Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://reference.wolfram.com/language/ref/ProbabilityPlot.html below:

ProbabilityPlot—Wolfram Language Documentation

We deliver solutions for the AI era—combining symbolic computation, data-driven insights and deep technology expertise.

• Data & Computational Intelligence
• Model-Based Design
• Algorithm Development
• Wolfram|Alpha for Business
• Blockchain Technology
• Education Technology
• Quantum Computation

WolframConsulting.com

• See Also
  • QuantilePlot
  • ProbabilityScalePlot
  • Quantile
  • Histogram
  • BoxWhiskerChart
  • DistributionChart
  • ListPlot
  • DiscretePlot
  • Plot
• Related Guides
  • Statistical Visualization
  • Random Variables
  • Reliability

ProbabilityPlot[list]

generates a plot of the CDF of list against the CDF of a normal distribution.

ProbabilityPlot[dist]

generates a plot of the CDF of the distribution dist against the CDF of a normal distribution.

ProbabilityPlot[data,rdata]

generates a plot of the CDF of data against the CDF of rdata.

ProbabilityPlot[data,rdist]

generates a plot of the CDF of data against the CDF of symbolic distribution rdist.

ProbabilityPlot[{data₁,data₂,…},ref]

generates a plot of the CDF of data_i against the CDF of a reference distribution ref.

• ProbabilityPlot is also known as normal probability plot in the one-argument form and probability-probability (P-P) plot in the two-argument form.
• ProbabilityPlot[data₁,data₂] works with data_i being either a dataset of real values or a symbolic univariate distribution.
• For datasets list, empirical CDFs are used, and for symbolic distributions dist, exact CDFs are used.
• ProbabilityPlot[data,dist[θ₁,…]] with symbolic parameters θ_i is equivalent to ProbabilityPlot[data,EstimatedDistribution[data,dist[θ₁,…]]].
• Datasets can be given in the following forms:
• {x₁,x₂,…} list of samples {Quantity[x₁,unit],Quantity[x₂,unit],…} samples with units <|k₁e₁,k₂e₂,…|> association of keys and samples WeightedData[…],EventData[…] augmented datasets TimeSeries[…],EventSeries[…],TemporalData[…] time series, event series, and temporal data w[{e₁,e₂,…},…] wrapper applied to a whole dataset w[{data₁,data₂,…}] wrapper applied to all datasets
• The form w[data] or w[dist] provides a wrapper w to be applied to the resulting graphics primitives.
• ProbabilityPlot[Tabular[…]cspec] extracts and plots values from the tabular object using the column specification cspec.
• The following forms of column specifications cspec are allowed for plotting tabular data:
• col_x plot the values from column x {col_x1,col_x2,…} plot columns x₁, x₂, …
• The following wrappers can be used:
• Annotation[e,label] provide an annotation Button[e,action] define an action to execute when the element is clicked EventHandler[e,…] define a general event handler for the element Highlighted[f_i,effect] dynamically highlight f_i with an effect Highlighted[f_i,Placed[effect,pos]] statically highlight f_i with an effect at position pos Hyperlink[e,uri] make the element act as a hyperlink PopupWindow[e,cont] attach a popup window to the element StatusArea[e,label] display in the status area when the element is moused over Style[e,opts] show the element using the specified styles Tooltip[e,label] attach an arbitrary tooltip to the element
• ProbabilityPlot has the same options as Graphics, with the following additions and changes: [List of all options]
• With Filling->Automatic, the region between a dataset and reference line will be filled. By default, "stems" are used for datasets, and "solid" filling is used for symbolic distributions. The setting Joined->True will force "solid" filling for datasets.
• The arguments supplied to functions in MeshFunctions and RegionFunction are , . Functions in ColorFunction are by default supplied with scaled versions of these arguments.
• The setting Joined->Automatic is equivalent to Joined->True when comparing two distributions and Joined->False otherwise.
• The setting PlotStyle->Automatic uses a sequence of different plot styles for different lines.
• With the ReferenceLineStyle->None, no reference line will be drawn.
• Possible highlighting effects for Highlighted and PlotHighlighting include:
• Highlight position specifications pos include:
• x, {x} effect at {x,y} with y chosen automatically {x,y} effect at {x,y} {pos₁,pos₂,…} multiple positions pos_i
• Typical settings for PlotLegends include:
• None no legend Automatic automatically determine legend {lbl₁,lbl₂,…} use lbl₁, lbl₂, … as legend labels Placed[lspec,…] specify placement for legend
• With ScalingFunctions->{s_x,s_y}, the coordinate is scaled using s_x and the coordinate is scaled using s_y.

A normal probability plot compared to an estimated normal distribution:

Compare to the standard normal distribution:

A probability-probability plot of two datasets:

Plot several datasets with a legend:

ProbabilityPlot works with numeric data:

ProbabilityPlot works with symbolic distributions:

Use multiple datasets and distributions:

The default reference distribution is the closest estimated NormalDistribution:

Specify data or distributions as the reference:

Reference distributions are estimated for each dataset:

Estimate specific reference distributions for numeric datasets:

Use all forms of built-in distributions:

Parametric:

Nonparametric:

Derived:

Plot values with units:

Plot the values from an association:

Plot data with weights:

Plot data from time series:

Get tabular data:

Compare the data to a normal distribution:

Compare multiple sets of data:

Use PivotToColumns to generate columns of "SepalWidth" per species:

Compare probability of sepal width per species:

Use abbreviated names for extended keys when the elements are unique:

Use legends for the plot:

Multiple datasets are automatically colored to be distinct:

Provide explicit styling to different sets:

Include legends for each dataset:

Add labels:

Use specific styles for the reference line:

Turn off the reference line:

Provide an interactive Tooltip for the data:

Provide a specific tooltip for the data:

Create filled plots:

Use shapes to distinguish different datasets:

Use Joined to connect datasets with lines:

Use a theme with grid lines:

Data usually has interactive callouts showing the coordinates when you mouse over them:

Including specific wrappers or interactions such as tooltips turns off the interactive features:

Choose from multiple interactive highlighting effects:

Color the line based on scaled value:

Color the line based on unscaled value:

Fill from the data to the reference line:

Use symbolic or explicit values for filling:

Points fill with stems:

Curves fill with solid regions:

Fill from the third dataset to the axis:

Fill between datasets using a particular style:

Use different styles above and below the filling level:

Use different fill colors:

Use a transparent orange filling:

Datasets are not joined by default:

Join the points:

Symbolic distributions are joined by default:

Use 20 mesh levels evenly spaced in the direction:

Use the mesh to divide the curve into deciles:

Specify Style and mesh levels in the direction:

Use a mesh evenly spaced in the and directions:

Show 5 mesh levels in the direction (red) and 10 in the direction (blue):

Color the mesh the same color as the plot:

Use a red mesh in the direction:

Use a red mesh in the direction and a blue mesh in the direction:

Use big red mesh points in the direction:

Plots have interactive coordinate callouts with the default setting PlotHighlightingAutomatic:

Use PlotHighlightingNone to disable the highlighting for the entire plot:

Move the mouse over the curve to highlight it with a ball and label:

Move the mouse over the curve to highlight it with a label and droplines to the axes:

Move the mouse over the plot to highlight it with a slice showing values corresponding to the position:

Move the mouse over the plot to highlight it with a slice showing values corresponding to the position:

Use a component that shows the points on the dataset closest to the position of the mouse cursor:

Specify the style for the points:

Use a component that shows the coordinates on the dataset closest to the mouse cursor:

Use Callout options to change the appearance of the label:

Combine components to create a custom effect:

By default, no legends are used:

Generate a legend using labels:

Generate a legend using placeholders:

Legends use the same styles as the plot:

Use Placed to specify the legend placement:

Place the legend inside the plot:

Use LineLegend to change the legend appearance:

ProbabilityPlot normally uses distinct colors to distinguish different sets of data:

Automatically use colors and shapes to distinguish sets of data:

Use shapes only:

Change the size of the default plot markers:

Use arbitrary text for plot markers:

Use explicit graphics for plot markers:

Use the same symbol for all the sets of data:

Use different style directives:

By default, different styles are chosen for multiple curves:

Explicitly specify the style for different curves:

Use a theme with grid lines:

Use a theme with high-contrast colors:

Turn off the grid lines:

Data is normally shown on linear scales:

Plot the data on a log-scaled axis:

KolmogorovSmirnovTest can be used to create a measure that quantifies the behavior in ProbabilityPlot. The Kolmogorov–Smirnov test statistic is equivalent to the maximum vertical distance between a point in the plot and the reference line:

The -value is larger when the points are closer to the reference line:

A -test for location assumes that the data was drawn from a NormalDistribution. If this assumption does not hold, a nonparametric test such as a signed-rank test is more appropriate. Suppose one wants to test for a location parameter of zero using the following data:

The plot suggests that the tails of the distribution are quite heavy. A SignedRankTest for location is more appropriate than the TTest:

Compare two time slices for a random process:

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4