what is Ishiwara and Pareto Analysis?that's under statistical quality control.thanks?
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Ishiwara analysis is the analysis of the root causes of problems like late deliveries, defects in products etc. \n\nPareto analysis is a set of tools for making decisions. It is basically the idea that 20 % of the causes are responsible for 80% of the effect. It can also be used to try to ferret out the causes of failures.
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quality control\n\nthe name brands have better quality control, but they are made the same
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Explain ABC analysis?\n\nWhat are the factors influence in  fixing  the Re-order level
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This is a good question even for a statistician.\nThe problem is your set is small and I do not know what the deviation between samples is, however the repeatability and reproducibility information can bind the window \nthrough which the third value will fall through or perhaps even remain in.  \n\nLet me know.
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Statistician, or anyone doing statistical data analysis (engineers, scientists, etc.)\n\nBox and whisker graphs are a tool for conveying location and variation information in data sets, particularly for detecting and illustrating location and variation changes between different groups of data.\n\nHere is a good description of how to use one.\nhttp://mathworld.wolfram.com/Box-and-WhiskerPlot.html
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Quality control is where the products are tested by a "team" to see how a product works or tastes.  This control helps business owners know if their products are okay the way they are, or need improvement. \n\nUsing "quality control" as your keyword, do a web search to find detailed information for your homework. I am a student also.good luck.
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This Electronic Statistics Textbook offers training in the understanding and application of statistics. \n\nExploratory Data Analysis is also a good site\n\n\navailable at the site below
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Parametric and Non-Parametric data\n\nIn statistical terms a parameter is a statistic calculated from a set of data, e.g. a mean or a standard deviation are both parameters. In order to calculate parameters, and to use them in statistical analysis three assumptions must be tested against the data. The data is considered as parametric if these three assumptions are valid:\n\n1. Can it be assumed that the data has been obtained from a population which is considered "normal" in the statistical sense. In other words if a sufficiently large sample of data was obtained from the population is it likely that it would be normally distributed. This may be known from similar research, but if not, and provided the sample is reasonably large, it may be possible to estimate from the sample whether this assumption is reasonable.\n\n2. The populations from which the samples are drawn should have equal variances. This can be determined by inspection of the data, looking at the spread or standard deviation of the data. The F - test can also be used to test the hypothesis that the samples have been drawn from populations with the equal variance.\n\n3. The data should be measured, at the very least, on an interval scale. Even this is not always easy to determine, but a useful "rule of thumb" is to compare two scores, (say) a 5 and a 10, and to ask yourself if a score of 10 means that there is exactly twice as much of that particular attribute compared with a score of 5. If the data was temperature, then this is obviously the case, but what if the data represented a measure of patient satisfaction. A score of 10 may indicate more satisfaction than a 5, but not necessarily twice as much; i.e. this may be an ordinal scale rather than interval.\n\nIt is not always easy to know whether the data should be treated as parametric or non-parametric, and where possible always look at how other researcher have treated similar data. Of course it does not follow that they were correct in their assumptions, but at least if the research has been published, it will have been scrutinised by other researchers.\n\nA further complication is that some statistical tests, e.g. the t - test is considered as "robust". This means that even if one of the assumptions is doubtful, then it is still possible to use the t - test.\n\nIf you have any doubts about the nature of your data, then it is safer to opt for the non-parametric test, because they do not require these assumptions of the data. Non-parametric tests are a little less sensitive than their parametric equivalent, and therefore they may not detect small differences between groups. You can't go wrong choosing a non-parametric test, but you may be accused of using the wrong sort of test if you choose a parametric test, when the nature of the data does not warrant it.
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experiment is replicated. data anaylsis has more got to do with experiments than treatments.
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If the outcome of your experiment is statistical then it is not possible to tell the exact value of your result. In this case you can give an interval that will, with certain confidence, contain your result values. If, say, 95% of your values will be contained in a given interval we say this is a confidence interval containing 95% of values, or this is 95% confidence interval. If you know (or assume) statistical distribution of the result values, the confidence interval can be estimated from few result values. For instance if you have more than 30 values you can estimate avarage and variance and use Gaussian distribution to obtain confidence interval. For less than 30 values the student distribution is used.
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statistical methods are used to quantify quality in any process.  \n\nMinitab is a good software package for quality and charting that information.  Easy to use too!\n\nOf course Excel is the old standby.  Click on help to get a list of statistical functions.\n\nGood luck!
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Expanding on the above response on the Pareto principle, you can also demonstrate fractals, e.g. if 80% of the sales come from 20% of the customers, 80% of the sales from those 20% will come from 20% of those 80% assuming a large enough set.  Confusing?  I love fractals and think your question is awesome.
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what statistical informations can be extracted from difference between actual and estimate data?and how?
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Just enter "statistical anomaly" in the search bar on the Yahoo home page for a real answer.
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Load the Analysis Toolpak add-in and then use this formula, it's shorter:\n\n=A1/GCD(A1, B1)&":"&B1/GCD(A1, B1)
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Principal components analysis is a statistical technique to find the important variables (or linear combination of variables) in a set of data.\n\nYou need a specialised computer program to solve practical problems, but a good tutorial can be found here:\n\nhttp://csnet.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
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I know of no way to determine this without a counter. Go to link below for quality counter that is hidden from viewer's site. FREE and gives you great reporting.