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  GraphPad Prism - 基础生物统计学(biostatistics),曲线拟合和科学制图软件

专为科学研究而设计的首选的分析和绘图解决方案。 和世界领先的科学家一起,探索如何使用Prism来节省时间,做出更合适的分析选择,并优雅地绘制和展示您的科学研究。

综合分析和强大的统计,简化

有效地组织您的数据

与电子表格或其他科学图形程序不同,Prism有八种不同类型的数据表,专门为您要运行的分析而格式化。这样可以更轻松地正确输入数据,选择合适的分析并创建令人惊叹的图形。

Organize Your Data Effectively

执行合适的分析

避免统计术语。 在清晰的语言中,Prism提供了广泛的分析库,从常见到高度特定的非线性回归,t检验,非参数比较,单,双和三因子方差分析,列联表,生存分析等等。 每个分析都有一个清单,以帮助您了解所需的统计假设,并确认您已选择适当的检验。

Perform The Right Analysis

随时随地获得可操作的帮助

降低统计数据的复杂性。 Prism的在线帮助超出了您的预期。 几乎每一步,都可以从在线Prism Guides访问数千页。 浏览图表产品组合,了解如何制作各种图表类型。 教程数据集还可帮助您了解执行某些分析的原因以及如何解释结果。

Get Actionable Help As You Go

 

更智能的工作,不是更难

一键式回归分析

没有其他程序像Prism那样简化曲线拟合。 选择一个方程式,Prism执行其余的曲线拟合处理,显示结果和函数参数表,在图形上绘制曲线,并插入未知值。

One-Click Regression Analysis

专注于您的研究,而不是您的软件

无需编码。 图表和结果会实时自动更新。 对数据和分析的任何更改 - 添加遗漏数据,省略错误数据,更正拼写错误或更改分析选择 - 都会立即反映在结果,图表和布局中。

Focus on Your Research, Not Your Software

无需编程即可自动完成工作

减少分析和绘制一组实验的繁琐步骤。 通过创建模板,复制系列或克隆图表可以轻松复制您的工作,从而节省您数小时的设置时间。 使用Prism Magic一键单击,对一组图形应用一致的外观。

Automate Your Work Without Programming

优雅地绘制和分享您的工作的最快方式

无数种自定义图表的方法

专注于数据中的故事,而不是操纵您的软件。 Prism可以轻松创建所需的图形。 选择图形类型,并自定义任何部分 - 数据的排列方式,数据点的样式,标签,字体,颜色等等。 定制选项是无止境的。

Countless Ways to Customize Your Graphs

一键导出出版级质量的图形

减少发布时间。 Prism允许您自定义导出(文件类型,分辨率,透明度,尺寸,颜色空间RGB / CMYK)以满足期刊的要求。 设置默认值以节省时间。

Export Publication-Quality Graphs With One Click

加强协作

不仅仅是分享图表。 Prism对您数据的全面记录可以与其他科学家进行有效的合作。 Prism项目的所有部分(原始数据,分析,结果,图形和布局)都包含在一个文件中,您只需单击一下即可共享。 现在,其他人可以在每一步都轻松地完成您的工作,提高您的发现的清晰度并简化您的协作工作。

Enhance Collaboration

GraphPad Prism 8统计特征

Discover the Breadth of Statistical Features Available in Prism 8
Statistical Comparisons
  • Paired or unpaired t tests. Reports P values and confidence intervals.
  • Automatically generate volcano plot (difference vs. P value) from multiple t test analysis.
  • Nonparametric Mann-Whitney test, including confidence interval of difference of medians.
  • Kolmogorov-Smirnov test to compare two groups.
  • Wilcoxon test with confidence interval of median.
  • Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further.
  • Ordinary or repeated measures ANOVA followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni or Holm-Sidak multiple comparison tests, the post-test for trend, or Fisher’s Least Significant tests.
  • One-way ANOVA without assuming populations with equal standard deviations using Brown-Forsythe and Welch ANOVA, followed by appropriate comparisons tests (Games-Howell, Tamhane T2, Dunnett T3)
  • Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.
  • Greenhouse-Geisser correction so repeated measures one-, two-, and three-way ANOVA do not have to assume sphericity. When this is chosen, multiple comparison tests also do not assume sphericity.
  • Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn's post test.
  • Fisher's exact test or the chi-square test. Calculate the relative risk and odds ratio with confidence intervals.
  • Two-way ANOVA, even with missing values with some post tests.
  • Two-way ANOVA, with repeated measures in one or both factors. Tukey, Newman-Keuls, Dunnett, Bonferroni, Holm-Sidak, or Fisher’s LSD multiple comparisons testing main and simple effects.
  • Three-way ANOVA (limited to two levels in two of the factors, and any number of levels in the third).
  • Analysis of repeated measures data (one-, two-, and three-way) using a mixed effects model (similar to repeated measures ANOVA, but capable of handling missing data).
  • Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend).
  • Comparison of data from nested data tables using nested t test or nested one-way ANOVA (using mixed effects model).
Nonlinear Regression
  • Fit one of our 105 built-in equations, or enter your own. Now including family of growth equations: exponential growth, exponential plateau, Gompertz, logistic, and beta (growth and then decay).
  • Enter differential or implicit equations.
  • Enter different equations for different data sets.
  • Global nonlinear regression – share parameters between data sets.
  • Robust nonlinear regression.
  • Automatic outlier identification or elimination.
  • Compare models using extra sum-of-squares F test or AICc.
  • Compare parameters between data sets.
  • Apply constraints.
  • Differentially weight points by several methods and assess how well your weighting method worked.
  • Accept automatic initial estimated values or enter your own.
  • Automatically graph curve over specified range of X values.
  • Quantify precision of fits with SE or CI of parameters. Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate).
  • Quantify symmetry of imprecision with Hougaard’s skewness.
  • Plot confidence or prediction bands.
  • Test normality of residuals.
  • Runs or replicates test of adequacy of model.
  • Report the covariance matrix or set of dependencies.
  • Easily interpolate points from the best fit curve.
  • Fit straight lines to two data sets and determine the intersection point and both slopes.
Column Statistics
  • Calculate descriptive statistics: min, max, quartiles, mean, SD, SEM, CI, CV, skewness, kurtosis.
  • Mean or geometric mean with confidence intervals.
  • Frequency distributions (bin to histogram), including cumulative histograms.
  • Normality testing by four methods (new: Anderson-Darling).
  • Lognormality test and likelihood of sampling from normal (Gaussian) vs. lognormal distribution.
  • Create QQ Plot as part of normality testing.
  • One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value.
  • Identify outliers using Grubbs or ROUT method.
  • Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify "significant" findings or discoveries.
Linear Regression and Correlation
  • Calculate slope and intercept with confidence intervals
  • Force the regression line through a specified point.
  • Fit to replicate Y values or mean Y.
  • Test for departure from linearity with a runs test.
  • Calculate and graph residuals in four different ways (including QQ plot).
  • Compare slopes and intercepts of two or more regression lines.
  • Interpolate new points along the standard curve.
  • Pearson or Spearman (nonparametric) correlation.
  • Multiple linear regression (including Poisson regression) using the new multiple variables data table.
Clinical (Diagnostic) Lab Statistics
  • Bland-Altman plots.
  • Receiver operator characteristic (ROC) curves.
  • Deming regression (type ll linear regression).
Simulations
  • Simulate XY, Column or Contingency tables.
  • Repeat analyses of simulated data as a Monte-Carlo analysis.
  • Plot functions from equations you select or enter and parameter values you choose.
Other Calculations
  • Area under the curve, with confidence interval.
  • Transform data.
  • Normalize.
  • Identify outliers.
  • Normality tests.
  • Transpose tables.
  • Subtract baseline (and combine columns).
  • Compute each value as a fraction of its row, column or grand total.

 

 

 

 

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