Stat-200 統計套裝軟體-資安軟體/研究分析軟體/心理學軟體/新永資訊有限公司

Stat-200 統計套裝軟體

Stat-200  統計套裝軟體

  • Stat-200 統計套裝軟體
  • 編號
  • 類別
    統計分析軟體
  • 介紹
    Stat-200 is a remarkably comprehensive general statistics package. It incorporates all the descriptive statistics, parametric and non-parametric statistical tests, graphics and data transforms you will need for processing, analyzing and presenting data. Parametric tests and procedures in Stat-200 total 60 and there are 66 non-parametric tests and procedures and 30 different descriptive statistics. ANOVA is comprehensive and includes general N factor analysis. Many unusual procedures are included e.g. survival analysis. All tests have associated information windows which give literature referen
  • 價格

Stat-200  統計套裝軟體

Stat-200 is a remarkably comprehensive general statistics package. It incorporates all the descriptive statistics, parametric and
non-parametric statistical tests, graphics and data transforms you will need for processing, analyzing and presenting data. Parametric tests and procedures in Stat-200 total 60 and there are 66 non-parametric tests and procedures and 30 different descriptive statistics. ANOVA is comprehensive and includes general N factor analysis. Many unusual procedures are included e.g. survival analysis. All tests have associated information windows which give literature references and other facts to assist in getting the right one for the job. Probabilities are calculated directly so there is no need to use tables.

Stat-200 has a state-of-the-art user interface with all its statistical and plotting modules easily selected from the logical tree views. Procedures you use frequently can be placed in a Favorites list for instant access. The workbook is a powerful tool for manipulating and selecting data. It can be any size, limited only by your computer hardware. More than 65 data transforms are supplied and there is a high-level language built-in to enable any transform to be programmed by the user. Data can be keyed-in and also either copied and pasted or dragged and dropped from spreadsheets. In addition, spreadsheet files in many formats can be directly opened. Stat-200 is readily integrated with databases and it supports Recordset tables for ODBC database connections enabling data and results to be easily passed back and forth.

Graph types in Stat-200 include all the usual types for statistical data such as line, pie, bar, scatter and box-whisker charts together with 3D plotting options. A wide variety of line graph styles is available including fits to polynomial, logarithmic, exponential and power functions and plots of moving averages, as well as graphs of raw data, means and data frequencies. In addition, many specialized graph types such as Kaplan-Meier, Weibull, Pareto, Shewart, Polar etc can be plotted automatically. Templates of frequently used graph styles can be created and saved.

Numeric results of tests can be printed direct from Stat-200 or copied to the clipboard or saved for import into other programs. Graphs can be printed or saved and also exported as Windows Bitmaps or Windows Metafiles. Integration with the Web is enhanced by optional output of text results in HTML and graphical results in .JPG and .PNG file formats.

Stat-200's statistical function module is supplied as an OCX which can be embedded in web pages or scripting languages and this gives enormous scope for incorporating statistical analysis as part of any type of project.
Descriptive Stats
  • •  Arithmetic mean
  • •  Standard deviation (n-1)
  • •  Standard deviation (n)
  • •  Standard error
  • •  Skewness
  • •  Kurtosis
  • •  95% confidence limit for mean
  • •  99% confidence limit for mean
  • •  Min value
  • •  Max value
  • •  Sample range
  • •  Number of samples
  • •  Median
  • •  Sx
  • •  Sx2
  • •  Geometric mean
  • •  Variance
  • •  Average deviation
  • •  Angular Descriptive Stats
  • •  Cosine mean
  • •  Sine Mean
  • •  Mean vector length
  • •  Mean angle Cosine
  • •  Mean angle Sine
  • •  Mean angle Tan
  • •  Mean angle
  • •  Circular variance
  • •  Angular variance
  • •  Angular deviation
  • •  Circular standard deviation
Nonparametric Tests
  • •  NxK Chi-squared
  • •  2x2 Chi-squared
  • •  Fisher's exact test
  • •  McNemar's test
  • •  One-sample chi-squared
  • •  Cramer's V
  • •  Contingency coefficient
  • •  Wilcoxon's matched pairs
  • •  Mann-Whitney U-Test
  • •  Friedmann's test
  • •  Kruskal-Wallis test
  • •  Spearman rank correlation
  • •  Kendall rank correlation
  • •  Kendall partial rank correlation
  • •  Kendall coefficient of concordance
  • •  LogRank test
  • •  Mantel-Haenszel test
  • •  Kolmogorov-Smirnov test
  • •  Phi-coefficient for 2x2 tables
  • •  Cramer coefficient C
  • •  Median test
  • •  Extension of the median test
  • •  Robust rank order test
  • •  Siegel-Tukey test for scale differences
  • •  Moses rank-like test for scale differences
  • •  Cochran Q-test
  • •  Jonckheere test for ordered alternatives
  • •  Page test for ordered alternatives
  • •  Chi-square test for k independent samples
  • •  Kendall coefficient of agreement u
  • •  Kappa statistic for nominally scaled data
  • •  Gamma statistic for ordered variables
  • •  Lambda statistic for asymmetrical association
  • •  Somers d for asymmetrical association of ordered variables
  • •  Cox's F-test
  • •  Fisher's cumulant test for normality of a distribution
  • •  F-test for two counts (Poisson distribution)
  • •  Wilcoxon inversion (U) test
  • •  Median test of two populations
  • •  Median test of k populations
  • •  The Siegal-Tukey rank sum dispersion test of two variances
  • •  Steel test for comparing K treatments with a control
  • •  Sequential test for a population mean
  • •  Sequential test for a standard deviation
  • •  Adjacency test for randomness of fluctuations
  • •  Serial correlation test for randomness of fluctuations
  • •  Turning point test for randomness of fluctuations
  • •  The difference sign test for randomness in a sample
  • •  Run-test on successive differences for randomness in a sample
  • •  Run-test for randomness of two related samples
  • •  Run-test for randomness in a sample
  • •  Wilcoxon-Mann-Whitney rank sum test for randomness of signs
  • •  Friedmann's test for multiple treatment of a series of subjects
  • •  Rank correlation test for agreement in multiple judgements
  • •  Test the equality of multinomial distributions
  • •  Bowker test for nominal-scale data
  • •  Lehmacher test for variables with more than 2 categories
  • •  Fisher contingency table test for variables with more than two categories
  • •  Gehan test for censored data
  • •  Fisher-Pitman randomization test for interval-scale data
  • •  Pitman-Welch test for interval scale data
  • •  Wall test for nominal scale data
  • •  Pitman randomization test for interval scale data
  • •  Angular-angular correlation
  • •  Watson U2 test (To test whether two samples from circular observations differ significantly from each other, regarding mean direction or angular variance
  • •  Watson-Williams test (to test whether the mean angles of two independent circular observations differ significantly from each other)
Graphics
  • •  Pie chart
  • •  Bar chart
  • •  Area graph
  • •  Line graph
  • •  Scatter graph
  • •  Box-whisker graph
  • •  3D surface graph
  • •  Bubble charts
  • •  Polar charts
  • •  Radar charts
  • •  Polynomial regression plot
  • •  Pareto chart option in frequency analysis plots
  • •  Kaplan-Meier survival curves
  • •  Density function plots and cumulative probability plots for Gaussian (Normal) distribution, lognormal distribution, Weibull distribution, gamma distribution, Poisson distribution, beta distribution and chi-square distribution
  • •  Regression plots direct from raw data (single factor, single factor repeated measures)
  • •  Polynomial regression plots
  • One-factor response curves and two-factor response surface plots(1st and 2nd order)
  • •  Minimum spanning tree plots for 2 dimensions
  • •  Levey-Jennings/Shewart Charts
  • •  Sequential test for a population mean
  • •  Sequential test for a standard deviation classification
  • •  c-chart
  • •  X-chart
  • •  R-chart
Parametric Tests
  • •  Frequency analysis
  • •  Unpaired t-test
  • •  Paired t-test
  • •  Unequal variance t-test
  • •  Bonferroni t-test
  • •  One-way non-repeated ANOVA
  • •  One-way repeated ANOVA
  • •  Two-way replicated ANOVA
  • •  Two-way repeated ANOVA
  • •  Three-way ANOVA
  • •  Linear regression
  • •  Multiple linear regression
  • •  Compare two observed values
  • •  Compare 2 sample proportions
  • •  Compare sample and population
  • •  Compare paired proportions
  • •  Bartlett's test
  • •  Dunnett's test
  • •  Duncan's test
  • •  Tukey's test
  • •  General N factor ANOVA for multiple fixed effects factors
  • •  2k factorial design for k=2,3
  • •  Durbin-Watson test (residual auto correlation test)
  • •  Single classification ANCOVA for completely randomized design
  • Pearson R
  • •  Repeated measures linear regression
  • •  Backward elimination for multiple linear regression
  • •  Polynomial regression
  • •  Tigonometric regression
  • •  Linear-linear correlation
  • •  Angular-angular correlation
  • •  Angular-linear correlation
  • •  Rayleigh test determine if oberved samples of angular data have a tendency to cluster around a given angle indicating a lack of randomness of the distribution)
  • •  Single Factor analysis of variance for angular data
  • •  Link-Wallace test for multiple com-parisons of k population means
  • •  Hotelling's T-square test for two series of population means
  • •  Dixon test for outliers
  • •  F-test for the overall mean of K subpopulations (ANOVA)
  • •  F-test for multiple comparisons of contrasts between K population means
  • •  F-test for K population means (ANOVA)
  • •  Z-test of a correlation coefficient
  • •  Z-test of 2 correlation coefficients
  • •  t-test of a correlation coefficient
  • •  Hartley's test for equality of K variances
  • •  Fisher cumulant test for normality of a distribution
  • •  F-test for two population variances
  • •  Z-test for correlated proportions
  • •  The w/s test for normality of a population
  • •  Cochran test for variance outliers
  • •  Chi-square test for compatibility of K counts
  • •  Cochran test for consistency in an nxk table of dichotomous data
  • •  Chi-square test for consistency in a 2xk table
  • •  Chi-square test for independence in a pxq table
  • •  Sign test for a median
  • •  Sign test for two medians (paired observations)
  • •  Signed rank test for a mean
  • •  Signed rank test for two mean (paired observations)
  • •  Mardia-Watson-Wheeler test (to test whether two independent random samples from circular observations differ significantly from each other regarding mean angle, angular variance or both)
  • Harrison-Kanji-Gadsden test
Data Transforms
  • •  Polynomial
  • •  Reciprocal
  • •  Natural Log
  • •  Log10
  • •  Log2
  • •  Ln(x+1)
  • •  Exp(x)
  • •  Square Root
  • •  Cube Root
  • •  Xn
  • •  Sin
  • •  Cos
  • •  Tan
  • •  Sort Ascending
  • •  Sort Descending
  • •  Rank Ascending
  • •  Rank Descending
  • •  Modulo
  • •  Absolute Value
  • •  Standardize
  • •  Center
  • •  Multiply columns
  • •  Divide columns
  • •  Add columns
  • •  Subtract columns
  • •  Error function
  • •  Complementary error function
  • •  Logit
  • •  Probit
  • •  Normit
  • •  Normal probabilites
  • •  Student's t probabilities
  • •  F distribution probabilities
  • •  Chi-Squared probabilities
  • •  Transform by a spreadsheet formula
  • •  Matrix operations: inverse matrix, tran•  spose Matrix
  • •  Sinh
  • •  Cosh
  • •  Tanh
  • •  aSin
  • •  aCos
  • •  aTan
  • •  Bessel functions of first and second kind
  • •  Conversions
  • •  Integer ceiling, floor
  • •  Powerful language to program user-d•  efined transforms
Miscellaneous
  • •  Generate uniformly distributed random numbers
  • •  Generate normal randomly distributed numbers
  • •  Generate Poisson randomly distributed numbers
  • •  Generate exponentially distributed numbers
  • •  Generate gamma randomly distributed numbers
  • •  Fill range with arithmetic sequence
  • •  Fill range with geometric sequence
  • •  Fill range with constant
  • •  Kolmogorov-Smirnoff test for goodness of fit (to investigate the difference between an observed distribution and a specified population distribution)
Quality control
  • •  The sequential test for a dichotomous classification
  • •  Quality control acceptance sampling
 
 


 
 

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