Verba Volant 30-01-02
Verba Volant 30-01-02, Every day a new quotation translated into many languages. _ Quotation of the day: Author - Friedrich Hegel English - have the courage to be mistaken Italian - abbiate il valore di sbagliarvi Spanish - tened el valor de equivocaros French - ayez le courage de vous tromper German - ich habe den Mut mich zu irren Albanian - kini mënçurinë të gaboni Basque - izan ezazue erratzeko adorea Bolognese - avîdi al curâg ed sbaglièruv Bresciano - ghè de jga la fórsa dé sbajà Calabrese - aviti 'u valuri di vi sbagliari Catalan - procureu tenir el coratge dequivocar-vos Croatian - budite dovoljno hrabri da priznate greke Czech - mej odvahu se mýlit Dutch - heb de moed je te vergissen Emiliano Romagnolo - faset' la stoima ad sbarlughers Esperanto - kuragu erari Ferrarese - avì al valor ad sbaiarav Finnish - uskalla erehtyä Flemish - heb de moed je te vergissen Galician - tede a coraxe de errar Hungarian - legyetek bátrak ahhoz, hogy hibázzatok Latin - errare audete Latvian; Lettish - lai tev pietiek drosmes maldities Leonese - tenéi'l xeitu d'equivocavos Mantuan - gh abièghi al coer da sbagliarav Neapolitan - tenite 'o curaggio 'e sbaglià Occitan - avetz lo coratge de vos trompar Parmigiano - ghi al corag ad sbalierev Piemontese - avèj ël coragi d'ësbaliéve Polish - miejcie odwage mylic sie Portuguese - tende a coragem de errar Reggiano - avìdi al curag ed sbaglierov Roman - avetece er core de' sbajavve Romanian - aveti curajul sa gresiti Sardinian (Limba Sarda Unificada) - tenide s'alientu de bos faddire Sicilian - avìti 'u curaggiu di sbagghiari Slovak - majte odvahu zmýlit sa Swedish - var modig nog att kunna ta fel Turkish - yanilmis olma cesaretiniz olsun Venetian - serchè de aver el corajo de sbajarve Zeneize - aggiæ l'ardî de inarâve _ All languages, please click on this link http://www.logos.net/owa-l/press.frasiproc.carica?code=507 _ To unsubscribe from Verba Volant, please follow this link: http://www.logos.net/owa-l/press.rol_ml.verbavolant1?lang=en and write in the empty field next to unsubscribe the email address that you find after TO: in the Verba Volant emails alternatively write to the following address: [EMAIL PROTECTED] always copying the EMAIL address written after X-RCPT-TO:
Re: How to test f(X , Y)=f(X)f(Y)
Linda [EMAIL PROTECTED] wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... I have 1000 observations of 2 RVs from an experiments. X is the independent variable and Y is the dependent variable. How do I perform the test whether the following statement is true or not?? f(X,Y)=f(X)f(Y) Linda f(X,Y)=f(X)f(Y) if and only if X and Y are independent, if they are independent they are also unrelated, so if the correlation coefficient is not equal (or at least not not very near) to zero then the statement is not true. However if the correlation coefficient is zero the statement can be false, unless X and Y are normal, hence you can do a scatter plot, if you see a pattern the variables are not independent. For example if X is a variable with simmetrically distributed around zero, that implies E(X)=0, and Y=X^(2n) , n0, say Y=X^2, then: E(X)E(Y)=0, because E(X) = 0 E(XY)=E(X^3)=0 and then X and Y are unrelated but much dependent. Independence means: E(g(X)h(Y))=E(g(X))E(h(Y)) for every function g and h where the integrals exist, it is a strong condition. There are also softwares that automatically try to find patterns between samples of two variables and if they did not send a warning of a probable indipendence. That's all, I hope someone else gives to you a more precise answer. Bruno = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: Definition of Relationship Between Variables (was Re: Eight
In some disciplines (psych, ed, nursing, etc), Research Methods is taught as another course. However, both courses should identify the relation between course contents. G Robin Edwards wrote: In article uon58.2302$[EMAIL PROTECTED], Donald Macnaughton [EMAIL PROTECTED] wrote: At great length, and with many quotes, on a very interesting topic. I fear I may have missed the original postings on this thread, though. There is however one area that seems not to have been addressed. This is the field of statistical design of experiments. The word design appears just once in the article, in connections with the t test. I am really disappointed that there was not some emphasis on the value of correctly designed experiments at all levels in the sciences, both hard and soft. As a non-statistician, non-mathematician and non-academic (merely a practical chemist who spent his entire working life in industry) I introduced myself to statistics via the experiment design route using Brownlee's Industrial Experimentation in 1956. The elegance of simple ANOVA became apparent even to me, but the introduction to the ideas of design were even more exciting. Many practical scientists at bench level can I feel readily appreciate many of the concepts of design and thus the notion of constructing a model which their experimental work will address and hence prove or fail to support the underlying hypotheses. This I feel is the way to get otherwise sceptical scientists and engineers into the way of considering their practical real-life problems as ones that require an holistic approach. Few industrial investigations are single variable problems. My belief and experience is that too much emphasis on the formal mathematical exposition of statistical ideas - however relevant they are to statistics majors - serve only to distance the experimental scientist from the huge advantages to be gained from making use of designed experiments in a complex world. Quite simple examples can serve to generate acceptance and even enthusiasm for what we might regard as a rational approach but which might otherwise be discouraging for the newcomer to statistical design of experiments. I've proved this to myself time and again in an industrial context. -- Robin Edwards ZFC Ta Serious Statistical Software REAL Statistics with Graphics for RISC OS machines Please email [EMAIL PROTECTED] for details of our loan software. = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: Definition of Relationship Between Variables (was Re: Eight
experimental design, whose basic principles are rather simple, is elegant and if applied in good ways, can be very informative as to data, variables and their impact, etc. but, please hold on for a moment when it comes to humans, we have developed some social policies that say: 1. experimental procedures should not harm Ss ... nor pose any unnecessary risk 2. Ss should be informed about what it is they are being asked to participate in as far as experiments are concerned 3. Ss are THE ones to make the decision about participation or not (except in cases of minors ... we allocate that decision to guardians/parents) you aren't suggesting, are you, that for the sake of knowledge, we should abandon these principles? so you see, there are serious restrictions (and valid ones at that) when it comes to doing experiments with humans ... for so much of human behavior and things we would like to explore, we are unable to do experiments ... it is that plain and simple yes, while we may be able to gather data on many of these variables of interest in other ways ... we are rarely if EVER in the position of being able to say with any causative assurance (since we did not experimentally manipulate things) that when we do X, Y happens 208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED] http://roberts.ed.psu.edu/users/droberts/drober~1.htm = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
area under the curve
A student wants to know how one can calculate the area under the curve for skewed distributions. Can someone give me an answer about when a distribution is too skewed to use the z table? Melady
Re: area under the curve
Melady Preece wrote: A student wants to know how one can calculate the area under the curve for skewed distributions. Can someone give me an answer about when a distribution is too skewed to use the z table? You can only use the z table directly to find the area under a curve when the distribution is a standard normal distribution - that is, when it has precisely the probability density function f(x) = 1/sqrt(2 pi) exp(-x^2/2) You can use it indirectly if the distribution can be transformed into a standard normal distribution. The main cases are: general normal distribution: subtract mu and divide by sigma lognormal distribution: take logs You can use it to *approximate* the area under the curve for near-normal distributions such as binomial (N large), t (nu large), gamma (beta large), beta (both parameters large), or the sampling distribution of the mean from any population that is not too heavy-tailed (N large). Large in every case depends on parameter values and desired degree of approximation. To calculate the area under the curve for a skewed distribution you can: (a) integrate the PDF for the distribution (gamma, Weibull, etc) (b) look it up in a table (chi-squared, noncentral t etc) (c) estimate based on the percentiles of a sample. Hope this helps. Robert Dawson = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: area under the curve
unless you had a table comparable to the z table for area under the normal distribution ... for EACH different level of skewness ... an exact answer is not possible in a way that would be explainable here is one example that may help to give some approximate idea as to what might happen though . .:::. .:. .:::. .:. .:::. ::: . .:::. . +-+-+-+-+-+---C1 the above is a norm. distribution of z scores ... where 1/2 the data are above 0 and 1/2 are below : :. .::. . :. . . ... . +-+-+-+-+-+---C2 -5.0 -2.5 0.0 2.5 5.0 7.5 here is a set of z score data that is radically + skewed ... and even though it has 0 as its mean, 50% of the area is NOT above the mean of 0 ... note the median is down at about -.3 ... so, there is LESS than 50% above the mean of 0 ... this means that for z scores above 0 ... there is not as much area beyond (to the right) ... as you would expect if the distribution had been normal ... so, we can have some approximate idea of what might happen but the exact amount of this clearly depends on how much skew you have MTB desc c1 c2 Descriptive Statistics: C1, C2 Variable N Mean Median TrMean StDevSE Mean C1 1 0.0008 0.0185 0.0029 1.0008 0.0100 C2 1 0.-0.3098-0.1117 1. 0.0100 Variable MinimumMaximum Q1 Q3 C1 -3.9468 3.9996-0.6811 0.6754 C2 -0.9946 8.0984-0.7127 0.3921 At 07:27 AM 1/30/02 -0800, Melady Preece wrote: A student wants to know how one can calculate the area under the curve for skewed distributions. Can someone give me an answer about when a distribution is too skewed to use the z table? Melady _ dennis roberts, educational psychology, penn state university 208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED] http://roberts.ed.psu.edu/users/droberts/drober~1.htm = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
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Re: area under the curve
On 30 Jan 2002 08:02:51 -0800, [EMAIL PROTECTED] (Melady Preece) wrote: A student wants to know how one can calculate the area under the curve for skewed distributions. Can someone give me an answer about when a distribution is too skewed to use the z table? It would be convenient if there was a general answer. Here are some reasons why there isn't one answer: - Sometimes the accuracy of F(z)-for-F(x) is needed within a certain absolute amount (like the accuracy for a KS goodness of fit). Skewness does not index this. - Often, the accuracy of F(z) is desired within 'so-many decimals' of accuracy: two or three or more places of accuracy for both F(z) and 1-F(z). Skewness does not index this. In short: there are several ways to ask for accuracy, but there is no automatic (or pre-computed) connection that I have heard of, between skewness and the accuracy of F, for any measure of accuracy. I think I would find it interesting to see some Monte Carlo experiments. I think those should start with specific classes of distributions, and specific ranges for their parameters, and plot their 'accuracies' against the observed skewness and kurtosis. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: cutting tails of samples
Look at minitab's trimmed mean. It is a Tukey (I think) invention w/5% chopped from each end, leaving the central 90%. For the high variance, high skew, common world, a good approach. On Tue, 29 Jan 2002, Rich Ulrich wrote: On 17 Jan 2002 00:05:02 -0800, [EMAIL PROTECTED] (Hekon) wrote: I have noticed a practice among some people dealing with enterprise data to cut the left and right tails off their samples (including census data) in both dependent and independent variables. The reason is that outliers tend to be extreme. The effects can be stunning. How is this practice to be understood statistically - as some form of truncation? References that deal formally with such a practice? This is called trimming - 5% trimming, 25% trimming. The median is what is left when you have done 50% trimming. Trimming by 5% or 10% reportedly works well for your measures of 'central tendency', so long as you *know* that the extremes are not important. I don't know what it is that you refer to as 'enterprise data.' -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ = = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: cutting tails of samples
here is an example from minitab ... in moore and mccabe's book intro. to practice of statistics ... 3rd edition ... they have an example of speed of light measurements ... with newcomb in his lab on the bank of the potomac ... bouncing light bursts off the base of the washington monument ... and then collecting observations (n=66) in nanoseconds (whatever the heck that is) [NOTE: just think about newcomb back in the mid 1800s. doing this ... i wonder how many times he totally MISSED hitting the monument with his light beam and, it ended up in philly???] MTB dotp c10 c11; SUBC same. Dotplot: nanodata, trimmed . : : :.: . :.::: : . : : : . . : .:.:... -+-+-+-+-+-+-nanodata . : : :.: . :.::: : . : : : . .:.: -+-+-+-+-+-+-trimmed 24765 24780 24795 24810 24825 24840 the original data set of n=66 shows at least on extreme value at the left ... now, of 66, the 5% rule would lop off about the lower 3 and upper 3 ... which does little to the top but, DOES lop off the extreme values at the bottom i sorted the data and did that ... and the bottom distribution above is the n=60 one ... with the top and bottom 3 axed in the desc. stats below, note that the original nanodata has a mean of 24826 .. and the trimmed mean of 24827 ... of course, the trimmed mean is THE mean in the trimmed data (n=60) perhaps the more important thing is the variation (ie, sds) ... (or variances if you like those better) ... trimming cuts the index number (sd in this case) for dispersion dramatically ... the variance for example would be almost only 1/10th of the variance in the original set of nanosecond data MTB desc c10 c11 Descriptive Statistics: nanodata, trimmed Variable N Mean Median TrMean StDevSE Mean nanodata66 24826 24827 24827 11 1 trimmed 60 24827 24827 24827 4 1 Variable MinimumMaximum Q1 Q3 nanodata 24756 24840 24824 24831 trimmed 24816 24836 24824 24830 there is no CLEAR cut rule for dealing with this situation nor, guidelines for telling one if this should be done or not ... a lot depends on whether these outlying values are real ... or if we can explain them away as abberations (miscodings, etc.) so, i don't make a value judgement about if what i did for an example is good or not, i just give you this example (since i just HAPPEN to be doing this thing in my class at the moment) as an illustration At 01:44 PM 1/30/02 -0700, Harold W Kerster wrote: Look at minitab's trimmed mean. It is a Tukey (I think) invention w/5% chopped from each end, leaving the central 90%. For the high variance, high skew, common world, a good approach. On Tue, 29 Jan 2002, Rich Ulrich wrote: On 17 Jan 2002 00:05:02 -0800, [EMAIL PROTECTED] (Hekon) wrote: I have noticed a practice among some people dealing with enterprise data to cut the left and right tails off their samples (including census data) in both dependent and independent variables. The reason is that outliers tend to be extreme. The effects can be stunning. How is this practice to be understood statistically - as some form of truncation? References that deal formally with such a practice? This is called trimming - 5% trimming, 25% trimming. The median is what is left when you have done 50% trimming. Trimming by 5% or 10% reportedly works well for your measures of 'central tendency', so long as you *know* that the extremes are not important. I don't know what it is that you refer to as 'enterprise data.' -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ = = Instructions for joining and leaving
Re: how to adjust for variables
Walter Willett has a whole chapter on this subject in his book Nutritional Epidemiology. It should be considered required reading before attempting to model anything that has to do with diet. Thanks this is a really good book, not just for ppl wanting to study nutrition but surveys in general as well as confounding and modifying by multivariables. (a simple guide) He has some really earth-moving examples of errors commited in the past. As an example one group tried to find a correlation between weight and disease as: disease=weight+blood pressure+heart rate+blood cholesterol and willett points out that they found no association because the implications of weight cannot be separated from its effects on heart rate etc. Anyway I'm currently going on the definition of adjusted for 1 2 and 3 as the following equation: adjusted variable=variable^-variable (where variable-hat represents the variable predicted by 1 2 and 3 in a multivariate equation and variable is just the actual variable.) = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
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Re: area under the curve
Almost by definition, if it is skewed enough to be notable, it is too skewed to force into a symmetric z table (which is a Normal dist.) Not to be facetious, but how close is 'close enough'? If you are lucky enough to find some sort of transformation that brings it back to a Normal, then but you didn't say that. Jay Melady Preece wrote: A student wants to know how one can calculate the area under the curve for skewed distributions. Can someone give me an answer about when a distribution is too skewed to use the z table?Melady -- Jay Warner Principal Scientist Warner Consulting, Inc. North Green Bay Road Racine, WI 53404-1216 USA Ph: (262) 634-9100 FAX: (262) 681-1133 email: [EMAIL PROTECTED] web: http://www.a2q.com The A2Q Method (tm) -- What do you want to improve today?
