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For k > 1, the density function tends to zero as x approaches zero from above, increases until its mode and decreases after it. The density function has infinite negative slope at x = 0 if 0 < k < 1, infinite positive slope at x = 0 if 1 < k < 2 and null slope at x = 0 if k > 2. For k = 1 the density has a finite negative slope at x = 0.
The Tafel equation is an equation in electrochemical kinetics relating the rate of an electrochemical reaction to the overpotential. [1] The Tafel equation was first deduced experimentally and was later shown to have a theoretical justification.
Under certain assumptions (typically, normal distribution assumptions) there is a known ratio between the true slope, and the expected estimated slope. Frost and Thompson (2000) review several methods for estimating this ratio and hence correcting the estimated slope. [ 4 ]
The very ambiguous terms "percent solution" and "percentage solutions" with no other qualifiers, continue to occasionally be encountered. This common usage of % to mean m/v in biology is because of many biological solutions being dilute and water-based, an aqueous solution. Liquid water has a density of approximately 1 g/cm 3 (1 g/mL). Thus 100 ...
This ongoing current in both directions is called the exchange current density. When the potential is set more negative than the formal potential, the cathodic current is greater than the anodic current. Written as a reduction, cathodic current is positive. The net current density is the difference between the cathodic and anodic current density.
Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result, the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero.
(1) The Type I bias equations 1.1 and 1.2 are not affected by the sample size n. (2) Eq(1.4) is a re-arrangement of the second term in Eq(1.3). (3) The Type II bias and the variance and standard deviation all decrease with increasing sample size, and they also decrease, for a given sample size, when x's standard deviation σ becomes small ...
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.