生物统计分析软件GraphPad Prism 8 已正式发布

2018年10月,生物统计分析软件GraphPad Prism 8版本已正式发布。新版本Windows及Mac两种平台,增强了数据可视化及图形定制功能,导航也直观,统计分析功能。

1、的组织您的数据。GraphPad Prism有八种不同类型的数据表,为用户要运行的分析而格式化。这样用户可以更、更正确的输入数据,选择合适的分析并创建令人惊喜的图形。

2、执行正确的分析。GraphPad Prism提供了广泛的分析库,从常见到特异性非线性回归,t检验,非参数比较,单因素,双因素和三因子方差分析,列联表,生存分析等等。分析都有清单,以帮助您了解所需的统计假设,并确认您已选择的测试。


4、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,对一组图形应用的外观。

5、无数种自定义图表的方法。GraphPad Prism可以创建所需的图形。选择图形类型,并自定义部分 - 数据的排列方式,数据点的样式,标签,字体,颜色等等。定制选项是的。


新增内容:嵌套数据表。分析和可视化相关组内子集的数据; 使用这些表中的数据执行嵌套t检验和嵌套单向ANOVA。

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).


· 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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