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SAS/QC software provides a wide range of specialized tools that help you improve products, optimize processes and increase levels of customer satisfaction. It enables organizations to go beyond basic process control to incorporate more advanced statistical analyses for additional insights into processes and product improvements.

Benefits
Handle large volumes of data from multiple processes. SAS/QC can operate on virtually any data source and runs across most computing platforms. With the ability to monitor multiple processes and integrate a wide variety of data, you gain a more complete picture of enterprisewide quality improvement efforts. This enables you to maintain consistent standards and use all information that is collected to make better decisions.
Identify the root causes of problems. It’s not enough to simply recognize that you have a quality problem. You have to find the cause of the problem to determine how to fix it. SAS/QC enables users to discover root causes of problems and goes beyond basic process control to provide more complex statistical analyses—all enabling you to create more efficient, cost-effective processes.
Design experiments to improve products or processes. SAS/QC provides powerful tools and a guided user interface for designing experiments and managing the experimental process. A point-and-click environment is designed for engineers and researchers who can benefit from an interface for each stage of the experimental design process, from building designs and determining significant effects to optimization and reporting.
Assess product reliability. Graphical and statistical tools benefit reliability engineers and industrial statisticians working with product life data and system repair data. They also benefit workers in other fields, such as medical research, pharmaceuticals, social sciences and business, where survival and recurrence data are analyzed.
Create insights that drive competitive advantage. SAS/QC provides a depth and breadth of tools for statistical quality improvement not found in other software packages. SAS enables you to go beyond the basic to create insights that drive competitive advantage.

Features
Basic quality problem solving
Pareto charts.
Ishikawa diagrams.
Statistical Process Control (SPC)
Shewhart charts: X and R charts, x and x charts, box charts, p charts, np charts, c charts, u charts, individual measurements and moving range charts, tests for special causes.
Cumulative sum control charts.
Moving average charts.
Nonstandard control charts:
Trend charts for time-dependent data.
Start charts for multivariate process data.
GAGE application
Measurement system evaluation: range charts, average charts.
Variance components method.
Process capability analysis
Comparative histograms.
CDF plots, probability plots, Q-Q plots, P-P plots.
Capability indices.
Confidence, tolerance and prediction intervals.
Descriptive statistics.
Reliability analysis
Accelerated life test models for censored data.
Maximum likelihood estimation.
Asymptotic normal and likelihood ration confidence intervals.
Weibayes analyses.
Nonparametric estimates and confidence intervals.
Analysis of multiple failure models.
Probability plots.
Life vs. stress plots.
Nonparametric plots of mean cumulative function.
Analysis of means
Simultaneously compare k treatment means with their overall mean.
Single or multiple response variables.
Compute decision limits from data.
Adjust decision limits for unequal samples sizes.
Means charts, p charts, u charts, box charts.
Design of Experiments
Full and fractional factorial designs.
D-optimal and A-optimal designs.
ADX Interface for Design of Experiments:
Two-level, response surface, mixture and mixed-level designs.
Split-plot and fractional factorial split-plot designs.
Orthogonal arrays for mixed-level design.
Analysis of unstructured experiments.
Main effect, interaction, cube and factorial plots.
Statistical analyses including regression, ANOVA, residual and outlier analysis.
Graphical optimization.
HTML report generation.

名称：SAS/数据控制
主要功能：1. SAS/数据控制可以操作几乎任何数据源，并在大多数计算平台上运行。有能力监视多个进程并集成了多种数据，使得企业能对自己为质量提高的努力做更全面的了解，使企业能够保持一致的标准，并使用所有收集到的信息做出更好的决策；
2.SAS /质量控制，能使用户发现问题的根源并可以对以后的基本过程控制，从而实现更复杂的统计分析，让您创建更有效的进程；
3.它可以通过设计实验，改进产品或过程。SAS /质量控制提供了强大的工具和设计实验及实验过程管理指导的用户界面。 有一个点击环境的设计，可以使工程师和研究人员在实验设计进程中受益。
4.它可以提高评估产品的可靠性。图形和统计工具可以使工程师等对产品寿命的数据和系统的维修工作的工业统计数据有更可靠的认识了解，同时在其他领域，例如医学研究，药品，社会科学和商业等也有很大的帮助；
5.发现新的视野以提升竞争优势。SAS /质量控制提供了其他软件所没有的、有着深度和广度的有利于质量改进的工具。

特点：
1. 基本问题的解决
帕累托图。
石川图。
2. 统计过程控制（SPC）
哈特图：X和R图，x和x图，图框，p图，国民党图表，ç图，美图表，个人移动范围测量和海图，对特殊原因测试。
累积总和控制图。
移动平均线图。
非标准控制图：
趋势图的时间依赖性的数据。
多变量过程数据开始图表。
3. Gage应用
测量系统评价：各种图表，平均图表。
方差分量的方法。
4. 过程能力分析
对比直方图。
民防部队图，概率图，QQ图，聚丙烯阴谋。
能力指标。
信心，容忍和预测区间。
描述性统计。
5. 可靠性分析
加速寿命试验截尾数据模型。
最大似然估计。
渐近正常和似然比置信区间。
4.Weibayes分析。
非参数估计和置信区间。
分析多种故障模式。
概率图。
5.分析方法
同时比较亩治疗手段的整体意思。
单个或多个响应变量。
从数据计算限额的决定。
调整大小不等的样本决定限制。
指图，p图，美图，图框。
6．实验设计
全部和部分因子设计。
D -最优和A -最优设计。
ADX接口实验设计：
两级，响应面，混合和混合级设计。
裂区和部分因析裂区设计。
混合级设计正交阵列。
非结构化分析实验。
主要作用，互动，多维数据集和阶乘阴谋。
统计分析包括回归分析，方差分析，剩余的异常值的分析。
图形优化。
HTML报告生成。
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