what is reliability R, in statistics?
399803
In statistics reliability is the consistency of a set of measurements or measuring instrument.\n\nReliability theory is a general theory about systems failure. It allows researchers to predict the age-related failure kinetics for a system of given architecture (reliability structure) and given reliability of its components. Reliability theory predicts that even those systems that are entirely composed of non-aging elements (with a constant failure rate) will nevertheless deteriorate (fail more often) with age, if these systems are redundant in irreplaceable elements. Aging, therefore, is a direct consequence of systems redundancy. Reliability theory also predicts the late-life mortality deceleration with subsequent leveling-off, as well as the late-life mortality plateaus, as an inevitable consequence of redundancy exhaustion at extreme old ages.\n\nThe failure of mechanical devices such as ships, trains, and cars, is similar in many ways to the life or death of biological organisms. Statistical models appropriate for any of these topics are generically called "time-to-event" models. Death or failure is called an "event", and the goal is to project or forecast the rate of events for a given population or the probability of an event for an individual.
249369
Is the term you are looking for "probability"?\n\nI hope this helps.
933392
Probability in statistics would agree with your claim to a point of certainty
121605
I'm assuming that you mean introductory statistics.  This online textbook is very good, it makes good use of hyperlinks and also provides exercises and answers for each chapter.  It also has links to useful sites and books for the various topics.  \nhttp://davidmlane.com/hyperstat/\nHope this is helpful :-)
793664
What's your problem - your in college - do your own homework (this son of a statistician is unimpressed).  If you don't know how to construct a confidence interval look at http://facweb.bhc.edu/EastCampus/leeb/ma108/06_01w/tsld032.htm\n\nGood Luck!!!
1011608
I really like R.  CRAN (the comprehensive R archive network) has packages that might help you out.\n\nTry a Yahoo Search such as:\ncanonical analysis CRAN\nhttp://search.yahoo.com/search?ei=utf-8&fr=slv8-wdgt&p=canonical%20analysis%20CRAN\n\nIt turns up results such as:\nhttp://www.bioconductor.org/CRAN/src/contrib/Views/Environmetrics.html\nhttp://www.bioconductor.org/CRAN/doc/packages/pcurve.pdf#search='canonical%20analysis%20CRAN'
17841
You might try DAP. You can find it here along with a lot of other free statistical software.\nhttp://members.aol.com/johnp71/javasta2.html
446411
Usually when  you pull a sample, you know the size of the sample you pulled.  Otherwise, how would you calculate any of the test statistics much less the margin of error?
929935
variations matter greatly in interpreting data.  Something might seem like it is returning significantly different results than the control but stat evaluation may show that the variations are minimal at best
837986
http://www.statsoft.com/textbook/stathome.html
228637
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!
466068
If you are referring to validity, do a principal components analysis.  Reliability use Cronbach's alpha.  Cross reference your results from PCA with Cluster Analysis.  I am a sas user so I only no the code for sas which is:\n\nproc factor data=females\nsimple\nmethod=prin\npriors=one\nmineigen=1\nscree\nrotate=varimax\nround\nflag=.5;\nvar rate1-rate10;\nrun;\n\nRobert DeVellis; Scale Development: Theory and Application 2nd edition.
558645
These are my steps; it might be different for different people.\n\nCritical value approach:\n\n1. State null and alternative hypothesis.\n2. Compute the test statistic.\n3. Find the critical value and rejection region.\n4. Make you conclusion.\n\nP-value approach:\n\n1. State null and alternative hypothesis.\n2. Compute the test statistic.\n3. Find the p-value.\n4. Make your conclusion.
286964
If a random experiment has two possible outcomes, success with proability  p and failure with probability q=1-p. And the experiment is repeated until a success happens. The number of trials before the success is a random variable  with density function\nf(x)=q^(x-1) * p     is called the geometric distribution.\n\n\nhttp://library.thinkquest.org/10030/6dpdgd.htm\n\nhttp://en.wikipedia.org/wiki/Geometric_distribution
461896
Wikipedia reveals all.\n\nThe third link lists online statistics textbooks.  The Statistics Homepage is especially good.
766757
Probability (Random) Sampling  \nAllows use of statistics, tests hypotheses  \nCan estimate population parameters  \nEliminates bias  \nMust have random selection of units \n \nNon-Probability (Non-Random) Sampling\nExploratory research, generates hypotheses\nPopulation parameters are not of interest\nAdequacy of the sample can't be known\nCheaper, easier, quicker to carry out\n\nSimple random sample: Each unit in the population is identified, and each unit has an equal chance of being in the sample. The selection of each unit is independent of the selection of every other unit. Selection of one unit does not affect the chances of any other unit.\nSystematic random sampling: Each unit in the population is identified, and each unit has an equal chance of being in the sample.\nStratified random sampling: Each unit in the population is identified, and each unit has a known, non-zero chance of being in the sample. This is used when the researcher knows that the population has sub-groups (strata) that are of interest.\n\nNon-probability (non-random) samples:\n    These samples focus on volunteers, easily available units, or those that just happen to be present when the research is done. Non-probability samples are useful for quick and cheap studies, for case studies, for qualitative research, for pilot studies, and for developing hypotheses for future research. \nVR
211268
Regression analysis is very complicated mathematical/statistic analysis that is very difficult to describe easily.  R-squared is a measure of how closely an investment tracks the S&P 500 or other index.  While I am not a great fan of investopedia.com, they give some answers in this case.\n(These comments refer to investment analysis, regression analysis is also used in other fields.)