Origin statistics software

Each legend entry refers to a box plot component: the box, whiskers, median or mean lines, percentiles, etc. In addition, the Legend Properties dialog lets you add custom entries to your legend for symbols and lines, by building the desired syntax using a dialog. The Piper diagram or Trilinear diagram is used to plot chemistry of water samples for hydro-geological studies.

Scatter point shape and color change for each sample. Sample ID is displayed next to each point and its color is matched to the sample point. A colorblind-safe color list is used Origin includes two such built-in color lists. A bubble scale relates bubble size to total dissolved solids.

A point-by-point legend identifies each sample point. A scatter plot with modifiers for color and size, set using other data columns. Note the nested bubble scale legend at bottom left.

The map of the continental USA was added to the graph using the Insert: Continental USA Map menu entry The menu entry will be shown when the scale matches the range of the map's latitude and longitude. Worksheet column label row cells can display and store LaTeX strings.

These strings can then be easily added to graph text objects, such as graph axis titles and legends. Editing the Objects. The OpenGL graphic technology brought unltrafast performance and more flexibility to customize your 3D graphs. This graph displays the topology of Mount St.

Helens before and after the volcanic eruption in The data are plotted as two color map surface plots in the same OpenGL graph layer, with the top surface offset along the Z axis. A light source has been added to illuminate the surface. Isometric scaling has been applied so that the X, Y and Z axis lengths reflect true relative distances. This graph displays the population of different counties by fetching map data from a new WMS server and then plotting 3D bars on top of the map plane.

The Maps Online app offers a few built-in map data sources for users to choose from. You can also add your own map source. With Origin, it is very easy to place 3D bars on a map using the Layer Contents dialog. This graph displays a 3D color map surface plot of Lake Ontario region. A second dataset has been added as a transparent plane at the Z value corresponding to the water level. Origin supports free rotation of OpenGL graphs by simply holding down the R key and using the mouse.

Additional options for rotating, resizing, stretching and skewing are available when the 3D graph layer is selected. This plot shows a 3D scatter plot with x, y, z errors, and projections on three axis planes. The 3D scatter symbol is colormapped to another data column population density. Symbols and error bars in each projection can be customized independently. This is a 3D Colormap Surface with Projection , made possible by plotting the data twice: once as a 3D surface plot and a second time as a flat surface.

The flat surface can be offset arbitrarily in the Z direction. Using a transparent surface and drop lines to surface instead of the bottom plane, you can show distances between the points and the surface. These toolbars are sensitive to the type of object selected.

The buttons in the pop up provide access to all of the common customization options, so you can perform quick changes without opening complex dialogs. Import speed in Origin is a factor of 10 or more compared to Excel , and compared to older versions of Origin. The gain in speed has been achieved by making full use of the processor's multi-core architecture. You can drag-and-drop data files onto the Origin interface to import them.

Drag-and-drop is supported for most common file types, and can be further customized for additional or custom file types. Starting with Origin , you no longer need to have MS Excel installed to import these file types.

Origin provides the following options for Excel file import:. Excel workbooks can also be opened directly within Origin. The Excel file can be saved with file path relative to the Origin Project file, for easy sharing of the project along with related Excel files. We recommend importing your Excel data, so that you have full access to Origin's powerful graphing and analysis environment.

Origin supports importing data from a database using Database Connector. Options include:. Origin supports importing from a database , and then saving the query in the worksheet for easy editing and re-importing. The Digitizer tool in Origin allows you to perform manual or semi-automated digitizing of graph images. Features include:. The Digitizer tool in Origin lets you generate data from images of graphs.

Cartesian, ternary, and polar coordinates are supported. Digitizing methods include manual or semi-automated operations. Multiple curves can be digitized, and points can be reordered and visualized in the result graphing and data worksheet. In a Zoom graph, a zoomed portion of a larger graph is added to explore a region of interest.

Moving the cyan rectangle updates that portion of the graph shown by the inset. Press the Z or X keys and scroll the mouse wheel to quickly and interactively zoom and pan data in graph layers. The new Data Slicer feature allows you to change filter conditions directly on a graph for easy data exploration.

Simply set up filters on desired worksheet columns, create a graph with one or more layers, and turn on the Data Slicer panel to control the filters. Features include: Mini Toolbar to toggle Data Slicer panel Directly disable or enable filters from the graph Text filter has option for single entry allowing for easy switch of filter conditon Numeric filters allow several conditions including combinations with AND or OR. Highlight a particular data plot in a graph.

Also works with complex graph types such as Parallel plot. You can customize the display to include information from other columns of the worksheet, including images embedded in worksheet cells. Use Vertical Cursor for exploring data in stacked graphs in multiple graph windows simultaneously.

You can find information of one cursor or compare two cursors such as the distance. Origin and OriginPro provide a rich set of tools for performing exploratory and advanced analysis of your data. Please view the following sections for details. Origin provides several gadgets to perform exploratory analysis by interacting with data plotted in a graph. Origin provides a selection of Gadgets to perform exploratory analysis of data from a graph. A region of interest ROI control allows you to interactively specify the subset of data to be analyzed.

Results from the analysis are dynamically updated on top of the ROI as it is resized or moved. This image shows peak fitting being performed using the Quick Fit gadget. Two statistics gadgets are applied to this graph to report statistics in two regions of interest ROI. The Y axis is moved to separate the two regions. Yellow ROI boxes are hidden so that they do not show in printouts. The "S" button on upper-right corner re-displays the ROI boxes when clicked.

Use the Quick Peak Gadget to interactively perform peak finding, baseline subtraction, and peak integration of data from a graph. The Quick Fit Gadget lets you perform linear, polynomial, or nonlinear curve fitting on data plots in a graph. Notice the label on top of the ROI displaying the slope and Pearson's r from a linear fit.

The label updates dynamically as the ROI is moved or resized. The ROI can be rectangular, elliptical, polygon or arbitrary hand-draw shape. The tool provides statistics on data inside and outside the ROI, and also lets you copy, clear, mask or delete selected data. Origin provides various tools for linear, polynomial and nonlinear curve and surface fitting. Fitting routines use state-of-the-art algorithms. The sections below provide a summary of key features.

Graph displaying result of linear regression. Graph displaying result of polynomial regression. Origin supports Global Fitting with Parameter Sharing , where you can simultaneously fit multiple datasets with the same function and optionally share one or more fitting parameters across all datasets.

The report sheet will provide a summary table with all parameter values and errors, and a single set of fit statistics from the global fit. OriginPro supports fitting with implicit functions using the Orthogonal Distance Regression algorithm which minimizes the orthogonal distance from data to the fit curve. Errors and weighting for both X and Y data are supported. Implicit functions can have two or more variables. Result of an Apparent Linear Fit on data plotted with logarithmic Y axis scale.

The latter supports weights for both X and Y data. Select from over 12 weighting methods including instrumental, statistical, direct, arbitrary dataset, and variance. When working with replicate data, Origin can perform a Concatenated Fit where the replicates are combined internally to a single dataset.

The graph included in the report sheet can either represent the data in replicate form, or as mean values with SD or SE error bars. A Quick Sigmoidal Fit Gadget is also available.

The Rank Models tool in OriginPro can fit and rank multiple functions to a dataset. Use OriginPro to perform nonlinear surface fitting of data organized in XYZ worksheet columns , a matrix , or a virtual matrix. Select from over 20 surface functions or create your own function. For peak functions, find peaks using local maximum, partial derivative, or contour consolidation. The raw data is plotted as a color-filled contour plot, and the fit results are plotted as contour lines.

Do you need to fit an implicit function to your data? Implicit Fitting uses the Orthogonal Distance Regression algorithm to find optimal values for the fit parameters. Errors or weights are supported for both X and Y data.

Origin provides several features for peak analysis, from baseline correction to peak finding, peak integration, peak deconvolution and fitting. The following sections list the key features for peak analysis. This is a preview graph for performing peak integration using the Peak Analyzer tool. The integration range can be applied for all peaks, or modified individually and interactively for each peak.

Once you have performed baseline detection and peak finding, Origin provides several options for peak fitting: Select from over 25 built-in peak functions, or create your own peak function Fit all peaks with same function form, or assign different functions to specific peaks Peak deconvolution to resolve overlapping or hidden peaks Fix peak centers or allow them to vary by a set percentage or within a set range of values Specify bounds and constraints on peak parameters Share parameters across peaks Full control of fitting process including step-by-step iterations Detailed report including fit statistics, residuals, and graph of individual and cumulative fit lines Over 25 peak properties for reporting, including peak area by percentage, variance, skewness and peak excess Fit summary graph with customizable peak properties table.

There are several options for batch peak analysis of multiple datasets in Origin: Use integration and peak gadgets to analyze multiple curves in a graph within or across layers Use a predefined peak analysis theme to analyze multiple datasets or files Output a custom report table with peak parameters from each dataset or file.

The Quick Peaks Gadget provides a quick and interactive way to perform peak analysis from a graph, using a region of interest ROI control. Batch operations such as integration of multiple curves over a desired range are also possible from this gadget. The Peak Analyzer tool in Origin supports baseline detection, peak picking, and peak integration. In OriginPro, this tool also supports fitting multiple peaks.

Peak detection methods include 2nd derivative search to detect overlapping or hidden peaks. The interface guides you step-by-step, allowing you to customize settings at each stage, and then save the settings as a theme for repeat use on similar data. In addition, Origin provides Stats Advisor App which helps user to interactively choose the appropriate statistical test, analysis tool or App. The Stats Advisor App asks a series of questions and then suggests the appropriate tool or App to analyze your data.

The graph shows a Custom Report of numerical and graphical results from multiple statistical tools, created from Origin's flexible worksheet. Once created, such reports can be automatically generated, greatly simplifying your statistical analysis tasks.

The image shows two of the embedded graphs opened for further editing. Edit an embedded graph by double-clicking on the thumbnail image in the report.

Once customizations are made, put the graphs back into the report and see your modifications. Dendrogram of spectra classification from Hierarchical Cluster Analysis of spectra. This plot can be used to classify observations across groups. This graph displays survival functions with confidence intervals, created by the Survival Analysis tool in OriginPro. The tool also performs a log-rank test to compare the two survival functions. A preview panel is provided to enable real-time visualization of specified parameters and corresponding results.

OriginPro provides several wavelet transform tools. From simple column calculations to interpolation, calculus and integration, Origin provides a wide range of tools for mathematical analysis of worksheet and matrix data. Use the Normalize tool to normalize data in a worksheet or a graph. The F x Column Formula row in Origin worksheet lets you directly type expressions to calculate column values based on data in other columns and metadata elements.

The expression can be further edited in the Set Values dialog which provides a lower panel to execute Before Formula scripts for pre-processing data. The Set Values dialog also provides a search button to quickly find and insert functions from over built-in functions. User-defined functions can also be added for custom transforms.

Auto complete helps to quickly find and enter functions as well as name ranges to complete your formula. Use Origin's Interpolation Gadget to perform interpolation and extrapolation on one or more data plots in a graph. You can interactively select the data range using a region-of-interest ROI control. Interpolation methods include linear, spline and Akima spline. Use the Integrate Gadget to perform integration of data curves in a graph.

A region-of-interest ROI control is provided to interactively select the desired data range. Baseline methods include selecting an existing data plot as a baseline to determine the area between two curves, as displayed in this graph. Batch integration of multiple curves is also supported. Origin provides multiple powerful data manipulation tools which can be used for pre-analysis data processing. The pre-analysis data processing can be carried out right after importing data into Origin, and help to get the data into a desired form for analysis in a quick and intuitive way.

Origin provides several tools for reorganizing your data, such as stacking and unstacking columns, and splitting or appending worksheets. With the Stack Columns tool displayed here, you can specify a row label such as Long Name or Comments to act as group identifier.

The tool also provides options for stacking into subgroups or stacking by rows. The Data Filter feature in Origin lets you specify numeric, string, or date-time filters on one or more worksheet columns to quickly reduce data. Custom filter conditions are also supported. Hidden rows are excluded from graphing and analysis. Import and reduce NetCDF file by partial import or averaging during import Matrix Image Stack to support Shapefile-based ROI averaging to create time axis profile Matrix stats, subtraction, simple math, linear fit along time axis Browser Graph for contour and image plots from matrix stack.

Origin provides many options for exporting and presentation, from sending graphs to PowerPoint, to creating movies. Journals typically require a specific width for the graph image, such as 86 mm for single column and mm for double column. In addition, at the scaled size, they may require text labels to be above a particular font size, and lines to be above a certain thickness.

In this version, we offer the following key features for preparing the Origin graph with the exact width specification: Resize graph page by specifying desired width, while maintaining aspect ratio auto scale height when width is changed Scale all elements on the page when resizing in order to maintain proportional balance in the graph Conversely, set element scale to some fixed factor when you want to maintain absolute size of elements Fit all graph layers to the available page area using user-specified margins, while maintaining layer relationships, relative size, and object scale.

Once the graph has been scaled to the desired width then it can be exported in a vector or raster format for submission to the journal. Specify desired width and units to match requirements of the journal. The page height will be proportionally scaled while maintaining aspect ratio. Relative dimensions of all objects in graph will be maintained. Reduce white space in your graph page by either expanding all layers to occupy available space Fit Layers to Page or by reducing page size Fit Page to Layers.

In the GIF, we used the Master Page feature to add a company logo and date stamp of identical style and position in graphs. You can send graphs individually by name, by Project Explorer folder, or send all graphs from the entire project.

Options include specifying slide margins and using a pre-existing slide as a template, allowing you to add a common set of elements to your published slides. A Send Graphs to Word App , available from the OriginLab File Exchange , exports your Origin graphs as embedded objects or pictures and inserts them into a Word document, with the option to insert them at specific bookmarked positions.

Then paste to other applications such as Microsoft word and edit further. Worksheet cells can also be copied as EMF. Use the Video Builder tool in Origin to create a video file from Origin graphs. Manually or programmatically add frames to the video from any graph in your project.

This animation displays the evolution of data values mapped onto a 3D surface. Origin's Layout Page can be used to arrange multiple graphs, text, equations, and images. The layout page window acts as a "display panel" for worksheets and graphs which are created and edited in separate child windows to create custom presentation.

This multi-page custom report was created by researchers at a sports institute , to review progress in athlete training regimens. Custom reports can be part of an Analysis Template , allowing you to generate a new report automatically when new data are imported. This multi-page custom report was created by a quantitative analyst at a financial institution , as part of a large-scale analysis workflow. Custom reports can be part of an Analysis Template , and can be exported as PDF files for publication.

These reports can be automatically updated when new data are imported. Origin provides multiple ways to handle repetitive graphing, importing and data analysis tasks. Batch operations can be performed directly from the GUI, without the need for any programming. Smart Plotting with Cloneable Templates. As an alternative to Graph Templates, Graph Themes provide a means to save graph customizations and apply them to different types of graphs across your projects.

The Template Library helps you organize and utilize Graph Templates you have created. Graph Templates are a great way to apply the customizations you have made to one graph, to additional graphs you make from similar data. Starting from Origin b, Origin provides a set of extended graph templates in the template library. Graph Template Library dialog shown in List View mode. Set up desired graphs and analysis operations on data in the current workbook.

Set the operations to automatically update. Then simply import multiple files, having Origin clone the current workbook for each file. All graphs and analysis results in the new books will be updated based on the data from each file. Origin provides a quick yet powerful way to allow users to perform batch graphing and analysis when importing multiple files. Origin supports automatic or manual recalculation of results from most analysis and data processing operations, which is the fundation of batch processing and automation.

The Batch Processing tool in Origin lets you process multiple data files or datasets using an Analysis Template. The template can include a summary sheet for collecting relevant results for each file in a summary table. The analysis template can also be linked to a Microsoft Word template using bookmarks, to create custom multi-page Word or PDF reports for each data file. The Correlation Coefficient tool can help to judge the strength of the relationship between pairs of variables. An outlier is an observation that is dramatically distant from the rest of the data.

Origin provides two tools to help detecting the outliers. The outliers plot in the tools can help user to visually judge how the outlier is distant from other observations. In addition to determining that differences exist among the means, ANOVA tools in Origin provide multiple means comparisons in order to identify which particular means are different. Two way ANOVA is an appropriate method to analyze the main effects of and interactions between two factors.

Three-way ANOVA ests for interaction effects between three independent variables on a continuous dependent variable i. They are help to visually compare multiple groups, determine whether their means are different.

The image displays results got in the one-way anova tool. There are also expandable Homogeneity of Variance Test and Means Comparisons table in result which helps to judge whether the groups have equal variance and provides pair-wise comparison.

The repeated measures design is also known as a within-subject design. It has the same subjects performed under every condition. The repeated measures ANOVA is used for comparing three or more means when all subjects are measured under a number of different conditions. The mean comparison tests in ANOVA, also known as Post Hoc tests, are useful to perform additional comparisons of subsets of the means.

The image shows two of the embedded graphs opened for further editing. Edit an embedded graph by double-clicking on the thumbnail image in the report. Once customizations are made, put the graphs back into the report and see your modifications. Parametric Hypothesis tests are frequently used to measure the quality of sample parameters or to test whether estimates on a given parameter are equal for two samples. Origin supports different input mode for hypothesis testing.

User don't need to transform their data before using the tools. The example shows the results of two-sample t-test , a footnote is provided in the table s to help draw conclusions. Origin also support Welch's test for the case that variance is not equal.

The example shows dialog and results of two sample t-test on rows perform on gene data. The t-tests on rows tools in Origin enable user to compare data store in rows. Nonparametric tests are useful for testing whether group means or medians are distributed the same across groups. In these types of tests, we rank or place in order each observation from our data set.

Nonparametric tests are widely used when you do not know whether your data follows normal distribution, or you have confirmed that your data do not follow normal distribution. Meanwhile, hypothesis tests are parametric tests based on the assumption that the population follows a normal distribution with a set of parameters.

The test determines whether the median of the sample is equal to some specified value. Data should be distributed symmetrically about the median. In resuts, a footnote is provided in the table s to help to draw conclusions.

Friedman ANOVA can be used to compare dependent samples or observations that are repeated on the same subjects. Thus, the test is well-suited to randomized block designs. The tool in Origin can be used to compare three or more related samples. Multivariate analysis is a set of techniques used to analyze data that corresponds to more than one variable. The main objective of this analysis is to study how the variables are related to one another, and how they work in combination to distinguish between multiple cases of observations.

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. 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.

Hierarchical PRO. In this method, elements are grouped into successively larger clusters by some measures of similarity or distance. K-means PRO. It is faster than Hierarchical but need user know the centroid of the observations, or at least the number of groups to be clustered.

Discriminant analysis is used to distinguish distinct sets of observations, and to allocate new observations to previously defined groups. Partial Least Squares regression PLS is used for constructing predictive models when there are many highly collinear factors.

The Partial Least Squares in Origin is used for constructing predictive models when there are many highly collinear factors. The Variable Importance Plot can help to judge the importance of each variable. The Principal Component Analysis PCA tool is used to explain the variance-covariance structure of a set of variables through linear combinations.

The scree plot is a useful visual aid for determining an appropriate number of principal components. And the Loading and Score plot can be used for interpreting relations among observations and variables. A Dendrogram plot created by the Hierarchical Cluster Analysis tool, which can be used to list all samples and indicates at what level of similarity any two clusters were joined.

This plot can be used to classify observations across groups. Survival Analysis is widely used in the biosciences to quantify survivorship in a population under study. The graph displays the survival function plot in Kaplan-Meier Estimator. A log-rank test is perform to compare the two survival function.

The image displays a part of reports of the Cox Proportional Hazard Regression , which is a semi-parameter method to forecast changes in the hazard rate along with a variety of fixed covariates. The Weibull Fit is a parameter method to analyze the relationship between the survival function and the failure time. User can see the parameter estimation of the Weibull model from the result table and visually decide whether the data are drop from Weibull distribution from the Weibull Probability Plot.

Kaplan-Meier Estimator, a non-parametric estimator, uses product-limit methods to estimate the survival function from lifetime data. In addition to estimating the survival functions, Kaplan-Meier Estimator in Origin provides three other methods to compare the survival function between two samples:.

The proportional hazards model, also called Cox model, is a classical semi-parameter method. It relates the time of an event, usually death or failure, to a number of explanatory variables known as covariates. Weibull fit is a parameter method to analyze the relationship between the survival function and the failure time.

We suppose that the survival function follows a Weibull distribution and fit the model with a maximum likelihood estimation.

Power and Sample Size analysis is useful for researchers to design their experiments. It can compute the power of the experiment for a given sample size, and can also compute the required sample size for given power values. Power and Sample Size Analysis includes both sample size analysis and power analysis. The sample size analysis is used to determine whether an experiment is likely to yield useful information with a given sample size, Conversely, power analysis can be useful in determining the minimum sample size needed to produce a statistically significant experiment.