Showing posts with label R. Show all posts
Showing posts with label R. Show all posts
Run in
A procedure to put usage on a machine within the manufacturing facility for the purpose of capturing early life failures before shipment.
Run Chart
A performance measure of a process over a specified period of time used to identify trends or patterns.
Rule of Thumb(ROT)
A simplified, practical procedure that can be used in place of a formal statistical test that will produce approximately the same result.
R-Square Adjusted
Unlike R-square, R-square adjusted takes into account the number of X's and the number of data points. FORMULA: R-sq (adj) = 1 - [(SS(error)/DF(error)) / (SS(total)/DF(total))]
Takes into account the number of Xs and the number of data points...also answers: how much of total variation is explained by X.
Takes into account the number of Xs and the number of data points...also answers: how much of total variation is explained by X.
R-Square
A mathematical term describing how much variation is being explained by the X.
Rsq = 1 - SS(regression)/SS(total), Assuming "SS" = Sum Squared error, and that "SS(total)" means the variance in the data. This should be obvious, as R-squared approaches unity as a regression approaches a perfect fit.(i.e., Rsq = 1 - sum((data - regression)^2))/sum((data - datamean).^2))
The R-squared value is the fraction of the variance (not 'variation') in the data that is explained by a regression.
Rsq = 1 - SS(regression)/SS(total), Assuming "SS" = Sum Squared error, and that "SS(total)" means the variance in the data. This should be obvious, as R-squared approaches unity as a regression approaches a perfect fit.(i.e., Rsq = 1 - sum((data - regression)^2))/sum((data - datamean).^2))
The R-squared value is the fraction of the variance (not 'variation') in the data that is explained by a regression.
Root Cause
An identified reason for the presence of a defect or problem.
The most basic reason, which if eliminated, would prevent recurrence.
The source or origin of an event.
The most basic reason, which if eliminated, would prevent recurrence.
The source or origin of an event.
Return on Net Assets (RONA)
Return on Net Assets
RONA is one bottom-line measurement showing performance relative to strategic goals and objectives. More and more successful companies are utilizing RONA measurement. It provides an apples-to-apples comparison of performance that is understood by non-financial professionals.
Calculated as: RONA = Net Income /(Fixed Assets + Net Working Capital)
Interpretation: The higher the return, the better the profit performance for the company.
RONA is one bottom-line measurement showing performance relative to strategic goals and objectives. More and more successful companies are utilizing RONA measurement. It provides an apples-to-apples comparison of performance that is understood by non-financial professionals.
Calculated as: RONA = Net Income /(Fixed Assets + Net Working Capital)
Interpretation: The higher the return, the better the profit performance for the company.
Rolled Throughput Yield (RTY)
Rolled Throughput Yield (RTY) is the probability that a single unit can pass through a series of process steps free of defects.
Next we will turn our attention to a Rolled Throughput Yield example. If you will remember, the First Time Yield calculation we did (FTT) considered only what went into a process step and what went out. Rolled Throughput Yield adds the consideration of rework. Using the previous example:
Process A = 100 units in and 90 out Process B = 90 in and 80 out Process C = 80 in and 75 out Process D = 75 in and 70 out.
If in order to get the yield out of each step we had to do some rework (which we probably did) then it really looks more like this:
Process A = 100 units, 10 scrapped and 5 reworked to get the 90. The calculation becomes [100-(10+5)]/100 = 85/100 = .85 This is the true yield when you consider rework and scrap.
Process B = 90 units in, 10 scrapped and 7 reworked to get the 80. [90-(10+7)]/90 = .81
Process C = 80 units in, 5 scrapped and 3 reworked to get the 75. [80-(5+3)]/80 = .9
Process D = 75 units in, 5 scrapped and 10 reworked to get the 70. [75-(5+10)]/75 = .8
Now to get the true Rolled Throughput Yield (Considering BOTH scrap and the rework necessary to attain what we thought was first time throughput yield) we find that the true yield has gone down significantly:
.85*.81*.9*.8 = .49572 or Rounded to the nearest digit, 50% yield. A substantially worse and substantially truer measurement of the process capability. An Assumption is made in the preceeding example that there are no spilled opportunities after each process step.
Next we will turn our attention to a Rolled Throughput Yield example. If you will remember, the First Time Yield calculation we did (FTT) considered only what went into a process step and what went out. Rolled Throughput Yield adds the consideration of rework. Using the previous example:
Process A = 100 units in and 90 out Process B = 90 in and 80 out Process C = 80 in and 75 out Process D = 75 in and 70 out.
If in order to get the yield out of each step we had to do some rework (which we probably did) then it really looks more like this:
Process A = 100 units, 10 scrapped and 5 reworked to get the 90. The calculation becomes [100-(10+5)]/100 = 85/100 = .85 This is the true yield when you consider rework and scrap.
Process B = 90 units in, 10 scrapped and 7 reworked to get the 80. [90-(10+7)]/90 = .81
Process C = 80 units in, 5 scrapped and 3 reworked to get the 75. [80-(5+3)]/80 = .9
Process D = 75 units in, 5 scrapped and 10 reworked to get the 70. [75-(5+10)]/75 = .8
Now to get the true Rolled Throughput Yield (Considering BOTH scrap and the rework necessary to attain what we thought was first time throughput yield) we find that the true yield has gone down significantly:
.85*.81*.9*.8 = .49572 or Rounded to the nearest digit, 50% yield. A substantially worse and substantially truer measurement of the process capability. An Assumption is made in the preceeding example that there are no spilled opportunities after each process step.
Robust Process
A robust process is one that is operating at 6 sigma and is therefore resistant to defects. Robust processes exhibit very good short-term process capability (high short-term Z values) and a small Z shift value. In a robust process, the critical elements u
Responsibility & Authority
The Manager Quality Systems has the responsibility & authority to maintain & implement this document.
Response Surfaces Roadmap
Suppose we had an equation and a graph describing how the independent variables affect a response.
Response surface methodology(RSM)
The empirical study of relation ships between one or more responses and input variable factors.
response surface methodology
When projections are made off a response surface, it is obviously important that the model fit the initial data satisfactorily.
Response Surface Experiments
Determining if there are important interactions between independent variables.
Resolution
Resolution is a measure of the degree of confounding among effects. Roman numerals are used to denote resolution. The resolution of your design defines the amount of information that can be provided by the design of experiment. As with a computer screen,
The above is an explanation of resolution in the context of DOE.
For Gage R & R, 'Number of distinct categories' measures the ability of the measurement system to resolve the data in to a number of catogories - hence a measurement of resolution - the greater, the better.
The above is an explanation of resolution in the context of DOE.
For Gage R & R, 'Number of distinct categories' measures the ability of the measurement system to resolve the data in to a number of catogories - hence a measurement of resolution - the greater, the better.
Residual
A residual is the difference between the actual Y output value and the Y output value predicted by the regression equation. The residuals in a regression model can be analyzed to reveal inadequacies in the model. Also called "errors"
Reproducibility
Reproducibility is the variation in average measurements obtained when two or more people measure the same parts or items using the same measuring technique.
Replication
Replication occurs when an experimental treatment is set up and conducted more than once. If you collect two data points at each treatment, you have two replications. In general, plan on making between two and five replications for each treatment.
Replication is a repeat of the experimental run such that the repeated run only occurs after a full breakdown of 'SET UP' and 'SET POINTS'. This requires an additional 'SET UP' and the establishment of'SET POINTS' even if the prior experimental run was with these exact 'SET POINTS'.
On the contrary Repetition is making a repeat of the experimental run and taking an immediate additional sample without breaking down the 'SET UP' or changing the 'SET POINT'
Replication is done to reduce the impact of the inherent variation in the process, whereas repetition reflects the uncontrolled variability in the measurements.
In other words Repetition is equivalent to Repeatability, whereas Replication is equivalent to Reproducibility.
Replication is a repeat of the experimental run such that the repeated run only occurs after a full breakdown of 'SET UP' and 'SET POINTS'. This requires an additional 'SET UP' and the establishment of'SET POINTS' even if the prior experimental run was with these exact 'SET POINTS'.
On the contrary Repetition is making a repeat of the experimental run and taking an immediate additional sample without breaking down the 'SET UP' or changing the 'SET POINT'
Replication is done to reduce the impact of the inherent variation in the process, whereas repetition reflects the uncontrolled variability in the measurements.
In other words Repetition is equivalent to Repeatability, whereas Replication is equivalent to Reproducibility.
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