Webinar: Add Analytics to Your Dashboard Applications
Whitepaper from Beagle Research Group: The Art of Analysis
Products for Developers
NetCharts Visualization Architecture
"NetCharts helped us complete our applications more quickly, and deliver more informative and concise ways for our clients to understand the technical data."
NetCharts Analytics Library
Included with NetCharts Designer, and NetCharts Server is a powerful analytics library that allows users to easily apply sophisticated data analysis- right out of the box.
The analytic library is used to analysis raw data, before it is placed into a chart, table, graph, scorecard or dashboard. For example, a grocery store manager could apply a Pareto function to raw register receipts to see a ranking of the most popular products in the store. Trend and forecasting lines can be applied to gauge future sales. And Statistical Process Control (SPC) functions can determine if the store is meeting sales and/or quality expectations.
The analytics library is implemented in the NetCharts Reporting Suite as process modules. The process module interface allows data to be modified through one or more transformations. In NetCharts Designer, the process modules may be selected via the Functions tab on the NDX data editor.
Any combination of functions can be chained together and mixed with other data processing routines to transform raw data using such functions as Pareto analysis, trending, seasonality, regression, correlation, control charts and other statistical techniques that provide actionable information.
For example, Six Sigma management methodology applies many of these analytical techniques to enhance business profitability, improve areas of specific business performance, increase productivity, reduce operational cost and improve customer satisfaction.
The analytics library includes more than 50 different analytical functions in five categories (see below). Plus, each Analytic module has built-in a GUI and online help dialog; a 100-page companion document helps you get the most out of your data:
List of Functions
General Functions
- Aggregate, Grade and Delete Columns
- Delete Rows
- Include Headers
- Count Group Names
- Sum Group Names
- Sort One Column
- Two Column Operation
Two Column Operation allows you to perform item-by-item addition, subtraction, multiplication or division with two columns of data of equal length and return the result in a column of the same length as the input columns.
Statistical Functions
- Bin Data
- Univariate Metrics
- Histogram Distribution
- Pareto Analysis
- Normal Distribution
- Quantile
- Mode
This Pareto chart is based on a combo chart and shows the number of occurrences and the cumulative effect each failed part has on the total causes of computer failure. The 80/20 lines are also plotted using notesets.
Financial Functions
- Moving Average
- Bollinger Bands
- Linear Regression
- Stochastic Oscillator
- Chaikin Oscillator
- Ultimate Oscillator
- Price Oscillator
The Price Oscillator is an indicator based on the difference between two moving averages over a short and a long period. It is expressed as either a percentage or in absolute terms. The short and long periods values can vary depending on user preference. It is used for for predictive stock analysis.
Calculus Functions
- Area
- Differentiation
- Integration
- Curve Length
- Surface Metrics
- Regression
- Fourier
- Filter
- Track Order
- Interpolation
Regression functions are useful in statistical surveys, polling, or other situations where a finite set of samples is used to determine the characteristics of the overall data set.
Statistical Process Control (SPC) Analytics
- SPC Attributes Chart – c
- SPC Attributes Chart – np
- SPC Attributes Chart - p
- SPC Attributes Chart - u
- SPC Variables Chart - EWMA
- SPC Variables Chart - Histogram
- SPC Variables Chart - Median-R
- SPC Variables Chart - Median-S
- SPC Variables Chart - Probability
- SPC Variables Chart - XBar-R
- SPC Variables Chart - XBar-s
The c chart is used to measure the number of defects for a given item.
It implies that each sample has an equal size.
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