Sigma levels using the table are approximately 3.6. Total Opportunities = 5000 Total Defects = 100
Defects/Opportunities = 100/5000 Defects/Million Opportunities = 100/5000*10,00,000 = 20,000. The
solution is 98% if 3.6 is substituted in the RTY table.
Please select 2 correct answers
The Fitted Line Plot suggests that there is a strong linear relationship between the Reactant level and the Energy Consumed. When the Reactant level is set to 6, the predicted output (Y) is close to -18, and the Reactant level accounts for over 85% of the variation in the Energy Consumed. This indicates that the linear regression model is a good fit for the data and can be used to make reasonably accurate predictions of the Energy Consumed based on the Reactant level.
The system would produce excessive waste if it processed more data than the consumer required.
The Moving Average (MA) chart is a type of control chart that is commonly used to monitor processes over time. It is particularly useful for identifying trends and patterns in the data. The smoothing process you mentioned likely involved calculating the moving average by taking the average of a fixed number of consecutive data points. This moving average calculation helps reduce random variation and highlight underlying trends or shifts in the process.
Cost reduction is not the primary objective of the Lean philosophy, which seeks to give perfect value to the
client through a perfect value generation process that produces zero waste.
The residuals are the result of the actual experimental response data varying somewhat from what a Belt had predicted them to be using a regression model.
In a regression analysis, the goal is to create a model that explains the relationship between the independent variable(s) (also known as predictor variable(s)) and the dependent variable (response variable).
A Control Plan is considered a living document in Lean Six Sigma, and it is never fully closed or considered a final, static document. Instead, the Control Plan is continuously updated and monitored as long as the process it describes remains in operation.
Typically, on SPC charts, the most recent data points are plotted on the right-hand side, and the chart extends to the left as new data becomes available. This layout allows users to visualize the process's performance over time, with the most recent information immediately visible. It also enables the easy detection of any shifts, trends, or other changes in the process behavior.
When implementing Statistical Process Control (SPC), having Control Limits that are wider than the Customer Specification Limits can be an undesirable situation.
Control Limits in SPC charts are used to determine the natural variation or inherent variability of a process. They are typically set at a certain multiple of the process standard deviation from the process mean. The purpose of control limits is to identify when the process is operating within the expected variation (in control) or when it exhibits significant variation (out of control). When points on the SPC chart fall within the control limits, it suggests that the process is stable and in a state of statistical control.
Although one of the clauses of CLT states that sample mean = population mean, this cannot always be said
with certainty.
Sun Square of Pure Error and Sum Square of Lack of Fit are combined to form the Sum Square of Error, or
SSE. Only Pure Error would be a factor in SSE if the model were flawless, which means there would be no fit issues.
Focus groups are thought to be the most effective and useful tool for data collection.
In Lean Six Sigma and other quality improvement methodologies, a Belt (a trained practitioner in Lean Six Sigma) may occasionally conduct a quick experiment referred to as OFAT, which stands for "One Factor At a Time."
A periodic time frame is commonly used to arrange for Control Limit and Center Line calculations with good Statistical Process Control (SPC) implementation in a process.
The Xbar-R charts are a type of control chart used to monitor the central tendency (average) and dispersion (variability) of a process over time when data are collected in subgroups.
If the p result for a hypothesis test is less than the significance level, the null hypothesis is rejected.
