Analysis of Non-Normal Data for Quality
Progress beyond the foundational principles introduced in the Manufacturing Statistical Quality Analysis course in minitab training and gain proficiency in a broader range of tools. Enhance your capabilities to evaluate quality standards and gauge process competency even in scenarios where your data exhibits skewness, extreme outliers, multimodality, or clustering. Deepen your understanding of control charting by acquiring the skills to accurately pinpoint instances of special cause variation in the presence of non-normally distributed data.
Discover the art of effectively employing graphical techniques and statistical tests to identify non-normally distributed data, thereby enabling you to select an appropriate distribution or transformation for your analysis. Moreover, grasp the implications of inadequate measurement resolution and sample size on the assessment of data normality. This course in minitab training empowers you with the expertise to navigate complex data scenarios and make informed decisions based on sound statistical practices.
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- Utilize a Probability Plot to assess how well a distribution aligns with data, estimate percentiles, and compare sample distributions.
- This plot illustrates each value against the percentage of values in the sample that are equal to or less than it, tracing a line corresponding to a fitted distribution.
- The y-axis transformation ensures that the fitted distribution forms a linear configuration.
Tests for Normality
- The test outcomes determine whether you should dismiss or retain the null hypothesis concerning the origin of data from a normally distributed population. Within a single analysis, you have the option to conduct a normality test and generate a normal probability plot. The normality test and probability plot are usually the best tools for judging normality.
- Types of normality tests –
- Anderson-Darling test
- Ryan-Joiner normality test
- Kolmogorov-Smirnov normality test
Capability Analysis for Nonnormal Data
- In cases where your data is not normally distributed, you have two methods at your disposal for conducting a capability analysis:
- Opt for a nonnormal distribution model that suits your data and subsequently employ a capability analysis tailored for nonnormal data, like Nonnormal Capability Analysis.
- Alternatively, modify the data to align with a normal distribution model and apply a capability analysis designed for normal data, such as Normal Capability Analysis.
Box-Cox and Johnson transformations
- A Box-Cox transformation applied to your process data can assist in rectifying the following situations:
- The process data exhibit non-normal distribution, particularly when collected without subgroups.
- Additionally, the variance among subgroups is unstable due to the variation in data being proportional to the subgroup mean.
- Apply the Johnson Transformation to convert your data into a normal distribution by utilizing the Johnson distribution framework. Using this analysis, you can do the following:
- Assess if both the original and transformed data adhere to a normal distribution.
- Record the modified values within the worksheet.
Multiple Variables Capability Analysis
- Multiple variable analysis enables comparison of capability across multiple variables or groups of the same variable.
- Useful for scenarios like assessing process capability pre and post improvement.
- After initial capability analysis, identify potential process enhancements.
- Following process modifications, conduct another capability analysis to measure improvement.
- When capability improves, implementing control mechanisms is crucial to maintain enhancements.
- Absence of control mechanisms risks reverting the process to its initial capability level.
- Apply tolerance intervals to calculate a range of values covering a specified proportion of forthcoming product outputs for a given characteristic.
- Tolerance intervals establish upper and/or lower limits within which a specified percentage of process outputs lies, with a specified level of confidence.
Individuals Control Charts
- Utilize an Individuals Chart to track the process mean when dealing with continuous data consisting of individual observations not organized into subgroups. This control chart is effective for monitoring process stability across time, facilitating the detection and rectification of any process instabilities.
Multiple Failure Modes Analysis
- Minitab has the capability to analyze systems that involve various failure modes. Since distinct failure modes often exhibit different failure distributions, it is typically advisable to categorize failure data based on the specific failure mode. Comprehending failure modes holds significant importance in enhancing product reliability.