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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.
  1. Getting Into and Out of Dataplot
  2. Setting Your Graphics Devices and Printing Graphics
  3. A Simple Example
  4. 4 Typical Problems

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
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)
    Frequency Plot
    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
    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
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
    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

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