GenStat 是個數據分析工具,也是統計軟體。事實上,GenStat 可以被稱為「我工作中不可或缺的幫手」。GenStat 是一款功能強大的統計系統軟體。靈活而完全交互式系統,最先進的圖形化工具,友好的圖形化用戶界面,以及強大的統計學程式編製功能。GenStat幫助你處理工程上的數據統計,科學研究中的數據分析...等。GenStat 具有悠久的成功歷史,並且不斷更新發展,使其活躍在統計學技術的最前沿。被廣泛應用於學術界、科研探索以及工業領域。
GenStat 是透過一個視窗選單介面讓初學用戶便於使用的全面化統計系統, 也可透過一個強有力的命令語言介面讓有經驗的用戶有更多的權力和靈活性。
完全互動式系統,最先進的圖形化工具,友好的圖形化用戶介面,還有強大的統計學程式編制功能。GenStat 具有30年悠久的成功歷史,並且不斷更新發展,使其活躍在統計學技術的最前端。GenStat的主要優點之一是已經透過培養訓練統計員重複被測試的可提供的統計方法的巨大的範圍,穿過很多應用和訓練。
basic statistics 基本的統計
design and analysis of designed experiments 實驗設計
analysis of linear and generalized linear mixed models
microarray analysis
regression (linear, nonlinear and generalized linear)
hierarchical generalized linear models
spatial analysis 空間分析
multivariate analysis techniques 多變量的分析技術
time series 時間序列
statistical process control methods 統計過程式控制制方法
survival analysis 生存分析
sample size calculations and resampling methods
更新介紹
Censored Poisson count data
Genstat makes it easy to analyse censored Poisson count data using a log-linear model, a generalized linear mixed model, or a hierarchical generalized linear model. Left and right censoring options are available on each of these menus, allowing you to set upper or lower values beyond which data will not be counted.
2D Trellis plot of groups
2D Trellis plots of groups are graphs that enable you to explore very large data sets in which observations are classified into groups. This graph type lets you investigate the distribution of groups of observations over two dimensions, that is, two variates, using either a trellis of bar charts or a trellis of pie charts.
Mosaic plots
Mosaic plots are a very powerful tool for visualising and exploring complex tables of counts, enabling you to identify patterns and relationships between multiple categorical variables in a single plot. Within the plot, each group (i.e., cell in the table) is represented by a coloured box, where the size of the box is proportional to the number of observations in that group.
PCA clustering
The aim of cluster analysis is to classify the observations into distinct groups based on how similar their data are. Genstat offers an efficient clustering algorithm specifically designed to handle very large multivariate data sets. This algorithm exploits principal components to reduce the dimensions of the data. Additionally, it uses a specialized clustering method to reduce the heavy computational load when dealing with a large number of observations.
Latent GOLD is a powerful latent class and finite mixture program with a very user-friendly point-and-click interface (GUI). Two add-on options are available to extend the basic version of the program.
The Advanced/Syntax add-on enables more control for advanced users via use of a Syntax command language including intuitive LG-equations™. This add-on also contains more advanced GUI modeling features such as Latent (Hidden) Markov and Multilevel models.
The Choice add-on allows estimation of discrete choice models via the point-and-click interface. When obtaining both the Choice and the Advanced/Syntax add-on, various advanced choice models can be estimated and the Syntax can also be used to further the customize discrete choice models.
LatentGold最主要的功能為:
潛類聚類分析(latentclass clusteranalysis)
潛類因子分析(latentclassfactor analysis)
潛類回歸模型(latent classregression)
Basic version Includes GUI for
LC Cluster
Latent GOLD®'s cluster module provides the state-of-the-art in cluster analysis based on latent class models. Latent classes are unobservable (latent) subgroups or segments. Cases within the same latent class are homogeneous on certain criteria (variables), while cases in different latent classes are dissimilar from each other in certain important ways.
The traditional latent class model can be used to handle measurement and classification errors in categorical variables, and can accomodate avriables that are nominal, ordinal, continuous, counts, or any combination of these. Covariates can be included directly in the model as well for improved cluster description.
Latent GOLD® improves over traditional ad-hoc types of cluster analysis methods by including model selection criteria and probability-based classification. Posterior membership probabilities are estimated directly from the model parameters and used to assign cases to the classes.
Discrete Factor (DFactor)
A DFactor model is often used for variable reduction or to define an ordinal attitudinal scale. It contains one or more DFactors which group together variables sharing a common source of variation. Each DFactor is either dichotomous (the default option) or consists of 3 or more ordered levels (ordered latent classes).
In this way, Latent GOLD®’s factor module has several advantages over traditional factor analysis:
Solutions are immediately interpretable and don’t require rotation
The factors are assumed to be ordinal and not continuous
No additional assumptions are required to estimate factor scores
The observed variables can be nominal, ordinal, continuous, or counts, or any combination of these
LC Regression and Growth
A Regression model is used to predict a dependent variable as a function of predictor variables in a homogeneous population.
Latent GOLD® makes it possible to estimate a regression model in a heterogeneous popu...
Netica 是一款功能強大且易於使用的完整解決方案,他可運用在信度網路(belief networks)以及影響圖(Influence diagrams)。直觀的用戶介面供使用者繪製網絡,用圖形來顯示網路結構,或是利用機率來描述變數間的強弱關係。
一旦建立網絡系統,他的領域知識可以透過複製貼上功能轉移到其他網路系統,視需要對所學出來的貝氏網路結構做修改。或是以創建連結中斷的節點庫儲存整模形式。當然,網絡結構和資料庫可以保存在檔案夾中或是列印出來。
Netica可以用網路系統進行各種最快速的推測模型、最現代的演算法。即使給的案例資料有限,Netica還是能在未知的變量中,找到適當的數值推測出我們需要的模型。這些數值或機率可能會以不同的形式像是柱狀圖或示意圖顯示。該範例可以方便的保存在檔案夾中,再從相同的網絡結構(或是不同的網絡結構)進一步查詢。使用者可以利用影響圖(Influence diagrams)在最大化的指定變量的期望值找出最佳的決策。
Netica可以建構條件規劃,因為使用者可以依靠可靠的意見做出決定,而時間與相互之間關聯的也會被列入考慮的重點。
Netica可以處理當學習資料增加時動態改變前端機率及條件機率表的問題。也可透過這個工具所提供的程式庫,使用Visual Basic 及C/C++等程式語言來控制及操作貝氏網路。
系統需求
Requirements: Netica Application requires a PC running any version of Microsoft Windows from XP to Windows10. The 64 bit version of Netica requires Windows 7 or higher. If you need a version of Netica for an earlier version of Windows (such as Windows 95), use Netica32.exe from the download package, or contact Norsys. Installation requires less than 10 MB of hard disk space.
Netica will run well even on a very slow PC (0.4 GHz), using very little RAM (about 30 MB), but working with complex Bayes nets may require much more speed and large amounts of RAM.
NLREG -- Nonlinear Regression and Curve Fitting
NLREG是一款強大地統計分析軟體。能夠進行線性和非線性回歸分析、表面和曲線擬和。此款軟體可以為一個方程式決定其參數值。NLREG能夠處理線性、多項式、指數、對數、周期性與一般的非線性函數。
NLREG features a full programming language with a syntax similar to C for specifying the function that is to be fitted to the data. This allows you to compute intermediate work variables, use conditionals, and even iterate in loops. With NLREG it is easy to construct piecewise functions that change form over different domains. Since the NLREG language includes arrays, you can even use tabular look-up methods to define the function.
The Standard version of NLREG can fit up to 5 variables and parameters to the data observations. The Advanced version can handle up to 2000 variables and parameters. In addition, the Advanced version can generate 3D surface plots such as shown here:
In addition to performing classic nonlinear regression, NLREG can be used to find the root or minimum value of a general multivariate, nonlinear function. It can also be used in a special form where the independent variable is omitted; an interesting application of this is "circular regression" where a circle is fitted to a set of data points.
Power and Precision 是一組獨立的強大統計分析軟體,可以在計畫研究中的案例分析做計算。這個程式的特色包含一個乾淨清楚的界面,還有許多幫助用戶發展理解能力分析的工具。它結合了建立表格、圖表和結果報告的能力,並在單鍵點擊下完成。
Work Interactively!
交互式的導覽讓你在 power analysis 中瞭解所有的步驟
在研究中修改任何元素並且能立即的看到相對的影響結果
利用大量的工具使影響使其可見化,並且了解 alpha, effect size, and sample size 的影響作用
Create A Report with a Single Click!
此程式產生一個清晰易懂、讓人充分理解的報告來解釋這個研究的設計、假設和權限
這個報告提供像教學工具一樣的服務,也可在讀書計畫和獎學金申請中的統計部分使用
可以輸出至 WordTM 或是其他 word 處理程式
Create Tables and Graphs with a Single Click!
在短時間內了解大綱!利用圖表來快速評估研究設計中的任何選項
可輸出至Word™, Excel™, PowerPoint™和其他程式
Include the graphs in your presentation or grant application to justify your selection of a sample size.
QuickEdit 讓您可輕易讀取 STDF 檔中的所有資料。各部份參數彙總、軟體二進制結果、硬體二進制結果、實驗參數資料,都以直覺性的表格方式呈現。您可以將結果另外輸出成 JMP、JSL、CSV、XLS檔案,與其它統計分析軟體共用。更可以編輯檔案,重新存成 STDF 格式。這項獨特的編輯功能,讓您突破硬體的限制,可依每個自變相排列實驗結果。
*STDF 為 Standard Test Data Format, 它是由 Teradyne 提出的一種檔案格式, 用於半導體測試記錄測試結果。
RapidMiner Studio 讓分析者可以毫不費力的從一團亂的資料中設計出可以預測分析跟可調整的資料模型。它的特色及功能簡述如下:
1. 讓您輕鬆的完成預測分析的工作且無需編寫任何程式
忘記那些煩人的程式碼吧!RapidMiner有最棒的最簡的直覺化且視覺化的使用者介面,讓你可以輕鬆的設計分析程序。更棒的是,可以藉由RapidMiner的社群的群眾智慧來給你使用上的最佳建議!
2. 具開放性及可擴展性。
上百種的資料載入、資料轉換、資料模型化及視覺化的方法且可以完整的配合Excel、Access、Oracle、IBM DB2、Netezza, Teradata, MySQL, Postgres, SPSS...等程式及系統讓你輕鬆容易的用你自己的演算法來整合各種資料!
3. 可以對各種規模的資料做現代化的分析,是您大數據分析的最佳選擇!
只要加入 "in-crowd" 並使用in-memory, in-database, in-stream, in-cloud及in-Hadoop來分析各種大小的資料來源。RapidMiner打破了傳統資料分析工具的限制,並讓你可以使用大量的資料來源。
4. 可以在所有重要的平台及作業系統上運行!包括 Windows, Mac OS, 及 Linux
產品特色
應用與界面
強大的可視化編程環境
數據訪問
訪問,加載和分析任何類型的數據
數據探索
提取統計信息和關鍵信息
數據準備
專業清理預測分析數據
Modeling
有效地構建和更快地交付更好的模型
驗證
自信而準確地估算模型性能
評分
為RapidMiner平台或其他應用程式評分模型
自動化
使用RapidMiner Studio中的編程結構
程式截圖
系統需求
Desktop system requirements
When setting up your system, keep in mind that available memory is the most important factor affecting the size of data sets that you can load and analyze in RapidMiner Studio. Additionally, while there is no specific minimal requirement on the CPU, data analysis is a computationally intensive task—the better your hardware, the better your experience. Finally, Java-based RapidMiner Studio is platform-independent and runs on every platform for which an appropriate Java Runtime Environment (JRE) is available.
Minimum
Dual core
2GHz processor
4GB RAM
>1GB free disk space
Resolution: 1280x1024
Recommended
Quad core
3GHz or faster processor
16GB RAM
>100GB free disk space
Operating System
Windows 10 (64-bit), Windows 11 (64-bit)
Linux (64-bit)
MacOS X 10.13 - 12
Java platform
64-bit recommended
OpenJDK Java 11 JRE (shipped, only needs to be installed manually on Linux)
Make sure that you are not just installing a headless JRE (as for example Ubuntu does by default)