Please help
My name is Adil Abubakar and i am a student.and seek help . I have a question if anyone can help, please respond to [EMAIL PROTECTED] Person A did research on a total of 4500 people and got the follwoing results Q How many hours do you spend on the web 0-7 8-15 15+ 18% 48% 34% Q. Do you read a privacy policy before signing on to a web site The answers were 1= Strongly Agree 2= Agree 3= Neutral 4= disagree 5= strongly disagree 9% 17% 20%32% 22% respectively Another person asked the the same questions from a 100 people and got the same results in % terms? Can it be shown via CI that the result is consitent with the expectations created by the previous survey? Also can it be argued that the subjects have been subjected to the questions before. can it be asserted with statiscal significance , that if the survey is repeated on at least 100 people the result will in the same proximity of the above survey?? any help ya'll can provide will be appreciated Just the need different methodlogies Thanking you in anticipation Adil Abubakar [EMAIL PROTECTED] = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Estimating priors for Bayesian analysis
I've gone to a lot of trouble to add Bayesian adjustment in a spreadsheet for estimating confidence limits of an individual's true score when the subject is assessed with a noisy test. I specify the prior belief simply by stating a best guess of the true score, and its x% likely limits, with assumption of normality. I now realize that the adjustment is sensitive to the value of x, but how does a person know what x is for a given belief? For example, I might believe that the individual's true score is 70 units, and that the likely range is +/- 10 units. So what describes likely? 90%, 95%, 99%...? Do Bayesians have any validated way to work that out? If they don't, then the whole Bayesian edifice might just come crashing down. I put this to a Bayesian who has been helping me, but I have received no reply from him since I sent the message, so I suspect the worst. Will = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Orthogonality of Designs for Experiments
In article [EMAIL PROTECTED], Jeff [EMAIL PROTECTED] wrote: Hello, Would like to ask the design of experiment gurus to help me with the following questions: 1. why designs for experiments should be orthogonal ? The computations get easier. 2. which problems may I encounter if I use non-orthogonal design ? The nice simple ANOVA procedures are no longer valid. This does not mean that nothing can be done. -- This address is for information only. I do not claim that these views are those of the Statistics Department or of Purdue University. Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399 [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Combinometrics
Herman Rubin wrote: I also doubt whether learning to compute answers gives any insight into the concepts, except for those with good research potential, and even there it tends to confuse. It depends on what learning to compute means. (*I'm* saying this in repsonse to a comment from Prf. Rubin?!) Consider exp(i pi). I can compute it by using Euler's rule or by viewing it as the pi radians rotation of a rod of unit length in the imaginary plane. Or consider the variance. I can compute it by using the desk calculator algorithm or by summing the squares of deviations. If learning to compute means simply that one is given a formula--any formula--that is to be used without any thought of its origins, I agree. OTOH, thoughts about the method of computation can often lead to important insights. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: errors in journal articles
Hi On 3 May 2001, Warren Sarle wrote: Joel Best is a professor of sociology and criminal justice at the University of Delaware. This essay is excerpted from _Damned Lies and Statistics: Untangling Numbers From the Media, Politicians, and Activists_, just published by the University of California Press So the prospectus began with this (carefully footnoted) quotation: Every year since 1950, the number of American children gunned down has doubled. I had been invited to serve on the student's dissertation committee. When I read the quotation, I assumed the student had made an error in copying it. I went to the library and looked up the article the student had cited. There, in the journal's 1995 volume, was exactly the same sentence: Every year since 1950, the number of American children gunned down has doubled. This quotation is my nomination for a dubious distinction: I think it may be the worst -- that is, the most inaccurate -- social statistic ever. Full text: http://chronicle.com/free/v47/i34/34b00701.htm Here is the progression, culminating in 35 trillion children being gunned down in 1995, far beyond the population of the world since its inception, as Best points out in the original article. In the article he describes tracking down the original basis for the statistic. At some point, doubling _since_ 1950 got translated into doubling every year since 1950. Year# Children Gunned Down 1950 1 1951 2 1952 4 1953 8 1954 16 1955 32 1956 64 1957 128 1958 256 1959 512 1960 1,024 1961 2,048 1962 4,096 1963 8,192 1964 16,384 1965 32,768 1966 65,536 1967 131,072 1968 262,144 1969 524,288 1970 1,048,576 1971 2,097,152 1972 4,194,304 1973 8,388,608 1974 16,777,216 1975 33,554,432 1976 67,108,864 1977 134,217,728 1978 268,435,456 1979 536,870,912 1980 1,073,741,824 1981 2,147,483,648 1982 4,294,967,296 1983 8,589,934,592 1984 17,179,869,184 1985 34,359,738,368 1986 68,719,476,736 1987 137,438,953,472 1988 274,877,906,944 1989 549,755,813,888 1990 1,099,511,627,776 1991 2,199,023,255,552 1992 4,398,046,511,104 1993 8,796,093,022,208 1994 17,592,186,044,416 1995 35,184,372,088,832 Best wishes Jim James M. Clark (204) 786-9757 Department of Psychology(204) 774-4134 Fax University of Winnipeg 4L05D Winnipeg, Manitoba R3B 2E9 [EMAIL PROTECTED] CANADA http://www.uwinnipeg.ca/~clark = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Combinometrics
In article 9ctkri$fjvug$[EMAIL PROTECTED], Neville X. Elliven [EMAIL PROTECTED] wrote: David Heiser wrote: We seem to have a lot of recent questions involving combinations, and probabilities of combinations. I am puzzled. Are these concepts no longer taught as a fundamental starting point in stat? I haven't seen a Combinatorics course in a college class schedule in nearly twenty years, but combinations and their probabilities are still taught in Statistics courses [perhaps not with as much emphasis as previously]. Combinatorics used to be a standard topic in high school algebra. It is USED in probability calculations, which are USED in statistical calculations, but it is not either probability or statistics, no more than addition is. In fact, it is overdone; students have no problems with understanding equally likely, but have major problems with probability when this is not the case. I also doubt whether learning to compute answers gives any insight into the concepts, except for those with good research potential, and even there it tends to confuse. -- This address is for information only. I do not claim that these views are those of the Statistics Department or of Purdue University. Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399 [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Analysis of a time series of categorical data
On 3 May 2001 09:46:12 -0700, [EMAIL PROTECTED] (R. Mark Sharp; Ext. 476) wrote: If there is a better venue for this question, please advise me. - an epidemiology mailing list? [ snip, much detail ] Time point 1Time point 2Time point 3Time point 4 Hosts Inf Not-InfInf Not-InfInf Not-InfInf Not-Inf Tested G1-S11 14 11 4 11 1 13 2 57 G1-S27 8 12 3 14 2 15 8 69 G1-S31 246 18815915 95 G2-S43 12 12 4 10 4 14 2 61 G2-S55 105 68 7 1114 57 G2-S62 26 12 12 1116 1412 105 The questions are how can group 1 (G1) be compare to group 2 (G2) and how can subgroups be compared. I maintain that the heterogeneity within each group does not prevent pooling of the subgroup data within each group, because the groupings were made a priori based on genetic similarity. Mostly, heterogeneity prevents pooling. What's an average supposed to mean? Only if the Ns represent naturally-occurring proportions, and so does your hypothesis, then you MIGHT want to analyze the numbers that way. How much do you know about the speed of expected onset, and offset of the disease? If this were real, It looks to me like you would want special software. Or special evaluation of a likelihood function. I can put the hypothesis in simple ANOVA terms, comparing species (S). Then, the within-Variability of G1 and G2 -- which is big -- would be used to test the difference Between: according to some parameter. Would that be an estimate of maximum number afflicted? -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Inference by Bootstrapping
I am fooling around with a paper that talks about how to do inferences, like constructing confidence intervals, with the bootstrap method for inference... because the assumption of i.i.d erros is reasonable... also... it is unlikely that the cumulative distribution functions of our estimators are approximately normal. He also says ... we have to ensure that the residual errors are not correlated. If the errors exhibit some correlation, then a transformation of the residuals is in order. S-PLUS has functions for this, but MATLAB does not. Can anyone provide an m-file, snippet, reference or discussion? He references: Efron (1982) The Jacknife, the Bootstap and Other Resampling Plans, Philadelphia: Society for Industrial Applied Mathematics. Seber and WIld (1989) Nonlinear Regression, New York, John Wley sons Shao and Tu (1995) The Jacknife and Boootstrap, New York, Springer Michael Robbins, CFA Director, Debt Capital Markets CIBC World Markets Corp., Canadian Imperial Bank of Commerce New York, NY USA [EMAIL PROTECTED] , [EMAIL PROTECTED] = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Q: Arithmetic, Harmonic, Geometric, etc., Means
Stanley110 wrote: Ladies and Gentlemen, What is the physical significance or meaning regarding a manufacturing process whose output over an extended period of time has the same value for the arithmetic, geometric and harmonic mean of a property, its purity, for example? ... Or if any two are same and the third is different? If any two are the same, the significance is that the output is CONSTANT. For instance, for the arithmetic and geometric means: (A) if n=2, AM = GM = x_1 = x_2. Proof: sqrt(x_1 x_2) = (x_1 + x_2)/2 = x_1 x_2 = x_1^2/4 + x_1 x_2/2 + x_2^2/4 =0= x_1^2/4 - x_1 x_2/2 + x_2^2/4 = (x_1 - x_2)^2 = x_1 = x_2 (B) if n is a power of 2, by induction all the x_i are equal. (C) even if n isn't a power of 2 a bit of algebra shows that the same result holds (forgive me for omitting the details but it's late in the day and I have to get home to my family.) Similar results hold for the other pairs of means, left as an exercise (quite a pleasant one) for the curious. If any of the data differ, there are inequalities (proved as above) that show that the AM GM HM in all cases. Analogous results hold for distributions. -Robert Dawson = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
No Subject
Why do articles appear in print when study methods, analyses, results, and conclusions are somewhat faulty? [This may be considered as a follow-up to an earlier edstat interchange.] My first, and perhaps overly critical, response is that the editorial practices are faulty. I don't find Dennis Roberts' "reasons" in his 27 Apr message too satisfying. I regularly have students write critiques of articles in their respective areas of study. And I discover many, many, ... errors in reporting. I often ask myself, WHY? I can think of two reasons: 1) journal editors can not or do not send manuscripts to reviewers with statistical analysis expertise; and 2) manuscript originators do not regularly seek methodologists as co-authors. Which is more prevalent? For whatever it is worth ... Carl Huberty
Re: Orthogonality of Designs for Experiments
Since other respondents have given the official answer which is an oversimplification that has become dogma, and is too often offered up without adequate explanation. For the most part the desire for absolute orthogonality comes from the pre-computer era when it was difficult to design and analyze non-orthogonal experiments. Experiments have many purposes, two of which are: (1) to predict a response, and (2) to investigate the effect of a factor. For prediction (1) there is no need for orthogonality at all, however the designs that produce the most information for a given sample size tend to orthogonality, and indeed in the limit are orthogonal. For (2), the aim is to test a hypothesis about a factor, and the correlation of the statistic used with other statistics in the model is of minor importance: all tests statistics for effects in a given experiment are dependent (orthogonal or not) -- they involve the same error estimate; however, as in (1) those designs that produce the most powerful tests tend to orthogonality. If you use a design constructed to achieve some respectable measure of efficiency, then you will have no problem in the analysis unless you insist on doing the calculations by hand. Jeff wrote: Hello, Would like to ask the design of experiment gurus to help me with the following questions: 1. why designs for experiments should be orthogonal ? 2. which problems may I encounter if I use non-orthogonal design ? Thank you in advance. -- Jeff -- Bob Wheeler --- (Reply to: [EMAIL PROTECTED]) ECHIP, Inc. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re:
Carl Huberty wrote: Why do articles appear in print when study methods, analyses, results, and conclusions are somewhat faulty?... I can think of two reasons: 1) journal editors can not or do not send manuscripts to reviewers with statistical analysis expertise; and 2) manuscript originators do not regularly seek methodologists as co-authors. Which is more prevalent? I would say that both are more or less necessary conditions for the appearance of a statistically illiterate paper. Neither is sufficient. Any difference in prevalence would be hard to spot, as the affected papers would be (in case 1\2) well-written but as pearls cast before swine; and (in case 2\1) sometimes well-written despite the lack of consultation, and sometimes rejected. 1 1/2 of these cases are hard to spot! -Robert Dawson = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Orthogonality of Designs for Experiments
This is a multi-part message in MIME format. --DBD3C05A63C0BFD0F8B0E075 Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit hello, Orthogonality is very important because it is an insurance that the estimation of the effect of a factor is not dependant or the other factors. For example, with orthogonality, you will have the same estimation for factor A if you keep or remove factor B of the model. In other words, your factor A is not depending of the other factors, so you are more confident in the estimation. Of course, you can analyze with no orthogonality, that is what we do when analyzing databases. But one of the goal of Designs for Experiments is to achieve orthogonality. Some Designs for Experiments are not orthogonal but are as close as possible to orthogonality. They are called D-optimal designs and can be used when you have a lot of levels for each factor, so standard design do not work. FR. Jeff wrote: Hello, Would like to ask the design of experiment gurus to help me with the following questions: 1. why designs for experiments should be orthogonal ? 2. which problems may I encounter if I use non-orthogonal design ? Thank you in advance. -- Jeff = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = --DBD3C05A63C0BFD0F8B0E075 Content-Type: text/x-vcard; charset=us-ascii; name=francois.bergeret.vcf Content-Transfer-Encoding: 7bit Content-Description: Card for Francois Bergeret Content-Disposition: attachment; filename=francois.bergeret.vcf begin:vcard n:Bergeret;Francois tel;work:33-561191205 x-mozilla-html:FALSE org:Motorola;Device Engineering, MOS20 adr:;; version:2.1 email;internet:[EMAIL PROTECTED] title:Statistician and Six Sigma Black Belt x-mozilla-cpt:;-28000 fn:Bergeret, Francois end:vcard --DBD3C05A63C0BFD0F8B0E075-- = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Please help
I rather think the problem is not adequately defined; but that may merely reflect the fact that it's a homework problem, and homework problems often require highly simplifying assumptions in order to be addressed at all. See comments below. On Fri, 4 May 2001, Adil Abubakar wrote: My name is Adil Abubakar and i am a student and seek help. snip if anyone can help, please respond to [EMAIL PROTECTED] Person A did research on a total of 4500 people and got the following results: Q. 1. How many hours do you spend on the web? 0-7 8-15 15+ 18% 48% 34% Q. 2. Do you read a privacy policy before signing on to a web site? 1=Strongly Agree 2=Agree 3=Neutral 4=disagree 5=strongly disagree 9% 17% 20% 32%22% If this were a research situation, or intended to reflect practical realities, there would also be information about the relationship between the answers to Q. 1 and the answers to Q. 2. This information might be in the form of a two-way table of relative frequencies, or (with suitable simplifying assumptions on the variables represented by Q.1 and Q.2) as a ccorrelation coefficient. Without _some_ information about the joint distribution, I do not see how one can hope to address the questions posed below. Another person asked the same questions of 100 people and got the same results in % terms. Can it be shown via CI that the result is consistent with the expectations created by the previous survey? If the % results were indeed the same (so that all differences in corresponding %s were zero), it would not be necessary to use a CI (by which I presume you mean confidence interval) to show consistency. (HOWEVER, even identical % results do not imply consistency, unless at the same time the joint distribution were ALSO identical; and you do not report information on this point.) OTOH, if the results were merely similar but not identical, you would want some means of assessing the strength of evidence that resides in the empirical differences. That in turn depends on the assumptions you're willing to make about the two variables: do you insist on treating the responses as (ordered) categories, or would you be willing, at least pro tempore, to assign (e.g.) codes 1, 2, 3 to the responses to Q. 1, use the codes 1, 2, 3, 4, 5 supplied for Q. 2, and treat those values as though they represented approximately equal intervals? Also can it be argued that the subjects have been subjected to the questions before? Not sure what you mean by this question. If you know that the Ss have indeed been asked these questions previously (are they perhaps a subset of the original 4500?), no arguing is needed; although what this would imply about the results is unclear. If you mean, do the identical (or at least consistent) results imply that the Ss must have encountered these same questions previously, I do not see how that can be argued, at least not without more information than you've so far provided. Perhaps more to the point, why would such an argument be of interest? Can it be asserted with statistical significance, that if the survey is repeated on at least 100 people the result will [be] in the same proximity of the above survey?? No. I suggest you look closely at the definition of statistical significance: the term is quite incompatible with the assertion you propose. (If you don't see that, you might bring a focussed version of the question back to the list. If you do see that, you may still have some question that is more or less in the same ball-park as the question you've asked here, and you may wish to bring the revised question to our attention.) any help ... will be appreciated. Just need the different methodologies. Yes; but for which questions, exactly? -- DFB. Donald F. Burrill [EMAIL PROTECTED] 348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED] MSC #29, Plymouth, NH 03264 603-535-2597 184 Nashua Road, Bedford, NH 03110 603-472-3742 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Orthogonality of Designs for Experiments
Short answers below; which may or may not adequately address the lurking questions you had in mind. On Fri, 4 May 2001, Jeff wrote: Would like to ask [for] help with the following questions: 1. why designs for experiments should be orthogonal ? So that results for each factor, and each interaction between factors, will be mutually independent. 2. which problems may I encounter if I use non-orthogonal design ? Same kinds of problems you encounter in the general multiple regression situation: apparent size of effect of any predictor (or factor) will depend on the presence or absence of other predictors in the model, and also on the sequence in which the several predictors (factors and their interactions) are considered in the statistical model. -- DFB. Donald F. Burrill [EMAIL PROTECTED] 348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED] MSC #29, Plymouth, NH 03264 603-535-2597 184 Nashua Road, Bedford, NH 03110 603-472-3742 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: errors in journal articles
Warren Sarle wrote: It reminds me of the recent headline in The Sunday Times (a leading UK newspaper) that taxes had tripled under the present UK government. As a bonus, the tax level when the government took power, and reported in the article as part of the argument, was something around 37% of GDP (the measure used throughout the article). Thom = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Estimating priors for Bayesian analysis
On 4 May 2001 04:11:23 -0700, [EMAIL PROTECTED] (Will Hopkins) wrote: For example, I might believe that the individual's true score is 70 units, and that the likely range is +/- 10 units. So what describes likely? 90%, 95%, 99%...? Do Bayesians have any validated way to work that out? If they don't, then the whole Bayesian edifice might just come crashing down. I put this to a Bayesian who has been helping me, but I have received no reply from him since I sent the message, so I suspect the worst. It's hard to get the value very accurately, but the appropriate way is to look at betting behaviour. If you're claiming a 50% belief that the value is between 60 and 80, then you should be indifferent to accepting a bet at even odds of the value being inside or outside that interval. For other levels, it would be a bet with different odds: e.g. you'd be willing to offer or accept 9 to 1 odds that it is in your 90% interval. Of course, there are other psychological things affecting the decision to accept or reject such a bet (can I afford to lose? Is winning worth the trouble of thinking about it? Do I think gambling is immoral? etc.), but the idea of indifference to each alternative is the key idea. Duncan Murdoch = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Estimating methods in SEM
In article [EMAIL PROTECTED], Kai Arzheimer [EMAIL PROTECTED] wrote: [EMAIL PROTECTED] (Rodney Carr) writes: The problem I am having is that I'm not sure what estimating method to use. EQS implements a number of different methods (Maximum Likelihood, Least Squares, GLS, etc). Unfortunately they give quite different results. Actually, LS gives fit indices that are fairly high, but none of the others do (so I'd like to use the LS method!). But I can't find any references that explain which method should be used. Please, do you have any ideas for where I might look for advice? I did not notice the earlier article. The question is what is wanted, and why. If one wants to come up with estimates of quantities based on current values of other quantities, least squares and related methods are quite appropriate. If one wants to understand what is happening structurally, least squares is likely to give excessively high fits. A VERY old example is that of estimating the consumption function, C = \alpha + \beta * Y + error, Y being income. Now if one wants to come up with an estimate of this year's consumption from this year's income under unchanged conditions, least squares is fine. But if one wants to estimate the effect of a government making grants to people, the structural value of \beta, not the regression value of the LS coefficient \gamma, is what is wanted. -- This address is for information only. I do not claim that these views are those of the Statistics Department or of Purdue University. Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399 [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Orthogonality of Designs for Experiments
Herman Rubin wrote: In article [EMAIL PROTECTED], Jeff [EMAIL PROTECTED] wrote: Hello, Would like to ask the design of experiment gurus to help me with the following questions: 1. why designs for experiments should be orthogonal ? The computations get easier. Also, the interpretation is usually more straightforward. 2. which problems may I encounter if I use non-orthogonal design ? The nice simple ANOVA procedures are no longer valid. This does not mean that nothing can be done. You may also encounter software that analyzes the data improperly! = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re:
At 09:44 AM 5/4/01 -0700, Carl Huberty wrote: Why do articles appear in print when study methods, analyses, results, and conclusions are somewhat faulty? [This may be considered as a follow-up to an earlier edstat interchange.] My first, and perhaps overly critical, response is that the editorial practices are faulty. I don't find Dennis Roberts' reasons in his 27 Apr message too satisfying. i was not satisfied with my own list either but, these are reasons why screw ups do occur I regularly have students write critiques of articles in their respective areas of study. And I discover many, many, ... errors in reporting. I often ask myself, WHY? I can think of two reasons: 1) journal editors can not or do not send manuscripts to reviewers with statistical analysis expertise; unfortunately ... an editor has to beg sometimes to get reviewers and, sometimes ... beggars can't be choosers ... this is the reality of journal article submission reviewing ... in addition ... a paper about say ... topic A ... has both content and methods ... and, you cannot always just find a person with skills in both ... so, what are you to do? you have to get 2/3 people to AGREE to review a paper ... and, we know that these are not all in tune to the same things ... thus, one might focus on methods/data ... another might focus on content theme ... and 2) manuscript originators do not regularly seek methodologists as co-authors. well, put yourself in the place of an untenured faculty member ... trying to get HIS/HER name as a sole author on sufficient stuff ... try to do it without a co-author ... you get more P and T points Which is more prevalent? For whatever it is worth ... let's put all of this in the proper perspective ... there is just FAR too much emphasis on getting papers submitted and published (especially in the social sciences ... we are NOT medicine where miraculous breakthroughs DO happen) ... the editorial load is too great for the resources at hand (free ... to boot!) ... so much of the stuff we do in the sake of scholarship is really on the fringe of quality and usefulness ... but, we put more and more pressure on faculty to be part of the game when will we wise up? we need LESS stuff done, but what's done should be of better quality over longer periods of time ... and of greater potential import ... if we pick up say most of the good journals in our field ... and honestly read papers and ask ourselves ... does this really matter? is this really important? if we are honest ... i would bet at least 50%-75% ... would be rated NO but, it goes on your VITA ... guess that is what counts, right? Carl Huberty == dennis roberts, penn state university educational psychology, 8148632401 http://roberts.ed.psu.edu/users/droberts/drober~1.htm = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Q: Arithmetic, Harmonic, Geometric, etc., Means
Ladies and Gentlemen, What is the physical significance or meaning regarding a manufacturing process whose output over an extended period of time has the same value for the arithmetic, geometric and harmonic mean of a property, its purity, for example? What is the physical significance or meaning if the different means are not the same? Or if any two are same and the third is different? Or if the different means agree for one property associated with the manufacturing process and not another? And can you direct me to literature or texts that address this subject? Please reply to this newsgroup and to me at [EMAIL PROTECTED]. Thank you. Stan Alekman = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Combinometrics
David Heiser wrote: We seem to have a lot of recent questions involving combinations, and probabilities of combinations. I am puzzled. Are these concepts no longer taught as a fundamental starting point in stat? I haven't seen a Combinatorics course in a college class schedule in nearly twenty years, but combinations and their probabilities are still taught in Statistics courses [perhaps not with as much emphasis as previously]. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
