The SQL command INSERT inserts data into a database. An alternative is to use the Mathematica command SQLInsert, as described in "Inserting Data". If you find that the ...
Graph
(Built-in Mathematica Symbol) Graph[{e_1, e_2, ...}] yields a graph with edges e_j.Graph[{v 1, v 2, ...}, {e_1, e_2, ...}] yields the graph with vertices v_i and edges e_j. Graph[{..., w_i[v_i, ...], ...
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 ...
Plot3D
(Built-in Mathematica Symbol) Plot3D[f, {x, x_min, x_max}, {y, y_min, y_max}] generates a three-dimensional plot of f as a function of x and y. Plot3D[{f_1, f_2, ...}, {x, x_min, x_max}, {y, y_min, ...
BarChart[{y_1, y_2, ...}] makes a bar chart with bar lengths y_1, y_2, ....BarChart[{..., w_i[y_i, ...], ..., w_j[y_j, ...], ...}] makes a bar chart with bar features defined ...
PieChart3D[{y_1, y_2, ...}] makes a 3D pie chart with sector angle proportional to y_1, y_2, ....PieChart3D[{..., w_i[y_i, ...], ..., w_j[y_j, ...], ...}] makes a 3D pie ...
LogitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial logistic regression model of the form 1/(1 + E -(\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...)) ...
ProbitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial probit regression model of the form 1/2 (1 + erf((\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...
ClosenessCentrality[g] finds the closeness centrality.
Agglomerate[{e_1, e_2, ...}] gives an hierarchical clustering of the elements e_1, e_2, ....Agglomerate[{e_1 -> v_1, e_2 -> v_2, ...}] represents e_i with v_i in each ...