New Features

64-Bit Support
Origin 8.6 is available in both native 64-bit and 32-bit versions.

The 64-bit application provides:

• Improved memory management
• Improved handling of large datasets
• Memory capacity limited only by installed hardware (32-bit application limited to 2 GB)

Note: Individual worksheet columns and matrices are limited to 90 million elements each.

Zoom and Scroll inside Graph Layer
Use the mouse and scroll wheel, or use the keyboard, to zoom and scroll inside a graph layer, in either X or Y direction. Quickly identify data sub range of interest and then use tools such as gadgets to analyze your selection.

Auto-hide Capability for Dockable Windows
Set dockable windows such as Project Explorer, Quick Help, and the new Messages Log, to auto hide mode. This frees up space in the Origin interface, and you can easily access these auto-hidden windows when needed. Some windows such as Messages Log will pop up even in auto hide mode, to display important messages.

New Gadgets

Based on multiple requests from our users, we have added three new gadgets in this version:

Vertical Cursor
Read X and Y coordinates of multiple data plots inside a layer or even across multiple stacked layers. Output the coordinate values to a worksheet, or tag your data with a line and label at desired X values.

Quick Sigmoidal Fit
Perform a quick sigmoidal fit to data within a region of interest. Select from multiple built-in fitting functions or use your own fitting function. Display asymptote lines and optionally fix parameters to desired values.

Intersect
Calculate the intersection points of multiple curves within a graph layer. Tag the intersection points and send the coordinate values to a worksheet.

Statistics: Multivariate Analysis (Pro Only).

Four commonly used multivariate analysis tools are new available
in Origin 8.6:

1. Principal Component Analysis
(PCA) is used to explain the variance-covariance structure of a set of variables through linear combinations of those variables. PCA is thus often used as a technique for reducing dimensionality.

There are two primary reasons for using PCA:

Data Reduction. PCA is most commonly used to condense the information contained in a large number of original variables into a smaller set of new composite variables or dimensions, at the same time ensuring a minimum loss of information.

Interpretation. PCA can be used to discover important features of a large data set. It often reveals relationships that were previously unsuspected, thereby allowing interpretations of the data that may not ordinarily result from examination of the data. PCA is typically used as an intermediate step in data analysis when the number of input variables is otherwise too large to perform useful analysis.

Origin provides the following outputs for Principal Component Analysis:

• Descriptive Statistics
• Correlation Matrix
• Eigenvalues of the Correlation Matrix
• Extracted Eigenvectors
• Scores for each observation
• Plots
• Scree Plot
• Loading Plot
• BiPlot

2. K-Means Cluster
Cluster analysis is used to construct smaller groups with similar properties from a large set of heterogeneous data. This form of analysis is an effective way to discover relationships within a large number of variables or observations.

Origin provides two cluster analysis methods:

Use K-means clustering to classify observations through K number of clusters. The idea is to minimize the distance between the data and the corresponding cluster centroid. K-means analysis is based on one of the simplest algorithms for solving the cluster problem, and is therefore much faster than hierarchical cluster analysis.

Users should typically consider K-means analysis when the sample size is larger than 100. However, K-means cluster analysis assumes that the user already knows the centroid of the observations, or at least the number of groups to be clustered.

3. Hierarchical Cluster
In this method, elements are grouped into successively larger clusters by some measure of similarity or distance. With the resulting hierarchical tree, or dendrogram, you can decide the quantity and content of the data clusters. Hierarchical cluster analysis is useful for variables or observations, categorical data, or continuous variables. It is, however, more time-consuming than K-means cluster analysis.

4. Discriminant Analysis
Discriminant analysis is used to distinguish distinct sets of observations, and to allocate new observations to previously defined groups. This method is commonly used in biology for classification of animal species, and in medicine for classification of tumor types. It is also used in facial recognition technologies for classifying pixel values, and in the credit and insurance industries for classifying risk.

Discriminant analysis has two main goals:

Discrimination. Construct a classifier to separate the distinct set of observations from all observations in a known population.

Classification. Separate unlabeled observations into labeled groups using a classifier.

For discriminant analysis, Origin provides two different probability settings:
• Equal
• Proportional to group size

Origin provides two methods for computing discriminant functions:
• Linear
• Quadratic