JSE Volume 9 Number 2 now available
I am pleased to announce the publication of the Volume 9, Number 2 issue of the Journal of Statistics Education (JSE). JSE is an official electronic journal of the American Statistical Association (ASA), and is available through the ASA Web site at: http://www.amstat.org/publications/jse/ The contents of the current Volume can be accessed directly at: http://www.amstat.org/publications/jse/contents_2001.html I have appended a list of the articles that appear in this July 2001 issue to the end of this message. I hope that you will find this issue of JSE to be interesting and informative. It includes articles of interest to K-12 teachers, undergraduate teachers, and graduate statistics teachers as well. Please note that access to JSE is free. We are soliciting advertising that will appear on the index pages of the site. If you are interested in placing a banner or button advertisement in JSE, please contact JSE at: [EMAIL PROTECTED] The next issue of JSE will appear article by article on the Web site as the issue is constructed in the coming months. The complete issue will be announced in November 2001. Thank you for reading and for exploring JSE! I apologize if you receive this announcement more than once. Statistically yours, Tom Short Editor, Journal of Statistics Education www.amstat.org/publications/jse/ [EMAIL PROTECTED] -- Journal of Statistics Education Volume 9, Number 2 July 2001 Articles Dexter C. Whittinghill and Robert V. Hogg A Little Uniform Density With Big Instructional Potential Robert F. Bordley Teaching Decision Theory in Applied Statistics Courses Linda S. Hirsch and Angela M. O'Donnell Representativeness in Statistical Reasoning: Identifying and Assessing Misconceptions Lorraine Garrett and John C. Nash Issues in Teaching the Comparison of Variability to Non-Statistics Students Christopher J. Malone and Christopher R. Bilder Statistics Course Web Sites: Beyond syllabus.html Departments Teaching Bits: A Resource for Teachers of Statistics Deborah J. Rumsey From the Literature on Teaching and Learning Statistics William P. Peterson Topics for Discussion from Current Newspapers and Journals Datasets and Stories Singfat Chu Pricing the C's of Diamond Stones -- = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
log
hi what is the opposite of a log? If you do lg10 of 3 in spss, it gives you a number. how can i take this number and have as a solution the initial one (3) its easy but i cannot remeber = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Thomas' Fuzziness and Probability
You raise a good question. As the author in question, may I respond as follows: There really is a dualistic relationship between fuzziness and probability. They are distinct concepts, but I would argue that there is a real sense in which the former *derives* from the latter. That thought is anathema to many fuzzicists who fear the implication that if so, then there is "nothing new" to fuzzy, as falling ultimately under the ambit of probability theory. I don't think so. There is plenty new semantics in the fuzzy set theory of which probabilists have been blissfully unaware, and which in fact helps to illuminate some problems in the foundations at least of statistical inference theory. The duality is precisely analogous to the duality that exists between probability and likelihood. A probability distribution over sample space gives rise to a likelihood function over parameter space. The one is a set function, the other a point function, and they pertain to different domains. In the case of natural language semantics, it is precisely because language-use is a chance phenomenon, even in a calibrational setting, that there is fuzziness in the meanings of terms. More precisely, uncertainty in the calibrational response variable, either yes or no, to a series of calibrational propositions such as "would you use the term 'tall' to describe the height value for which John stands as exemplar, in the context of heights of adult males?" gives rise to a semantic likelihood function over height space, induced by probabilistic response uncertainty over calibrational response (yes/no) space, it being understood that many different height value exemplars (Jim, Peter, Paul, etc.) are similarly presented in calibrational setting. The affirmation probability (Bernoulli parameter) varies as a point function over height space, as opposed to a set function over calibrational response space. Thus the calibrational response rates traced out with respect to the height variable is in no sense a probability distribution, since it would in general not sum to unity; nor is it a frequency distribution over the height values of the adult male population that in an obvious sense may be rendered as a probability distribution. All you have is a characteristic function that describes, for various height values, the rate at which a relevant speaker population would use the term "tall" to describe the height values in question. It is a membership function in the obvious Zadehian sense of a point function ranging from 0 to 1, though Zadeh may or may not approve of the manner in which it is obtained. It could also have been called a *semantic likelihood* function, or a *characteristic* function of the *term* tall, as distinct from the *membership* function characterizing the associated set of tall *men*. I like the term semantic likelihood because it gets to the heart of the matter in my view. In a non-calibrational setting, eg. the use of the term "tall" by a rape victim in court to describe the height of her attacker, it is the calibrational response uncertainty in terms purely of language-use, that leads to semantic uncertainty about the precise height to which she refers. The semantic likelihood function traces out the relative possibility of various height-value hypotheses consistent with her description of her attacker as "tall". In ordinary discourse and comprehension, we don't need to have it spelt out, obviously. But is in some sense there. This analogy leads to the perhaps startling conclusion that the (absolute) likelihood function familiar from statistical inference theory is in some sense also a membership function! It certainly satisfies the minimum condition that it range on the [0,1] interval. But semantically as well, it may be construed simply as the term corresponding to what the data "say" about some unknown parameter of interest. The greater the quantity of data, the more precise, or less fuzzy, is the characterization of the unknown parameter of interest. Statistical sample data relevant to inference concerning the value of model parameters are therefore analogous to fuzzy natural-language statements about things like people's height. It is also analogous to measurement, which for continuous attributes is fuzzy in general, since no measurement may be made literally to an infinite number of decimal places, and at the digit where uncertainty enters, accidental and systematic errors of measurement, exactly like those associated with the calibrational proposition with which we started for characterizing the term "tall", may conspire to render the range of uncertainty in the ultimate digit of measurement fuzzy rather than crisp. In all of this there is an essential and unavoidable duality and interplay between uncertainty of the probabilistic sort, and uncertainty of the fuzzy (also likelihood) sort. To treat these two kinds of uncertainty in this fashion is not to exalt one over the other, rather to recognize that they a
Re: log
log10(3) = 0.477121 10^0.477121.. = 3 (i.e. 10 raised to the exponent 0.477121...) If you are using log10, it's always easier to use multiples of 10 to explain so maybe this will help even more: log10(1000) = 3 10^3 = 1000 Hope this helps "ToM" <[EMAIL PROTECTED]> wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > hi > > what is the opposite of a log? > > If you do lg10 of 3 in spss, it gives you a number. how can i take > this number and have as a solution the initial one (3) > > its easy but i cannot remeber = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: log
On 31 Jul 2001, ToM wrote: > what is the opposite of a log?[logarithm] An antilog [properly, antilogarithm]. Equivalently, 10 to that power (if, as in your example, you are taking logarithms to the base 10); or e to that power (if you are taking natural logarithms), which is also called the exponential function, exp( ). > If you do lg10 of 3 in spss, it gives you a number. how can i take > this number and have as a solution the initial one (3)? lg10(3) = 0.47712. In SPSS-speak, 10**(0.47712) = 2.9 For natural logarithms, ln(3) = 1.0986, exp(1.0986) = 2.6 Donald F. Burrill [EMAIL PROTECTED] 184 Nashua Road, Bedford, NH 03110 603-471-7128 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Distribution function or moments
Hi All, Is there any hope to calculate the distribution or at least first two moment of the following random variable z = x*N(A*x+B) where x is a LOGNORMAL random variable with parameters MU and SIGMA N() is a standard NORMAL CUMULATIVE probability function and A and B are positive constant. In fact the real problem is for more complicated like z1 = (x^C)*(N(A*x+B)^D)*(N(F*x+G)^E) where A,B,C,D,E,F,G are constants and C,D,E are NATURAL numbers like 1,2,3 Thanks for any reference or suggestion. Alex = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
New Branche of Medicine
MEDICINE HORIZONS - newsletter Integration of Ancient Wisdom and Modern Scientific Technologies Leading to a New Branch of Medicine No innovation has gained such a fast foothold and recognition among medical experts as the utilisation of the natural laws of harmony. When one considers that the term Medical Resonance Therapy Music was only coined 14 years ago, and then looks at the present level of documentation on research and developments in this new branch of medicine, and the statements of leading medical experts from many fields such as hormone research, gynaecology, paediatrics, dermatology, research into headaches, intensive care medicine etc. right up to international recognition as the most successful "anti-stress remedy in the world" at the International Conference "Society, Stress and Health" of the World Health Organisation (WHO), then it is also worthwhile examining the causes for such enormous medical success more closely. Interesting links: Medical Resonance Therapy Music http://www.medicalresonancetherapymusic.com Scientific Music Therapy - Research http://www.scientificmusictherapy.com Music as Data Carrier of the Laws of Harmony Nature´s Laws of Harmony in the Microcosm of Music Chronobiological Aspects of Music Physiology International Experts http://www.digipharm.com Micro Music Laboratories http://www.micromusiclaboratories.com MEDICINE HORIZONS http://www.medicinehorizons.com p.s.: this message was sent to you in the genuine belief that it is of professional interest to you. If you do not want to receive further information please simply send us an email: mailto:[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/ =
