2018年10月，生物統計分析軟件GraphPad Prism 8版本已正式發布。新版本支持Windows及Mac兩種平臺，增強了數據可視化及圖形定制功能，導航也更加直觀，統計分析功能更加強大。
1、有效的組織您的數據。與電子表格和其他科學繪圖程序不同，GraphPad Prism有八種不同類型的數據表，專門為用戶要運行的分析而格式化。這樣用戶可以更輕松、更正確的輸入數據，選擇合適的分析并創建令人驚喜的圖形。
2、執行正確的分析。GraphPad Prism提供了廣泛的分析庫，從常見到高度特異性非線性回歸，t檢驗，非參數比較，單因素，雙因素和三因子方差分析，列聯表，生存分析等等。每個分析都有一個清單，以幫助您了解所需的統計假設，并確認您已選擇適當的測試。
3、一鍵式回歸分析。沒有其他程序像GraphPad Prism那樣簡化曲線擬合。選擇一個方程式，Prism進行曲線的其余擬合，顯示結果和函數參數表，在圖形上繪制曲線，并插入未知值。
4、無需編程即可自動完成工作。減少分析和繪制一組實驗的繁瑣步驟。通過創建模板，復制系列或克隆圖表可以輕松復制您的工作，從而節省您數小時的設置時間。使用Prism Magic一鍵單擊，對一組圖形應用一致的外觀。
5、無數種自定義圖表的方法。專注于數據中的故事，而不是操縱您的軟件。GraphPad Prism可以輕松創建所需的圖形。選擇圖形類型，并自定義任何部分 - 數據的排列方式，數據點的樣式，標簽，字體，顏色等等。定制選項是無止境的。
6、現在有八種數據表。新：多變量數據表。每行代表不同的主題，每列是不同的變量，允許您執行多元線性回歸（包括泊松回歸），將數據子集提取到其他表類型，或選擇和轉換數據的子集。
新增內容：嵌套數據表。分析和可視化包含相關組內子集的數據; 使用這些表中的數據執行嵌套t檢驗和嵌套單向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).
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.
