Getting Started with DATAPLOT

Introduction

Dataplot has a wide range of capabilities. As with many large programs, the most difficult step can simply be getting started.

The first step is to know how to initiate Dataplot on your local platform. If you performed the Dataplot installation yourself, you should already know the answer to this. If you did not perform the installation and you do not know how to initiate Dataplot, check with your local system administrator (i.e., the person who performed the local installation).

Many of the entries on the Dataplot web page serve as an online Dataplot tutorial.

Command Line Version or GUI Version

The first issue to be aware of is that Dataplot can be run either in a traditional command line mode or with a graphical user interface (GUI).

I recommend keeping both the command line and GUI version available. The command line version can be useful for automating routine tasks and it can also be faster to use once you know the basic Dataplot commands. The GUI is useful for learning about Dataplot's capabilities. New users and those who use Dataplot infrequently may be more comfortable using the GUI version.

This getting started section is oriented towards the command line version. See the GUI web entry for guidance on getting started with the GUI.

Perform a Simple Analysis with Dataplot

A good way to get started with Dataplot is to learn how to get in, perform an analysis that can be done with a few Dataplot commands, and print the results.

Once you run through these simple examples, you may want to browse through some of the other web pages. For example, the io and Data Types entries are a good start for learning how to get your data into Dataplot.

The tables below provide links to documentation for some of the more commonly used Dataplot commands.

Index to Some Commonly Used Plotting Commands

Plotting
X-Y Plots: Line Plot Character Plot Scatter Plot Bar Plot Error Bar Plot Vector Plot	Distributional Plots: 4-Plot (sequence plot, lag plot, histogram, normal probability plot) Histogram Bi-Histogram Frequency Plot Rootogram Stem and Leaf Plot Pie Chart Probability Plot Probability Plot Correlation Coefficient Plot Normal Plot Box-Cox Homogeneity Plot Box-Cox Linearity Plot Box-Cox Normality Plot Percent Point Plot Quantile-Quantile Plot Symmetry Plot	Time Series: Run Sequence Plot Lag Plot Auto-, Cross-, Partial Correlation Plot Spectral Plot Complex Demodulation Plot Periodogram Allan Variance Plot Allan Standard Deviation Plot
3-D Plots: 3-D Plot Contour Plot	Multivariate Plots: Andrews Plot Parallel Coordinates Plot Profile Plot Star Plot Symbol Plot Biplot	Design of Experiments and Analysis of Variance Plots Block Plot Box Plot DEX Scatter Plot DEX Mean Plot DEX SD Plot DEX Range Plot DEX Effects Plot
Statistics Plots: Mean Plot Standard Deviation Plot Statistic Plot Bootstrap Plot Jacknife Plot Homoscedasticity Plot Phase Plot Fractal Plot Pareto Plot Analysis of Proportions Plot	Quality Control Plots: XBAR Control Chart S Control Chart R Control Chart C Control Chart U Control Chart P Control Chart NP Control Chart	Reliability, Extreme Value Plots: Tail Area Plot Weibull Plot CME Plot

Index to Some Commonly Used Data Analysis Commands

Data Analysis
Data and Function Transformations: Define variables, and parameters, transform data User Defined Functions Statistical Calculations Mathematical Calculations (interpolation, derivatives, integrals, and more) Matrix Calculations Built-In Functions	Statistical Summaries and Tests: Summary Statistics Runs Analysis Tabulate counts, means, standard deviations, and ranges of grouped data Tabulate counts, means, standard deviations, ranges, or compute the chi-square test of independence for data grouped by 2 variables Confidence Limits for the mean 1- or 2-sample t-test 2-sample F-test 1-sample chi-square test for standard deviations k-sample Bartlett test for homogeneity of variances	Fitting (Regression) and Smoothing: Linear Fitting (Regression) Non-Linear Fitting Exact Rational Fitting Locally Weighted Least Squares Smoothing Weighted Least Squares Fitting Robust Fitting (Iteratively Re-weighted Least Squares, Least Absolute Deviations) Spline Fit Smoothing (Least Squares, Robust)
Experiment Design and Analysis of Variance: Analysis of Variance Median Polish (robust ANOVA) Yates Analysis Principle Hessian Directions for a Yates Analysis	Time Series: Discrete Fast Fourier Transform	Multivariate: Variance-Covariance Matrix Correlation Matrix Principle Components Singular Value Factorization Other Matrix Operations Fisher's Discriminant Analysis Canonical Correlation Analysis

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Date created: 06/05/2001
Last updated: 09/20/2016
Please email comments on this WWW page to alan.heckert@nist.gov.