This loads the package. Economized rational approximations. A Pad é approximation is very accurate near the center of expansion, but the error increases rapidly as you get ...
FindDistributionParameters[data, dist] finds the parameter estimates for the distribution dist from data.FindDistributionParameters[data, dist, {{p, p_0}, {q, q_0}, ...}] ...
LocationTest[data] tests whether the mean or median of the data is zero. LocationTest[{data_1, data_2}] tests whether the means or medians of data_1 and data_2 are ...
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 ...
SmoothKernelDistribution[{x_1, x_2, ...}] represents a smooth kernel distribution based on the data values x_i.SmoothKernelDistribution[{{x_1, y_1, ...}, {x_2, y_2, ...}, ...
BoxWhiskerChart[{x_1, x_2, ...}] makes a box-and-whisker chart for the values x_i.BoxWhiskerChart[{x_1, x_2, ...}, bwspec] makes a chart with box-and-whisker symbol ...
DistributionChart[{data_1, data_2, ...}] makes a distribution chart with a distribution symbol for each data_i.DistributionChart[{..., w_i[data_i, ...], ..., w_j[data_j, ...
KagiChart[{{date_1, p_1}, {date_2, p_2}, ...}] makes a Kagi chart with prices p_i at date date_i.KagiChart[{" name", daterange}] makes a Kagi chart of closing prices for the ...
A wide variety of plots and charts are used to gain an overview of data from a statistical perspective. Some summarize statistical computations on the data, while others ...
Mathematica provides a broad range of powerful constructs for laying out content on a screen or page. They are designed to be immediately useful for the beginner, yet also ...