Showing posts with label B. Show all posts
Showing posts with label B. Show all posts
Businss Process Quality Management
Also called Process Management or Reengineering. The concept of defining macro and micro processes, assigning ownership, and creating responsibilities of the owners.
Business Value Added
A step or change made to the product which is necessary for future or subsequent steps but is not noticed by the final customer.
Burn-in
Stress screen at perhaps high temperature or elevated voltages conducted on a product for the purpose of capturing failures inside the manufacturing facility and minimizing earlylife field failures.
Bug
Small insect. Also a problem in software.
The part of code that makes the program behave in an unwanted manner. The sooner a bug is detected in the Software Lifecycle, lesser would be the cost involved in fixing it
The term bug came from the fact that a moth flew into an early computer that ran on vacuum tubes.
The part of code that makes the program behave in an unwanted manner. The sooner a bug is detected in the Software Lifecycle, lesser would be the cost involved in fixing it
The term bug came from the fact that a moth flew into an early computer that ran on vacuum tubes.
Brainstorming
A method to generate ideas. Groundrules such as -no idea is a bad idea- are typical. Benefit of brainstorming is the power of the group in building ideas of each others ideas.
A problem solving approach/technique whereby working members in a group are conducting a deductive methodology for identifying possible causes of any problem, in order to surmount poor performance in any process or activity pursued by the group members and facilitator.
A problem solving approach/technique whereby working members in a group are conducting a deductive methodology for identifying possible causes of any problem, in order to surmount poor performance in any process or activity pursued by the group members and facilitator.
Boxplot
A box plot, also known as a box and whisker diagram, is a basic graphing tool that displays centering, spread, and distribution of a continuous data set.
A box and whisker plot provides a 5 point summary of the data.
1) The box represents the middle 50% of the data.
2) The median is the point where 50% of the data is above it and 50% below it. (Or left and right depending on orientation).
3) The 25th quartile is where, at most, 25% of the data fall below it.
4) The 75th quartile is where, at most, 25% of the data is above it.
5) The whiskers cannot extend any further than 1.5 times the length of the inner quartiles. If you have data points outside this they will show up as outliers.
A box and whisker plot provides a 5 point summary of the data.
1) The box represents the middle 50% of the data.
2) The median is the point where 50% of the data is above it and 50% below it. (Or left and right depending on orientation).
3) The 25th quartile is where, at most, 25% of the data fall below it.
4) The 75th quartile is where, at most, 25% of the data is above it.
5) The whiskers cannot extend any further than 1.5 times the length of the inner quartiles. If you have data points outside this they will show up as outliers.
Bottom line
Profit as stated on the income statement. A function of revenue, costs of goods and services sold and expenses.
Bootstrapping
A resampling technique that provides a simple but effective methodology to describe the uncertainty associated with a summary state ment without concern about details of the chosen summary or exact distribution from which data are calculated.
Bollocks
What most customers talk. It usually happens at a quality meeting, where they (the customer) compare your company to their other suppliers. (Usually caused by a lack of understanding of the customer requirements).
Blocks
Groups of experimental units treated similarly in an experimental design. Blocks are usually defined by background variables.
Blocking
Blocking neutralizes background variables that can not be eliminated by randomizing. It does so by spreading them across the experiment.
You can think of a block as an kind of uncontrolable factor that is added to the experiment. A block is ususally used when this uncontrolable factor cannot be avoided during the experiment, so it is incorporated into the experiment in a controlled way. The idea is to pull the variation due to the blocks out of the expermental error in order to reduce the experimental error and give the test more power.
Common examples of when blocking factors are used:
" When you don't have enough units from one lot and are forced to use another lot, AND you suspect (maybe know) that there are important differences between lots. You then use (in a controlled manner) half of the units from one lot and half of the units from the other lot.
" When you don't have enough test chambers for all the parts, AND you suspect (maybe know) that there are important differences in the effects between test chambers. You then assign (in a controlled manner) half of your units to one chamber and half to the other chamber.
" The original use of blocking involved agricultural experiments. Researchers needed to use multiple fields or fields that has important intra-field variation. They would the potential fertility of a field as a block factor. Similar blocking might have been needed due to differences between edge-effects and center effects on crops grown.
You can think of a block as an kind of uncontrolable factor that is added to the experiment. A block is ususally used when this uncontrolable factor cannot be avoided during the experiment, so it is incorporated into the experiment in a controlled way. The idea is to pull the variation due to the blocks out of the expermental error in order to reduce the experimental error and give the test more power.
Common examples of when blocking factors are used:
" When you don't have enough units from one lot and are forced to use another lot, AND you suspect (maybe know) that there are important differences between lots. You then use (in a controlled manner) half of the units from one lot and half of the units from the other lot.
" When you don't have enough test chambers for all the parts, AND you suspect (maybe know) that there are important differences in the effects between test chambers. You then assign (in a controlled manner) half of your units to one chamber and half to the other chamber.
" The original use of blocking involved agricultural experiments. Researchers needed to use multiple fields or fields that has important intra-field variation. They would the potential fertility of a field as a block factor. Similar blocking might have been needed due to differences between edge-effects and center effects on crops grown.
Black Noise
Black Noises are effected by external factor and mean value is shifted.
And generally, we can analyze thier cause.
Can control them in the process.
Generally, mean is shifted form target value.
According to over time, we can track capability changes.
And generally, we can analyze thier cause.
Can control them in the process.
Generally, mean is shifted form target value.
According to over time, we can track capability changes.
Black Belt
Six Sigma team leaders responsible for implementing process improvement projects (DMAIC or DFSS) within the business -- to increase customer satisfaction levels and business productivity. Black Belts are knowledgeable and skilled in the use of the Six Sigma methodology and tools.
Black Belts have typically completed four weeks of Six Sigma training, and have demonstrated mastery of the subject matter through the completion of project(s) and an exam.
Black Belts have typically completed four weeks of Six Sigma training, and have demonstrated mastery of the subject matter through the completion of project(s) and an exam.
Binomial Random Variable
A discrete random variable which represents the number of successes out of n (the sample size) identical and independent trials.
Binomial experiment
an experiment involving a sequence of n identical and independent trials, where there are only two possible outcomes(e.g., success or failure) for each trial.
Binomial distribution
Given that a trial can have only two possible outcomes(yes/no, pass/fail, heads/tail), of which one outcome has probability p and the other probability q(q=1-p), the probability that the outcome represented by p occurs x times in n trials is given by the binomial distribution.
Bimodal distribution
A distribution that is a combination of two different distributions resulting in two distinct peaks.
Bias
Bias in a sample is the presence or influence of any factor that causes the population or process being sampled to appear different from what it actually is. Bias is introduced into a sample when data is collected without regard to key factors that may influence it.
Beta Risk
Beta risk is defined as the risk of accepting the null hypothesis when, in fact, it is false.
Consumer Risk or Type II Risk.
Beta risk is defined as the risk of accepting the null hypothesis when, in fact, the alternate hypothesis is true. In other words, stating no difference exists when there is an actual difference. A statistical test should be capable of detecting differences that are important to you, and beta risk is the probability (such as 0.10 or 10%) that it will not. Beta risk is determined by an organization or individual and is based on the nature of the decision being made. Beta risk depends on the magnitude of the difference between sample means and is managed by increasing test sample size. In general, a beta risk of 10% is considered acceptable in decision making.
The value (1-beta) is known as the "power" of a statistical test. The power is defined as the probability of rejecting the null hypothesis, given that the null hypothesis is indeed false.
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Actually, the term "risk" really means probability or chance of making an incorrect decision. The actual risks of making a wrong decision are unique to the decision being made and may be realized only if a wrong decision is made. For example, the probability of a false negative (type II error) on an a test for aids in a patient might be calculated as 0.001. The risk of concluding that a person does not have aids when in fact they do is quite a different concept - the "risk" is propagation of a deadly disease.
Consumer Risk or Type II Risk.
Beta risk is defined as the risk of accepting the null hypothesis when, in fact, the alternate hypothesis is true. In other words, stating no difference exists when there is an actual difference. A statistical test should be capable of detecting differences that are important to you, and beta risk is the probability (such as 0.10 or 10%) that it will not. Beta risk is determined by an organization or individual and is based on the nature of the decision being made. Beta risk depends on the magnitude of the difference between sample means and is managed by increasing test sample size. In general, a beta risk of 10% is considered acceptable in decision making.
The value (1-beta) is known as the "power" of a statistical test. The power is defined as the probability of rejecting the null hypothesis, given that the null hypothesis is indeed false.
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Actually, the term "risk" really means probability or chance of making an incorrect decision. The actual risks of making a wrong decision are unique to the decision being made and may be realized only if a wrong decision is made. For example, the probability of a false negative (type II error) on an a test for aids in a patient might be calculated as 0.001. The risk of concluding that a person does not have aids when in fact they do is quite a different concept - the "risk" is propagation of a deadly disease.
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