[下载]SYSTAT 11.0 Users Guide

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SYSTAT 11.0 User's Guide

NEW FEATURES IN RELEASE 11

Revamped User InterfaceSYSTAT 11 comes with a thoroughly revamped user interface with increased customization of Menu, Spaces, Toolbars, etc. The menu bar has been reorganized to include two new items --- Utilities and Monte Carlo --- and with Statistics menu item renamed as Analysis. SYSTAT 11 provides for import of new data formats like StatView, Stata, Statistica, JMP, MINITAB and S-Plus. Many existing dialog boxes have been reorganized so that additional settings are tabs instead of additional dialog boxes. All mouse functions have keyboard alternatives. Improved Online HelpDialog box items have new, interactive "what's this" help descriptions. New online HTML and context-sensitive help are available. Extensive tool tips are provided. All dialog box input fields show range value in the tool tips. Dialog boxes with limited parameter entries now come with multiple entries; entries can be added or deleted by the user. Individual command files are provided in the SYSTAT directory for over 500 examples in the manual; with simple modifications to suit their data sets, users can run similar analyses effortlessly. Graphics with Improved Quality and Interactivity SYSTAT 11 now makes use of Microsoft's 16M color palette. It provides for more graph customization and improvement. Its new interactive Graph Editor lets you display each individual element name (e.g. Line, Plot, Histogram, X-axis Legend etc.) on moving the mouse over the editor. Interactive aesthetic change (Line style, Fill Style, Font & Color) of each element is now possible while editing by a right-click. Editing text is done by double-clicking the text elements. The coordinate system can be changed, axes can be set, grid-lines can be hidden or shown, interactively. Different formats for legends and changing the formats can be accomplished from the Graph Editor. The axis variable can be changed to produce another graph using the Graph Editor. Automatic and Mouse Interactive animation are available for three-dimensional graphs. GIF, TIFF, PNG & PS files can be exported. Zoom In & Out feature with selection Zoom and Step Zoom and moving in the graph by holding and dragging the graph using mouse are new features. You can set error bars and draw anchor bars interactively. Polyline, Arrow and Circle are new annotation objects from the Graph Editor. Monte Carlo (Including Markov Chain Monte Carlo) SYSTAT 11 offers the Mersenne-Twister random number generator, a powerful random number generator with many desirable properties, to facilitate modern bootstrap and Monte Carlo exercises. SYSTAT 11 now provides more Random Sampling, IID Monte Carlo, and Markov chain Monte Carlo algorithms to generate random samples from many standard distributions, not-so-standard distributions, and indirectly specified distributions. These features help you to accomplish your simulation tasks and give you computational help to solve your analytically intractable Bayesian problems. Quality Analysis SYSTAT 11 provides a comprehensive set of statistical tools to help in all phases of a quality program in an industry --- Definition, Measurement, Analysis, Improvement and Control phases. It provides additional Control Charts and tools like Gauge R & R, Sigma Measurements, Process Capability Analysis, Taguchi's On-Line SPC and Signal-to-Noise Ratio Analysis of Taguchi Loss Functions. Probability Distributions SYSTAT's suite of 13 distributions has been expanded to 33 discrete and continuous, univariate and multivariate distributions. Desired number of random samples of the same desired size can be drawn from these 33 distributions. Probability calculations (density, cumulative distribution, inverse cumulative distribution functions) can be done driven by menu with dynamic dialog and graphs. Fitting of distributions can be accomplished in respect of the 28 univariate distributions with chi-square goodness-of-fit tests and Kolmogorov-Smirnov tests; Shapiro-Wilk normality test can be performed for normal, lognormal and logit normal distribution fitting. New Regression Techniques

• Bayesian Regression provides another paradigm for fitting a multiple linear regression model. The prior distribution for the regression parameters used in this feature is the (multivariate) normal-gamma distribution. Bayes estimates and credible intervals for the regression coefficients are computed. Also, the parameters of the posterior distribution are provided along with plots of prior and posterior densities of the regression coefficients.

• Robust Regression now provides the Least Median of Squares (LMS) regression. Also the Nonlinear Robust Regression procedure has been enhanced with 3 additional weight functions: Ramsay, Andrews, Tukey.

Row Statistics

All the basic statistics and stem-and-leaf plot, including the newly-added P-tiles and N-tiles by seven different methods, are now available for rows as well as for columns. Hypothesis Testing Tests for variances, correlations and proportions are now available. These tests as well as the earlier tests for means, are provided with one-sided alternatives also. Power Analysis Power analysis computations are now available for one-sided alternatives also. Multivariate Analysis The Multivariate Analysis features are now reorganized under one drop-down menu item with the addition of a MANOVA feature incorporating the more-often used test procedures. Matrix Computations All matrix operations and computations can now be performed driven by menu.

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[此贴子已经被作者于2005-4-12 12:15:55编辑过]

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关键词：SYSTAT Guide Users User guid 下载 Guide Users SYSTAT

兄弟！！以后先说明一下具体内容好吗？

是些发表个人观点的文章！！来至www.blogchina.com/ 博客网站。这个网站很不错[em01]

以下是引用whongjiang在2005-1-8 13:52:14的发言：

兄弟！！以后先说明一下具体内容好吗？

就是，好多帖子一点说明都没有～～

Systat 11.0

Overview

Monte Carlo methods (Fishman, 1996; Gentle, 1998; Robert and Casella, 1999) are used to estimate a functional of a distribution function using the generated random samples. SYSTAT provides Random Sampling, IID MC, and MCMC algorithms to generate random samples from the required target distribution.

Random Sampling in SYSTAT enables the user to draw a number of samples, each of a given size, from a distribution chosen from a list of 33 distributions (discrete and continuous, univariate and multivariate) with given parameters.

If no method is known for direct generation of random samples from a given distribution or when the density is not completely specified, then IID Monte Carlo methods may often be suitable. The IID Monte Carlo algorithms in SYSTAT are usable only to generate random samples from univariate continuous distributions. IID Monte Carlo consists of two generic algorithms, viz, Rejection Sampling and Adaptive Rejection Sampling (ARS). In these methods an envelope (proposal) function for the target density is used. The proposal density is such that it is feasible to draw a random sample from it. In Rejection Sampling, the proposal distribution can be selected from SYSTAT’s list of 20 univariate continuous distributions. In ARS, the algorithm itself constructs an envelope (proposal) function. The ARS algorithm is applicable only for log-concave target densities.

A Markov chain Monte Carlo (MCMC) method is used when it is possible to generate an ergodic Markov chain whose stationary distribution is the required target distribution. SYSTAT provides two classes of MCMC algorithms: Metropolis-Hastings (M-H) algorithm and the Gibbs sampling algorithm. With the M-H algorithm, random samples can be generated from univariate distributions. Three types of the Metropolis-Hastings algorithm are available in SYSTAT: Random Walk Metropolis-Hastings algorithm (RWM-H), Independent Metropolis-Hastings algorithm (IndM-H), and a hybrid Metropolis-Hastings algorithm of the two. The choice of the proposal distribution in the Metropolis-Hastings algorithms is restricted to SYSTAT’s list of 20 univariate continuous distributions. The Gibbs Sampling method provided is limited to the situation where full conditional univariate distributions are defined from SYSTAT’s library of univariate distributions. It will be advisable for the user to provide a suitable initial value/distribution for the MCMC algorithms. No convergence diagnostics are provided and it is up to the user to suggest the burn-in period and gap in the MCMC algorithms.

From the generated random samples, estimates of means of user-given functions of the random variable under study can be computed along with their variance estimates, relying on the law of large numbers. A Monte Carlo Integration method can be used in evaluating the expectation of a functional form. SYSTAT provides two Monte Carlo Integration methods: Classical Monte Carlo integration and Importance Sampling procedures.

IID MC and MCMC algorithms of SYSTAT generate random samples from positive functions only. Samples generated by the Random Sampling, IID MC and MCMC algorithms can be saved.

The user has a large role to play in the use of the IID MC and MCMC features of SYSTAT and the success of the computations will depend largely on the user’s judicious inputs.

Estimating Mean and Variance of a Bounded Posterior Density Function Using RWM-H Algorithm and IndM-H Algorithm

---

(i) To generate a random sample using the RWM-H algorithm, the selected proposal is uniform(-0.1, 0.1), which is symmetric around zero with small steps. Since the target function is bounded between 0 and 1, the value generated by the initial distribution should lie between 0 and 1 and thus the initial distribution is chosen as uniform(0,1). For getting samples from the posterior and computing its basic statistics, the input is:

MCMC

MH TARGET='(X^16*(1-X))/((-LOG(1-X))^10)'RANGE B =0,1 /RW, SIZE=100000 NSAMPLE=1 BURNIN=500 GAP=30 RSEED=237465

INITSAMP U(0.0,1.0)

PROPOSAL U(-0.1,0.1)

SAVE MHRWSAMP.SYD

GENERATE

USE MHRWSAMP.SYD

STATS

CBSTAT S1/ MAXIMUM MEAN MINIMUM SD VARIANCE N

DENSITY S1 /KERNEL

The output is:

S1

N of cases 100000

Minimum 0.066

Maximum 0.953

Mean 0.528

Standard Dev 0.136

Variance 0.019

The mean and variance from the simulated data are 0.528 and 0.019 respectively.

(ii) When IndM-H is used, the support of the proposal should contain the support of the target function; hence the selected proposal in this example is uniform(0,1). For generating random samples from the posterior and getting its mean and variance, the input is:

MCMC

MH TARGET='(X^16*(1-X))/((-LOG(1-X))^10)' RANGE B =0,1, /IND SIZE=100000 NSAMPLE=1 BURNIN=500 GAP=30 RSEED=65736736

INITSAMP U(0.0,1.0)

PROPOSAL U(0.0,1.0)

SAVE MHINDSAMP.SYD

GENERATE

USE MHINDSAMP.SYD

STATS

CBSTAT S1/ MAXIMUM MEAN MINIMUM SD VARIANCE N

DENSITY S1 / KERNEL

The output is:

S1

N of cases 100000

Minimum 0.066

Maximum 0.966

Mean 0.527

Standard Dev 0.137

Variance 0.019

The mean and variance of the posterior from simulated data obtained by RWM-H algorithm and IndM-H algorithm are approximately 0.528and 0.018 respectively.

[此贴子已经被作者于2005-2-17 5:25:23编辑过]

Fitting Linear Regression using Gibbs Sampler

---

This example taken from Congdon (2001) illustrates a Bayesian Linear Regression of December rainfall on November rainfall based on data for ten years. The data is from Lee (1997), where Y is December rainfall and X is November rainfall.

The full conditional densities take the form

By taking prior distribution parameters as μ₁=0, μ₂=0, σ₁²=10000 , σ₂²=1000, γ =0.001 and δ=0.001, for getting random samples from the full conditionals and computing basic statistics, the input is:

MCMC

USE RAINFALL

GIBBS /SIZE=10000 NSAMP=1 BURNIN=1000 GAP=1 RSEED=53478

FULLCOND / VAR='ALPHA' DIST=Z, PAR1='((0/(10000))+((SUM(Y))*(TAU)))/((10*(TAU))+(1/10000))', PAR2='SQR(1/((10*(TAU))+(1/10000)))' INIT=29.0

FULLCOND / VAR='BETA' DIST=Z, PAR1='((0/(1000))+((SUM(Y*(X-MEAN(X))))*TAU))/(((SUM((X- MEAN(X))^2))*TAU)+(1/1000))', PAR2='SQR(1/(((SUM((X-MEAN(X))^2))*TAU)+(1/1000)))' INIT=0.5

FULLCOND / VAR='TAU' DIST=G, PAR1='(10/2)+0.001', PAR2='1/(((1/2)*(SUM((Y-ALPHA-(BETA*(X- MEAN(X))))^2)))+0.001)' INIT=0.5

SAVE GIBBSYORKRAIN.SYD

GENERATE

USE GIBBSYORKRAIN.SYD

LET SIGSQ=1/TAU1

STATS

CBSTAT ALPHA1 BETA1 SIGSQ/MAXIMUM MEAN,MEDIAN MINIMUM SD VARIANCE N PTILE=2.5 50 97.5

The output is:

 
   ALPHA1  BETA1  SIGSQ  N of cases  10000  10000  10000  Minimum  7.494  -1.014  47.178  Maximum  67.112  0.700  3651.797  Median  40.694  -0.163  207.885  Mean  40.636  -0.161  257.108  Standard Dev  5.078  0.139  181.699  Variance  25.785  0.019  33014.685Method = CLEVELAND  2.5 %  30.639  -0.440  88.040  50 %  40.694  -0.163  207.885  97.5 %  50.732  0.121  748.459

SERIES

TPLOT ALPHA1

TPLOT BETA1

TPLOT SIGSQ

By posterior predictive simulation, the December rainfall can be predicted based on the new November rainfall 46.1.

The input is:

LET THETANEW= ALPHA1+BETA1*(46.1-57.8)

LET YNEW= ZRN(THETANEW, SQR(SIGSQ))

STATS

CBSTAT YNEW/MAXIMUM MEAN,MEDIAN MINIMUM SD VARIANCE N PTILE=2.5 50 97.5

The prediction of December rainfall is 42.553 with standard deviation 16.861.

[此贴子已经被作者于2005-2-17 5:27:42编辑过]

Systat PeakFit TOPICS Why Should You Use Nonlinear Curve Fitting? PeakFit Offers Sophisticated Data Manipulation Highly Advanced Baseline Subtraction Full Graphical Placement of Peaks and... Publication-Quality Graphs and Data Output PeakFit Saves You Precious Research Time PeakFit Automatically Places Peaks in Three Ways
Why Should You Use Nonlinear Curve Fitting? Nonlinear curve fitting is by far the most accurate way to reduce noise and quantify peaks. Many instruments come with software that only approximates the fitting process by simply integrating the raw data numerically. When there are shouldered or hidden peaks, a lot of noise or a significant background signal, this can lead to the wrong results. (For example, a spectroscopy data set may appear to have a peak with a 'raw' amplitude of 4,000 units -- but may have a shoulder peak that distorts the amplitude by 1,500 units! This would be a significant error.) PeakFit helps you separate overlapping peaks by statistically fitting numerous peak functions to one data set, which can help you find even the most obscure patterns in your data. The background can be fit as a separate polynomial, exponential, logarithmic, hyperbolic or power model. This fitted baseline is then subtracted before peak characterization data (such as areas) is calculated, which gives much more accurate results. And any noise (like you get with electrophoretic gels or Raman spectra) that might bias raw data calculations is filtered simply by the nonlinear curve fitting process. Nonlinear curve fitting is essential for accurate peak analysis and accurate research. PeakFit Offers Sophisticated Data Manipulation
With PeakFit's visual FFT filter, you can inspect your data stream in the Fourier domain and zero higher frequency points -- and see your results immediately in the time-domain. This smoothing technique allows for superb noise reduction while maintaining the integrity of the original data stream. PeakFit also includes an automated FFT method as well as Gaussian convolution, the Savitzky-Golay method and the Loess algorithm for smoothing. AI Experts throughout the smoothing options and other parts of the program automatically help you to set many adjustments. And, PeakFit even has a digital data enhancer, which helps to analyze your sparse data. Only PeakFit offers so many different methods of data manipulation.		Click to View Larger Image
Highly Advanced Baseline Subtraction
PeakFit's non-parametric baseline fitting routine easily removes the complex background of a DNA electrophoresis sample. PeakFit can also subtract eight other built-in baseline equations or it can subtract any baseline you've developed and stored in a file.		Click to View Larger Image
Full Graphical Placement of Peaks
If PeakFit's auto-placement features fail on extremely complicated or noisy data, you can place and fit peaks graphically with only a few mouse clicks. Each placed function has "anchors" that adjust even the most highly complex functions, automatically changing that function's specific numeric parameters. PeakFit's graphical placement options handle even the most complex peaks as smoothly as Gaussians.
Publication-Quality Graphs and Data Output
Every publication-quality graph (see above) was created using PeakFit's built-in graphic engine -- which now includes print preview and extensive file and clipboard export options. The numerical output is customizable so that you see only the content you want.
PeakFit Saves You Precious Research Time
For most data sets, PeakFit does all the work for you. What once took hours now takes minutes – with only a few clicks of the mouse! It’s so easy that novices can learn how to use PeakFit in no time. And if you have extremely complex or noisy data sets, the sophistication and depth of PeakFit’s data manipulation techniques is unequaled.
PeakFit Automatically Places Peaks in Three Ways
PeakFit uses three procedures to automatically place hidden peaks; while each is a strong solution, one method may work better with some data sets than the others. The Residuals procedure initially places peaks by finding local maxima in a smoothed data stream. Hidden peaks are then optionally added where peaks in the residuals occur. The Second Derivative procedure searches for local minima within a smoothed second derivative data stream. These local minima often reveal hidden peaks. The Deconvolution procedure uses a Gaussian response function with a Fourier deconvolution/ filtering algorithm. A successfully deconvolved spec-trum will consist of “sharpened” peaks of equivalent area. The goal is to enhance the hidden peaks so that each represents a local maximum.

[此贴子已经被作者于2005-2-17 5:55:42编辑过]

FEATURES

• General features
• Graphs
• Reporting features
• Smoothing
• Statistics
• Transformations

GENERAL FEATURES

Large, Scientific Worksheets

• Over 32,000 columns by millions of rows
• Handles numeric, text (categorical), and date & time data
• Automatically generate column statistics
• Data sorting
• Rename column and row titles
• Insert color, symbols, line styles and bar patterns
• Insert and delete rows and columns
• Row and column cell clipping and in-place editing
• Independent graphically adjustable row height and column widths
• Missing data handling
• Data point sampling
• Multiple worksheets per session
• Graphical feedback of current curve and datapoint
• Text support of up to 256 characters
• Change the font type and grid colors
• Zoom in and zoom out
• Change font for worksheet
• Multiple Undo
• Format empty cells - formatted selected columns even if they do not contain data
• More flexible column titles allow for duplicates and numeric only titles
• Enhanced data/time recognition and more formats
• Arrow-key functionality is similar to that of Microsoft Excel
• Freeze Panes
• Multi-line editing - text wraps to fit the column while the row height automatically adjusts
• Print preview
• Find and replace data from among millions of rows and columns
• Format text based cells with independent fonts
• Multiple sorting

Microsoft Office Integration

• You can open Excel spreadsheets directly inside SigmaPlot, allowing you to use the many features Excel offers. Use in-cell formulas and other Excel data analysis tools on your data.
• One-click access to directly launch SigmaPlot from Microsoft Excel

SigmaStat 3.1 Integration

• SigmaStat's statistical capabilities are directly accessible in SigmaPlot through the statistics menu.
• Over 30 of the most frequently used statistical tests to analyze scientific research
• Advisor Wizard guides you through the process of choosing the appropriate statistical test
• Report generation that translates the statistics into plain and simple English
• Descriptive statistics
• Non-parametric tests: t-tests, ANOVA
• One-way, two-way, three-way ANOVA
• Repeated measures
• Rates and proportions
• Correlation
• Survival analysis (Kaplan-Meir)
• Power and sampe size analysis

Symbol Types

• Over 80 symbol types
• 30 new symbol types that include half-filled and BMW styles
• Edit font when using text as symbol
• Access new symbols directly from graph properties dialog, toolbar, legend page, and the symbol dialog box
• More line types such as dash and gap patterns
• More fill patterns, for bar charts and area plots, that can be independently set from the line color

"Picking from Column" Option

• Enter colors, patterns, symbols, line styles, tick mark intervals, tick labels and more directly into your worksheet to customize your graph the way you want. Transforms and "picking from column" allow you to create data dependent color gradients, symbols and sizes.

SigmaPlot Notebook

• Can hold SigmaPlot worksheets, Excel worksheets, reports, documents, regression wizard equations, graph pages, and macros.
• New dialog-bar-based notebook that has several states: docks, resizable, hide-able, summary information mode, etc.
• Browser-like notebook functionality that supports drag-n-drop capabilities
• Direct-editing of notebook summary information

Import

• Axon Binary, Axon Text, ASCII Plain, Comma and general import filter, 1-2-3T, Symphony T, Quattro T, Excel, dBASE E, DIF, SigmaPlot for DOS 4.0, 4.1, 5.0, SigmaPlot 1.0, 2.0, and 3.0, 4.0, 5.0 for Windows, SigmaPlot 4.1 and 5.0 for Macintosh data worksheets, SYSTAT, SigmaScan Pro, SigmaScan, SigmaScan Image, Mocha
• Import and ODBC compliant database
• Run SQL queries on tables and selectively import information

Export

• ASCII Text, Tabbed, Comma, 1-2-3T, Excel, DIF, SigmaPlot 1.0, 2.0, and 3.0 for Windows, SigmaPlot 5.0 for Macintosh data worksheets, SigmaScan Pro
• PDF and HTML export of graphs and reports

Export Graphs Options

• Export an individual graph, a group of graphs and objects, or an entire page
• Different levels of resolution and color depths: EPS, TIFF, JPEG, WMF, BMP
• True color EPS vector
• Compressed CMYK TIFF
• Publication Help: guides user through the complexities of selecting the correct DPI, image size, file export format, etc.
• True CMYK EPS export

Publish as Web Page

• Export graphs as high-resolution Web objects
• WebViewer: free browser plug-in to view data used to create graph or print, pan and zoom in on graph without losing resolution
• The WebViewer supports IE 4.01 or higher. A screen-resolution JPEG file is automatically displayed for other browser applications and operating systems.

Automate Routine and Complex Tasks

• Visual Basic compatible programming using built-in macro language interface
• Macro recorder to save and play-back operations
• Full automation object support - use Visual Basic to create your own SigmaPlot-based applications
• Run built-in macros or create and add your own scripts
• Add menu commands and create dialog boxes
• Toolbox menu: helpful macros appear as a separate menu item
• Export graph to PowerPoint Slide (macro)
• New 'Insert Graph to Microsoft Word' Toolbox macro
• New keyboard shortcuts in the Graph Properties and most Microsoft Excel keyboard shortcuts in the worksheet

Windows Application

• Excel, Word and PowerPoint for Office 2000 and Windows 2000 support ToolTips
• Tips and Tricks at startup
• Full 32-bit implementation
• OLE 2 container and server
• Use Excel worksheets inside SigmaPlot
• Uninstaller
• Controls have bitmaps to give feedback about selections
• Right mouse button property editing
• Selection of objects on graph page
• Full precision and date/time Microsoft Excel copy and paste