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A process {Y} is said to be trend-stationary if = +, where t is time, f is any function mapping from the reals to the reals, and {e} is a stationary process. The value () is said to be the trend value of the process at time t. Simplest example: stationarity around a linear trend
The polynomial regression model. can be expressed in matrix form in terms of a design matrix , a response vector , a parameter vector , and a vector of random errors. The i -th row of and will contain the x and y value for the i -th data sample. Then the model can be written as a system of linear equations : which when using pure matrix ...
The Cochran–Armitage test for trend, [1] [2] named for William Cochran and Peter Armitage, is used in categorical data analysis when the aim is to assess for the presence of an association between a variable with two categories and an ordinal variable with k categories. It modifies the Pearson chi-squared test to incorporate a suspected ...
Quadratic growth. In mathematics, a function or sequence is said to exhibit quadratic growth when its values are proportional to the square of the function argument or sequence position. "Quadratic growth" often means more generally "quadratic growth in the limit ", as the argument or sequence position goes to infinity – in big Theta notation ...
Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints. [4] [5] Curve fitting can involve either interpolation, [6] [7] where an exact fit to the data is required, or smoothing, [8] [9] in which a "smooth" function is ...
Here the ordinary least squares method is used to construct the regression line describing this law. In statistics, ordinary least squares ( OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of ...
Exponential growth is a process that increases quantity over time at an ever-increasing rate. It occurs when the instantaneous rate of change (that is, the derivative) of a quantity with respect to time is proportional to the quantity itself. Described as a function, a quantity undergoing exponential growth is an exponential function of time ...
In numerical analysis, polynomial interpolation is the interpolation of a given bivariate data set by the polynomial of lowest possible degree that passes through the points of the dataset. [1] Given a set of n + 1 data points , with no two the same, a polynomial function is said to interpolate the data if for each .