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BlueSky Statistics delivers a comprehensive set of data management, statistics, machine learning, visualizations, and six sigma capabilities. Some of the capabilities include descriptive and inferential statistics, advanced modeling, survival analysis, longitudinal data analysis, power analysis, six sigma process analysis, control charts, design of experiments (DoE)…

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Main Menu

Top level menu groups related features in a ribbon bar


Sub menu

Ribbon bar displays

dialogs associated with top level menu


Data Grid

Editable datagrid displays data


R Editor

Write/paste and execute

R code


Output tabs

Displays the results of the analysis


Split indicator

Indicates whether the dataset is split by a variable

User Interface

  • Open, browse, create,  edit multiple datasets, add/remove variables, edit factor levels, recode.... all via a graphical user interface

  • Access popular statistics, machine learning, data mining, data manipulation, and exploratory data analysis functions

  • Access the output of the analysis in a rich graphical user interface that supports interactive tables, copy and paste into MS Office applications, export to popular formats like HTML, PDF

  • Run R programs and interact with the output of the analysis

Hover over the orange circles to see more info

Data Grid -Data tab


New Dataset 

Click to create a new dataset


Refresh active dataset in

Refresh active dataset running R code

the grid to reflect

changes made by


Open Datasets

Displays es of open names




Displays variable name, icon indicates type


Data tab 

Displays data in dataset


Variables tab 

Click to displays and modify variables in the dataset


Variables options

Options available on right click within the

variable column

  • Data Grid -Data tab.

  • Fully interactive data tab

  • Edit data in place

  • Create, edit, delete data rows or columns

  • Right click and convert a variable to factor, numeric, string...

Hover over the orange circles to see more info


Variable name

Edit the variable name


Variable class

Displays R class associated with the variable


Variable type

Displays R type

associated with the variable


Variable SPSS class

Displays corresponding SPSS

class associated with the variable


Variable levels

Edit the levels associated with a variable of type factor/ordered factor


Variables options

Options available on

right click

Data Grid -Variable tab

  • Data grid -Variable tab

  • Fully interactive variable grid

  • Inspect all variables

  • Edit variable names, create new variables, convert variables from one format to another

  • Add, edit factor levels

  • Delete variables

Hover over the orange circles to see more info

Output management

  • Share, reuse, reproduce the results of your analysis

  • Copy and paste tables and graphs to MS Word, Excel, and PowerPoint

  • Reorder output tables

  • Double-click on a graph to maximize the display

  • Save to BlueSky output format, HTML, R Markdown, LaTeX

Hover over the orange circles to see more info

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Dialog relaunch

Launch the dialog with the original settings that created the output.


Inspect R code

Displays the R code executed by the dialog


Move output chunk
Move the output associated with the dialog up


Delete output chunk 
Delete the output associated with the dialog lower


Save output

Save the output to

BlueSky format, HTML

or R Markdown


Clear Output

Clear the output file

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Execute R code

Step through R code, execute selected code, or run all


R code editor window

Hide or expand the R code editor window


Execute R code

Run all or selected R code 

R IDE for programmers

  • Copy, paste and execute R code from popular websites

  • Step through R code

  • Write, import, export, execute your own custom R code/script

Hover over the orange circles to see more info

Popular Features

Model Fitting, Tuning, and Scoring

Build, tune, and score datasets using over 50 algorithms across popular model families

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Survival Analysis

Competing Risks, Kaplan-Meier estimation, Cox Proportional Hazards Model

Machine Learning

  • Traditional Machine Learning Algorithms like decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), linear regression, and logistic regression, among others

  • Deep learning: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)

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Six Sigma

The functionality has been designed around the DMAIC methodology of Six Sigma, which is a data-driven quality strategy used to improve processes. DMAIC implies five quality improvement steps - Define, Measure, Analyze, Improve and Control. The comprehensive functionality includes

  • Pareto charts, fishbone cause and effect diagrams, process capability analysis, measure system analysis (gage R&R analysis, attribute agreement, and gage bias analysis), seven types of Shewhart control charts (XBar-R, XBar-S, I-MR, I-MR Between/Within, p, np, u, c-charts) with customizable settings for testing eight special causes, the ability to label process stages/phases, T-squared/ MPCC charts, EWMA charts, CUSUM charts, and multi-vari charts

  • Any number of additional sigma/limit lines can be added to any of the Shewhart charts to help identify early shifts and drifts in the process data

DoE (Design of Experiments)

DoE is a branch of applied statistics that deals with planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors (i.e., input variables) that control the value of a parameter or group of parameters (i.e., response variables). The experiments allow the designers to study/analyze the effect of each factor on the response variable and the effects of interactions between factors on the response variable. DoE is very useful in many different fields, including manufacturing, engineering/R&D, agriculture, marketing, and consumer goods, to Increase product quality, increase yield, reduce costs, or identify the desired mix of new product features/attributes. The comprehensive functionality includes

  • Creating or uploading factor details (factors and levels), creating several different types of design, inspecting a design, plotting a design, exporting a design, importing design responses, and modifying a design

  • Analyzing design responses with various statistical models such as Linear Model and Response Surface Model. In addition, responses can be analyzed with Main Effects and Interaction Effects plot, Half Normal plot for 2-level to plot Daniel effects normal (or Half)

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