing through the origin its slope = b say) must be equal to nja, since the number of molecular layers is equal to both t/ff and n/n ... 

To describe the state of a two-component system at equilibrium, we must specify the number of moles nj and na of each component, as well as—ordinarily- the pressure p and the absolute temperature T. It is the Gibbs free energy that provides the most familiar access to a discussion of equilibrium. The increment in G associated with increments in the independent variables mentioned above is given by the equation... 

Other correlations based partially on theoretical considerations but made to fit existing data also exist (71—75). A number of researchers have also attempted to separate from a by measuring the latter, sometimes in terms of the wetted area (76—78). Finally, a number of correlations for the mass transfer coefficient itself exist. These ate based on a mote fundamental theory of mass transfer in packed columns (79—82). Although certain predictions were verified by experimental evidence, these models often cannot serve as design basis because the equations contain the interfacial area as an independent variable. 

Equation 215 asserts that only N of the 2N mole numbers are independently variable. If the independent mole numbers are chosen as it follows from equation 212 that a set of Aiconditions on at the equiUbrium state can be written as follows, where i = 1,2, - , N ... 

The number of independent variables is reduced from the original nine to four. This is a great saving in terms of the number of experiments required to determine the desired function. For example, suppose that a decision is made to test only four values for each variable. Then it would require 4 = 262144 experiments to test aU. combinations of these values in the original equation. As a result of equation 59, only 4 = 256 tests are now required for four values each of the four B-numbers. 

The least-squares technique can be extended to any number of variables as long as the equation is linear in its coefficients. The linear correlation ofj vs X can be extended to the correlation ofj vs multiple independent variables generating an equation of the form ... 

Similarity Variables The physical meaning of the term similarity relates to internal similitude, or self-similitude. Thus, similar solutions in boundaiy-layer flow over a horizontal flat plate are those for which the horizontal component of velocity u has the property that two velocity profiles located at different coordinates x differ only by a scale factor. The mathematical interpretation of the term similarity is a transformation of variables carried out so that a reduction in the number of independent variables is achieved. There are essentially two methods for finding similarity variables, separation of variables (not the classical concept) and the use of continuous transformation groups. The basic theoiy is available in Ames (see the references). 

A difference equation is a relation between the differences and the independent variable, A y, A " y,. . . , Ay, y, x) = 0, where ( ) is some given function. The general case in which the interval between the successive points is any real number h, instead of I, can be reduced to that with interval size I by the substitution x = hx. Hence all further difference-equation work will assume the interval size between successive points is I. 

The intensive state of a PVT system is established when its temperature and pressure and the compositions of all phases are fixed. However, for equihbrium states these variables are not aU independent, and fixing a hmited number of them automaticaUy estabhshes the others. This number of independent variables is given by the phase rule, and is called the number of degrees of freedom of the system. It is the number of variables which may be arbitrarily specified and which must be so specified in order to fix the intensive state of a system at equihbrium. This number is the difference between the number of variables needed to characterize the system and the number of equations that may be written connecting these variables. 

Develop via mathematical expressions a valid process or equipment model that relates the input-output variables of the process and associated coefficients. Include both equality and inequality constraints. Use well-known physical principles (mass balances, energy balances), empirical relations, implicit concepts, and external restrictions. Identify the independent and dependent variables (number of degrees of freedom). 

The relationship set out in Eq. (9-115) can also be viewed via a different chain of causality with (DCFRR) as a given parameter, (PBP) as the independent variable, and n as the variable whose value is being sought. Such an approach is the basis for the lines in Fig. 9-31, each of which shows the number of years of projec t life required to achieve an effective interest rate or a (DCFRR) of 20 percent by projects having various payback periods. The three hues differ from each other with respec t to the matter of inflation. 

This is a technique developed during World War II for simulating stochastic physical processes, specifically, neutron transport in atomic bomb design. Its name comes from its resemblance to gambling. Each of the random variables in a relationship is represented by a distribution (Section 2.5). A random number generator picks a number from the distribution with a probability proportional to the pdf. After physical weighting the random numbers for each of the stochastic variables, the relationship is calculated to find the value of the independent variable (top event if a fault tree) for this particular combination of dependent variables (e.g.. components). 

The dimensionless numbers are important elements in the performance of model experiments, and they are determined by the normalizing procedure ot the independent variables. If, for example, free convection is considered in a room without ventilation, it is not possible to normalize the velocities by a supply velocity Uq. The normalized velocity can be defined by m u f po //ao where f, is the height of a cold or a hot surface. The Grashof number, Gr, will then appear in the buoyancy term in the Navier-Stokes equation (AT is the temperature difference between the hot and the cold surface) ... 

The flow field in front of an expanding piston is characterized by a leading gas-dynamic discontinuity, namely, a shock followed by a monotonic increase in gas-dynamic variables toward the piston. If both shock and piston are regarded as boundary conditions, the intermediate flow field may be treated as isentropic. Therefore, the gas dynamics can be described by only two dependent variables. Moreover, the assumption of similarity reduces the number of independent variables to one, which makes it possible to recast the conservation equations for mass and momentum into a set of two simultaneous ordinary differential equations ... 

Observation number Parameter number Independent variables Dependent variable Calculated variable... 

Sinee there are six unknowns and three equations, there are three independent variables. We ean associate these with any three elementary independent modes of point defect formation which conserve the numbers of atoms. These are like basis vectors for representing arbitrary point defect concentrations. Let us define them as follows ... 

For a system such as discussed here, the Gibb s Phase Rule [59] applies and establishes the degrees of freedom for control and operation of the system at equilibrium. The number of independent variables that can be defined for a system are ... 

As we will soon see, the nature of the work makes it extremely convenient to organize our data into matrices. (If you are not familiar with data matrices, please see the explanation of matrices in Appendix A before continuing.) In particular, it is useful to organize the dependent and independent variables into separate matrices. In the case of spectroscopy, if we measure the absorbance spectra of a number of samples of known composition, we assemble all of these spectra into one matrix which we will call the absorbance matrix. We also assemble all of the concentration values for the sample s components into a separate matrix called the concentration matrix. For those who are keeping score, the absorbance matrix contains the independent variables (also known as the x-data or the x-block), and the concentration matrix contains the dependent variables (also called the y-data or the y-block). 

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