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The th triangular number has value (+) /, approximately /. For a real function of a real variable, quadratic growth is equivalent to the second derivative being constant (i.e., the third derivative being zero), and thus functions with quadratic growth are exactly the quadratic polynomials, as these are the kernel of the third derivative ...
The standard logistic function is the logistic function with parameters =, =, =, which yields = + = + = / / + /.In practice, due to the nature of the exponential function, it is often sufficient to compute the standard logistic function for over a small range of real numbers, such as a range contained in [−6, +6], as it quickly converges very close to its saturation values of 0 and 1.
For example, say one wants to calculate with a calculator N 10, the population at the tenth generation, knowing N 0 the initial population and λ the finite rate of increase. With the last formula, the result is immediate by plugging t = 10, whether with the previous one it is necessary to know N 9, N 8, ..., N 2 until N 1. We can identify ...
A quantity x depends exponentially on time t if = / where the constant a is the initial value of x, () =, the constant b is a positive growth factor, and τ is the time constant—the time required for x to increase by one factor of b: (+) = (+) / = / / = ().
A percentage change is a way to express a change in a variable. It represents the relative change between the old value and the new one. [6]For example, if a house is worth $100,000 today and the year after its value goes up to $110,000, the percentage change of its value can be expressed as = = %.
I = (PAT) is the mathematical notation of a formula put forward to describe the impact of human activity on the environment. I = P × A × T. The expression equates human impact on the environment to a function of three factors: population (P), affluence (A) and technology (T). [1]
Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
The formulas given in the previous section allow one to calculate the point estimates of α and β — that is, the coefficients of the regression line for the given set of data. However, those formulas do not tell us how precise the estimates are, i.e., how much the estimators α ^ {\displaystyle {\widehat {\alpha }}} and β ^ {\displaystyle ...