"Muda" is the Japanese word for waste, particularly in the context of Lean management and the Toyota Production System.
The median is the middle value in a data set when the values are arranged in numerical order.
In statistics, the median is a measure of central tendency that represents the value that separates the lower half of the data from the upper half. To find the median of a dataset, you first arrange the data in ascending or descending order and then determine the middle value. If the dataset has an odd number of values, the median is the single middle value. If the dataset has an even number of values, the median is the average of the two middle values.
Yellow Belts are team members who have basic training in Lean Six Sigma concepts and tools. They provide support to Green Belts and Black Belts in process improvement projects. Yellow Belts typically have a general understanding of Lean Six Sigma principles and may be involved in data collection, analysis, and smaller improvement activities within their work areas.
SPC stands for Statistical Process Control.
Statistical Process Control (SPC) is a methodology used to monitor, control, and improve processes by analyzing data and making data-driven decisions. It is a quality control technique that involves the use of statistical methods to understand the variation present in a process and ensure that the process operates within its desired specifications.
Serious workplace accidents that cause a shutdown for more than 2 hours would not be considered a common cause variation in a process.
A suitable control chart with the right control limits can help differentiate special cause variation from common cause variation in a process.
The Interquartile Range (IQR) is not a measure of central tendency or position of a process; instead, it is a measure of dispersion or variability.
Rework activities are typically considered non-value-added activities in a process.
Rework activities involve correcting defects, errors, or issues in a product or process that were not done correctly the first time. While rework is necessary to ensure that the product meets quality standards, it is considered non-value-added because it does not directly contribute to the customer's desired features or attributes. The customer is not willing to pay for rework activities; they expect the product to be defect-free from the start.
A hypothesis test is a statistical tool used for deciding if statistical significance exists between data samples or groups.
A hypothesis test is a formal procedure to test a claim or hypothesis about a population parameter based on sample data. It helps determine whether there is enough evidence to support or reject a specific claim or hypothesis. The main goal of a hypothesis test is to assess whether the observed differences between sample data are significant enough to infer meaningful differences or relationships in the population from which the samples were drawn.
The one-sample sign test is similar to the one-sample t-test in the sense that both are used to assess the difference between a sample mean (or median) and a hypothesized value. However, the one-sample sign test is specifically used when the data set is non-normal or when the data does not meet the assumptions required for the one-sample t-test.
The one-sample t-test assumes that the data follows a normal distribution, while the one-sample sign test is a non-parametric test that does not assume any specific distribution for the data. Non-parametric tests, like the sign test, are useful when the data is not normally distributed or when the sample size is small.
UCL stands for Upper Control Limit.
The control limits are calculated statistically from the data and represent the boundaries within which the process is expected to operate when it is in a state of statistical control. The UCL (Upper Control Limit) is the upper boundary, and the LCL (Lower Control Limit) is the lower boundary. These limits are typically set at a specified number of standard deviations from the process mean.
A process Sigma level of 6 corresponds to approximately 3.4 defects per 1 million opportunities.
A typical linear regression equation is represented as:
Y = a + bx
Where:
Y is the dependent variable (the variable we want to predict or explain).
a is the intercept, which represents the predicted value of Y when the independent variable (X) is zero.
b is the slope coefficient, which indicates the change in the dependent variable (Y) for a one-unit change in the independent variable (X).
If the correlation coefficient is between 0.5 and 1 (inclusive), it indicates a positive correlation between the two variables being analyzed.
The correlation coefficient, often denoted by the symbol "r," is a statistical measure that quantifies the strength and direction of the linear relationship between two variables. It ranges from -1 to +1:
W. Edwards Deming is often referred to as the "Father of Quality Control" and is recognized for his significant contributions to the field of quality management and improvement.
In hypothesis testing, the p-value represents the probability of obtaining the observed results (or more extreme results) when the null hypothesis is true. The level of significance, often denoted by α, is the predetermined threshold that defines the acceptable risk of making a Type I error (incorrectly rejecting the null hypothesis when it is true).
