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Internal rate of return (IRR) is a method of calculating an investment's rate of return.The term internal refers to the fact that the calculation excludes external factors, such as the risk-free rate, inflation, the cost of capital, or financial risk.
Time series of the Tent map for the parameter m=2.0 which shows numerical error: "the plot of time series (plot of x variable with respect to number of iterations) stops fluctuating and no values are observed after n=50". Parameter m= 2.0, initial point is random.
Each cash inflow/outflow is discounted back to its present value (PV). Then all are summed such that NPV is the sum of all terms: = (+) where: t is the time of the cash flow; i is the discount rate, i.e. the return that could be earned per unit of time on an investment with similar risk
Formulas say how to mechanically compute new values from existing values. Values are general numbers, but can also be pure text, dates, months, etc. Extensions of these concepts include logical spreadsheets. Various tools for programming sheets, visualizing data, remotely connecting sheets, displaying cells' dependencies, etc. are commonly ...
The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).
On a single-step or immediate-execution calculator, the user presses a key for each operation, calculating all the intermediate results, before the final value is shown. [1] [2] [3] On an expression or formula calculator, one types in an expression and then presses a key, such as "=" or "Enter", to evaluate the expression.
The use of the MAPE as a loss function for regression analysis is feasible both on a practical point of view and on a theoretical one, since the existence of an optimal model and the consistency of the empirical risk minimization can be proved. [1]
Run sequence plots [1] are an easy way to graphically summarize a univariate data set. A common assumption of univariate data sets is that they behave like: [2] random drawings; from a fixed distribution; with a common location; and; with a common scale. With run sequence plots, shifts in location and scale are typically quite evident.