Syntax of notation declarations. Notation takes both an external representation and an internal representation as arguments. Mathematica translates any input matching the ...

LinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a linear model of the form \[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ... that fits the y_i for successive x ...

Some basic matrix operations. Transposing a matrix interchanges the rows and columns in the matrix. If you transpose an m×n matrix, you get an n×m matrix as the result. ...

"Defining Functions" discusses how you can define functions in Mathematica. In a typical case, you would type in f[x_]=x^2 to define a function f. (Actually, the definitions ...

One of the important features of Mathematica is its ability to give you exact, symbolic, results for computations. There are, however, computations where it is just ...

When you write down a set of simultaneous equations in Mathematica, you are specifying a collection of constraints between variables. When you use Solve, you are finding ...

Mathematica has three functions for generating pseudorandom numbers that are distributed uniformly over a range of values. Pseudorandom number generation. Generating tables ...

When fitting models to data, it is often useful to analyze how well the model fits the data and how well the fitting meets the assumptions of the model. For a number of ...

expr_1; expr_2; ... evaluates the expr_i in turn, giving the last one as the result.

GroebnerBasis[{poly_1, poly_2, ...}, {x_1, x_2, ...}] gives a list of polynomials that form a Gröbner basis for the set of polynomials poly_i. GroebnerBasis[{poly_1, poly_2, ...