Technological Development in the Third World
The Financial News, September 2001 Production Mini-plants in mobile containers "...Science Network will supply to countries and developing regions the technology and the necessary support for the production in series of Mini-plants in mobile containers (40-foot). The Mini-plant system is designed in such a way that all the production machinery is fixed on the platform of the container, with all wiring, piping, and installation parts; that is to say, they are fully equipped... and the mini-plant is ready for production." More than 700 portable production systems: Bakeries, Steel Nails, Welding Electrodes, Tire Retreading, Reinforcement Bar Bending for Construction Framework, Sheeting for Roofing, Ceilings and Faades, Plated Drums, Aluminum Buckets, Injected Polypropylene Housewares, Pressed Melamine Items (Glasses, Cups, Plates, Mugs, etc.), Mufflers, Construction Electrically Welded Mesh, Plastic Bags and Packaging, Mobile units of medical assistance, Sanitary Material, Hypodermic Syringes, Hemostatic Clamps, etc. For more information: Mini-plants in mobile containers By Steven P. Leibacher, The Financial News, Editor - If you received this in error or would like to be removed from our list, please return us indicating: remove or un-subscribe in 'subject' field, Thanks. Editor 2001 The Financial News. All rights reserved.
likelihood ratio chi-square: L^2
Dear Debater, I've got two models which are nested (or hierarchical), and values likelihood ratio chi-square (L^2) for them with degrees of freedom : 1 model: L^2 =21.93 , df=16 2 model: L^2=22.13 , df=18 I read that it' posible to settle which model is better (because they are hierarchical). In a book was written: we conclude that the fit is acceptable if the increase in the L^2 is small relative to its degrees of freedom - model 2 is better. My question is: how small increase of L^2 should be relative to its degrees of freedom in order to say that model 2 fit better. Could you give me assistance or to recommend books or webside solving my problem. I'd appreciate Nathaniel = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Looking for Presenters at 2002 International Symposium on Forecasting in Dublin 6/23-6/26
I am looking for interested parties to present next June in the field of Intermittent Demand Forecasting. Of course, if you have an interest in another area I will be glad to pass your name on to for consideration. If you are interested send me an e-mail at [EMAIL PROTECTED] stating a synopsis of what your 45 minute speech would focus on. This conference is more of academic group, but applications of methodologies is also welcomed. See http://www.isf2002.org/ for more info. The conference is 6/23-6/26. They always have well organized functions with good eats and tours. Tom Reilly = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
What is a confidence interval?
Hi, I've been teaching an introductory stats course for several years. I always learn something from my students...hope they learn too. One thing I've learned is that confidence intervals are very tough for them. They can compute them, but why? Of course, we talk about confidence interval construction and I try to explain the usual 95% of all intervals so constructed will in the long run include the parameter...blah, blah. I've looked at the Bayesian interpretation also but find this a bit hard for beginning students. So, what is your best way to explain a CI? How do you explain it without using some esoteric discussion of probability? Now, here's another question. If I roll 2 dice and find the mean of the pips on the upturned faces. You can compute sample standard deviations, but if you roll 2 alike the s.d. is 0. So, you cannot compute a CI based on these samples. How would you explain? Thanks, Warren = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
E as a % of a standard deviation
re: the formula: n = (Z?/e)2 could you express E as a % of a standard deviation . In other words does a .02 error translate into .02/1 standard deviations, assuming you are dealing w/a normal distribution? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: error estimate as fraction of standard deviation
Thanks for the formula, but I was really interested in knowing what % of a standard deviation corresponds to E. In other words does a .02 error translate into .02/1 standard deviations? Graeme Byrne [EMAIL PROTECTED] wrote in message 9orn26$m80$[EMAIL PROTECTED]">news:9orn26$m80$[EMAIL PROTECTED]... This sounds like homework but I will . Anyway assume the normal approximation to the binomial can be used (is this reasonable?) then the formula for estimating sample sizes based on a given confidence level and a given maximum error is n = z*Sqrt(p*(1-p))/e where z = the z-scores associated with the given confidence level (90% in this case) p = the proportion of successes (bruised apples in this case) e = the maximum error (4% in this case) Your problem is you don't know p since that is what you are trying to estimate. Ask yourself what value of p will make n a large as possible and then you can use this worst case solution. Another solution would be to estimate (roughly) the maximum value of p (10%, 20% ...) and use it to find n. Whatever you do you should read up on Calculating sample sizes for estimating proportions in any good basic statistics text. GB @Home [EMAIL PROTECTED] wrote in message b25s7.46471$[EMAIL PROTECTED]">news:b25s7.46471$[EMAIL PROTECTED]... If you have a confidence level of 90% and an error estimate of 4% and don't know the std deviation, is there a way to express the error estimate as a fraction of a std deviation? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: What is a confidence interval?
as a start, you could relate everyday examples where the notion of CI seems to make sense A. you observe a friend in terms of his/her lateness when planning to meet you somewhere ... over time, you take 'samples' of late values ... in a sense you have means ... and then you form a rubric like ... for sam ... if we plan on meeting at noon ... you can expect him at noon + or - 10 minutes ... you won't always be right but, maybe about 95% of the time you will? B. from real estate ads in a community, looking at sunday newspapers, you find that several samples of average house prices for a 3 bedroom, 2 bath place are certain values ... so, again, this is like have a bunch of means ... then, if someone asks you (visitor) about average prices of a bedroom, 2 bath house ... you might say ... 134,000 +/- 21,000 ... of course, you won't always be right but perhaps about 95% of the time? but, more specifically, there are a number of things you can do 1. students certainly have to know something about sampling error ... and the notion of a sampling distribution 2. they have to realize that when taking a sample, say using the sample mean, that the mean they get could fall anywhere within that sampling distribution 3. if we know something about #1 AND, we have a sample mean ... then, #1 sets sort of a limit on how far away the truth can be GIVEN that sample mean or statistic ... 4. thus, we use the statistics (ie, sample mean) and add and subtract some error (based on #1) ... in such a way that we will be correct (in saying that the parameter will fall within the CI) some % of the time ... say, 95%? it is easy to show this via simulation ... minitab for example can help you do this here is an example ... let's say we are taking samples of size 100 from a population of SAT M scores ... where we assume the mu is 500 and sigma is 100 ... i will take a 1000 SRS samples ... and summarize the results of building 100 CIs MTB rand 1000 c1-c100; made 1000 rows ... and 100 columns ... each ROW will be a sample SUBC norm 500 100. sampled from population with mu = 500 and sigma = 100 MTB rmean c1-c100 c101 got means for 1000 samples and put in c101 MTB name c1='sampmean' MTB let c102=c101-2*10 found lower point of 95% CI MTB let c103=c101+2*10 found upper point of 95% CI MTB name c102='lowerpt' c103='upperpt' MTB let c104=(c102 lt 500) and (c103 gt 500) this evaluates if the intervals capture 500 or not MTB sum c104 Sum of C104 Sum of C104 = 954.00954 of the 1000 intervals captured 500 MTB let k1=954/1000 MTB prin k1 Data Display K10.954000 pretty close to 95% MTB prin c102 c103 c104 a few of the 1000 intervals are shown below Data Display Row lowerpt upperpt C104 1 477.365 517.365 1 2 500.448 540.448 0 here is one that missed 500 ...the other 9 captured 500 3 480.304 520.304 1 4 480.457 520.457 1 5 485.006 525.006 1 6 479.585 519.585 1 7 480.382 520.382 1 8 481.189 521.189 1 9 486.166 526.166 1 10 494.388 534.388 1 _ 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 and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Analysis of covariance
From my understanding, there are three popular ways to analyze the following design (let's call it the pretest-posttest control-group design): R Pretest Treatment Posttest R PretestControl Posttest In the social sciences (e.g., see Pedhazur's popular regression text), the most popular analysis seems to be to run a GLM (this version is often called an ANCOVA), where Y is the posttest measure, X1 is the pretest measure, and X2 is the treatment variable. Assuming that X1 and X2 do not interact, ones' estimate of the treatment effect is given by B2 (i.e., the partial regression coefficient for the treatment variable which controls for adjusts for pretest differences). Another traditionally popular analysis for the design given above is to compute a new, gain score variable (posttest minus pretest) for all cases and then run a GLM (ANOVA) to see if the difference between the gains (which is the estimate of the treatment effect) is statistically significant. The third, and somewhat less popular (?) way to analyze the above design is to do a mixed ANOVA model (which is also a GLM but it is harder to write out), where Y is the posttest, X1 is time which is a repeated measures variable (e.g., time is 1 for pretest and 2 for posttest for all cases), and X2 is the between group, treatment variable. In this case one looks for treatment impact by testing the statistical significance of the two-way interaction between the time and the treatment variables. Usually, you ask if the difference between the means at time two is greater than the difference at time one (i.e., you hope that the treatment lines will not be parallel) Results will vary depending on which of these three approaches you use, because each approach estimates the counterfactual in a slightly different way. I believe it was Reichardt and Mark (in Handbook of Applied Social Research Methods) that suggested analyzing your data using more than one of these three statistical methods. I'd be interested in any thoughs you have about these three approaches. Take care, Burke Johnson http://www.coe.usouthal.edu/bset/Faculty/BJohnson/Burke.html = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: What is a confidence interval?
Dennis: Example A is a mistaken interpretation of a confidence interval for a mean. Unfortunately, this is is a very common misinterpretation. What you have described in Example A is a _prediction_ interval for an individual observation. Prediction intervals rarely get taught except (maybe) in the context of a regression model. Jon At 03:11 PM 9/26/01 -0400, you wrote: as a start, you could relate everyday examples where the notion of CI seems to make sense A. you observe a friend in terms of his/her lateness when planning to meet you somewhere ... over time, you take 'samples' of late values ... in a sense you have means ... and then you form a rubric like ... for sam ... if we plan on meeting at noon ... you can expect him at noon + or - 10 minutes ... you won't always be right but, maybe about 95% of the time you will? B. from real estate ads in a community, looking at sunday newspapers, you find that several samples of average house prices for a 3 bedroom, 2 bath place are certain values ... so, again, this is like have a bunch of means ... then, if someone asks you (visitor) about average prices of a bedroom, 2 bath house ... you might say ... 134,000 +/- 21,000 ... of course, you won't always be right but perhaps about 95% of the time? but, more specifically, there are a number of things you can do 1. students certainly have to know something about sampling error ... and the notion of a sampling distribution 2. they have to realize that when taking a sample, say using the sample mean, that the mean they get could fall anywhere within that sampling distribution 3. if we know something about #1 AND, we have a sample mean ... then, #1 sets sort of a limit on how far away the truth can be GIVEN that sample mean or statistic ... 4. thus, we use the statistics (ie, sample mean) and add and subtract some error (based on #1) ... in such a way that we will be correct (in saying that the parameter will fall within the CI) some % of the time ... say, 95%? it is easy to show this via simulation ... minitab for example can help you do this here is an example ... let's say we are taking samples of size 100 from a population of SAT M scores ... where we assume the mu is 500 and sigma is 100 ... i will take a 1000 SRS samples ... and summarize the results of building 100 CIs MTB rand 1000 c1-c100; made 1000 rows ... and 100 columns ... each ROW will be a sample SUBC norm 500 100. sampled from population with mu = 500 and sigma = 100 MTB rmean c1-c100 c101 got means for 1000 samples and put in c101 MTB name c1='sampmean' MTB let c102=c101-2*10 found lower point of 95% CI MTB let c103=c101+2*10 found upper point of 95% CI MTB name c102='lowerpt' c103='upperpt' MTB let c104=(c102 lt 500) and (c103 gt 500) this evaluates if the intervals capture 500 or not MTB sum c104 Sum of C104 Sum of C104 = 954.00954 of the 1000 intervals captured 500 MTB let k1=954/1000 MTB prin k1 Data Display K10.954000 pretty close to 95% MTB prin c102 c103 c104 a few of the 1000 intervals are shown below Data Display Row lowerpt upperpt C104 1 477.365 517.365 1 2 500.448 540.448 0 here is one that missed 500 ...the other 9 captured 500 3 480.304 520.304 1 4 480.457 520.457 1 5 485.006 525.006 1 6 479.585 519.585 1 7 480.382 520.382 1 8 481.189 521.189 1 9 486.166 526.166 1 10 494.388 534.388 1 _ 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 and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = ___ --- | \ Jon Cryer, Professor Emeritus ( ) Dept. of Statistics www.stat.uiowa.edu/~jcryer \\_University and Actuarial Science office 319-335-0819 \ * \of Iowa The University of Iowa home 319-351-4639 \/Hawkeyes Iowa City, IA 52242 FAX319-335-3017 |__ ) --- V It ain't so much the things we don't know that get us into trouble. It's the things we do know that just ain't so. --Artemus Ward = 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 covariance
At 02:26 PM 9/26/01 -0500, Burke Johnson wrote: From my understanding, there are three popular ways to analyze the following design (let's call it the pretest-posttest control-group design): R Pretest Treatment Posttest R PretestControl Posttest if random assignment has occurred ... then, we assume and we had better find that the means on the pretest are close to being the same ... if we don't, then we wonder about random assignment (which creates a mess) anyway, i digress ... what i would do is to do a simple t test on the difference in posttest means and, if you find something here ... then that means that treatment changed differentially compared to control if that happens, why do anything more complicated? has not the answer to your main question been found? now, what if you don't ... then, maybe something a bit more complex is appropriate IMHO _ 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 and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: E as a % of a standard deviation
At 04:49 PM 9/26/01 +, John Jackson wrote: re: the formula: n = (Z?/e)2 could you express E as a % of a standard deviation . In other words does a .02 error translate into .02/1 standard deviations, assuming you are dealing w/a normal distribution? well, let's see ... e is the margin of error ... using the formula for a CI for a population mean .. X bar +/- z * stan error of the mean so, the margin of error or e ... is z * standard error of the mean now, let's assume that we stick to 95% CIs ... so the z will be about 2 ... that leaves us with the standard error of the mean ... or, sigma / sqrt n let's say that we were estimating SAT M scores and assumed a sigma of about 100 and were taking a sample size of n=100 (to make my figuring simple) ... this would give us a standard error of 100/10 = 10 so, the margin of error would be: e = 2 * 10 or about 20 so, 20/100 = .2 ... that is, the e or margin of error is about .2 of the population sd if we had used a sample size of 400 ... then the standard error would have been: 100/20 = 5 and our e or margin of error would be 2 * 5 = 10 so, the margin of error is now 10/100 or .1 of a sigma unit OR 1/2 the size it was before but, i don't see what you have accomplished by doing this ... rather than just reporting the margin of error ... 10 versus 20 ... which is also 1/2 the size since z * stan error is really score UNITS ... and, the way you done it ... .2 or .1 would represent fractions of sigma ... which still amounts to score UNITS ... i don't think anything new has been done ... certainly, no new information has been created = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = _ 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 and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: What is a confidence interval?
Have you tried simulations? with something like Resampling Stats or Minitab? WBW On 26 Sep 2001, Warren wrote: Hi, I've been teaching an introductory stats course for several years. I always learn something from my students...hope they learn too. One thing I've learned is that confidence intervals are very tough for them. They can compute them, but why? Of course, we talk about confidence interval construction and I try to explain the usual 95% of all intervals so constructed will in the long run include the parameter...blah, blah. I've looked at the Bayesian interpretation also but find this a bit hard for beginning students. So, what is your best way to explain a CI? How do you explain it without using some esoteric discussion of probability? Now, here's another question. If I roll 2 dice and find the mean of the pips on the upturned faces. You can compute sample standard deviations, but if you roll 2 alike the s.d. is 0. So, you cannot compute a CI based on these samples. How would you explain? Thanks, Warren = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: What is a confidence interval?
In article [EMAIL PROTECTED], Dennis Roberts [EMAIL PROTECTED] wrote: as a start, you could relate everyday examples where the notion of CI seems to make sense A. you observe a friend in terms of his/her lateness when planning to meet you somewhere ... over time, you take 'samples' of late values ... in a sense you have means ... and then you form a rubric like ... for sam ... if we plan on meeting at noon ... you can expect him at noon + or - 10 minutes ... you won't always be right but, maybe about 95% of the time you will? B. from real estate ads in a community, looking at sunday newspapers, you find that several samples of average house prices for a 3 bedroom, 2 bath place are certain values ... so, again, this is like have a bunch of means ... then, if someone asks you (visitor) about average prices of a bedroom, 2 bath house ... you might say ... 134,000 +/- 21,000 ... of course, you won't always be right but perhaps about 95% of the time? These examples are NOT analogous to confidence intervals. In both examples, a distribution of values is inferred from a sample, and based on this distribution, a PROBABILITY statement is made concerning a future observation. But a confidence interval is NOT a probability statement concerning the unknown parameter. In the frequentist statistical framework in which confidence intervals exists, probability statements about unknown parameters are not considered to be meaningful. Radford Neal Radford M. Neal [EMAIL PROTECTED] Dept. of Statistics and Dept. of Computer Science [EMAIL PROTECTED] University of Toronto http://www.cs.utoronto.ca/~radford = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Psychology of the Future
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Re: What is a confidence interval?
some people are sure picky ... given the context in which the original post was made ... it seems like the audience that the poster was hoping to be able to talk to about CIs was not very likely to understand them very well ... thus, it is not unreasonable to proffer examples to get one into having some sense of the notion the examples below ... were only meant to portray ... the idea that observations have error ... and, over time and over samples ... one gets some idea about what the size of that error might be ... thus, when projecting about behavior ... we have a tool to know a bit about some penultimate value ... say the parameter for a person ... by using AN observation, and factoring in the error you have observed over time or samples ... in essence, CIs are + and - around some observation where ... you conjecture within some range what the truth might be ... and, if you have evidence about size of error ... then, these CIs can say something about the parameter (again, within some range) in face of only seeing a limited sample of behavior At 09:30 PM 9/26/01 +, Radford Neal wrote: In article [EMAIL PROTECTED], Dennis Roberts [EMAIL PROTECTED] wrote: as a start, you could relate everyday examples where the notion of CI seems to make sense A. you observe a friend in terms of his/her lateness when planning to meet you somewhere ... over time, you take 'samples' of late values ... in a sense you have means ... and then you form a rubric like ... for sam ... if we plan on meeting at noon ... you can expect him at noon + or - 10 minutes ... you won't always be right but, maybe about 95% of the time you will? B. from real estate ads in a community, looking at sunday newspapers, you find that several samples of average house prices for a 3 bedroom, 2 bath place are certain values ... so, again, this is like have a bunch of means ... then, if someone asks you (visitor) about average prices of a bedroom, 2 bath house ... you might say ... 134,000 +/- 21,000 ... of course, you won't always be right but perhaps about 95% of the time? These examples are NOT analogous to confidence intervals. In both examples, a distribution of values is inferred from a sample, and based on this distribution, a PROBABILITY statement is made concerning a future observation. But a confidence interval is NOT a probability statement concerning the unknown parameter. In the frequentist statistical framework in which confidence intervals exists, probability statements about unknown parameters are not considered to be meaningful. you are clearly misinterpreting, for whatever purpose, what i have said i certainly have NOT said that a CI is a probability statement about any specific parameter or, being able to attach some probability value to some certain value as BEING the parameter the p or confidence associated with CIs only makes sense in terms of dumping all possible CIs into a hat ... and, asking what is the probability of pulling one out at random that captures the parameter (whatever the parameter might be) ... the example i gave with some minitab work clearly showed that ... and made no other interpretation about p values in connection with CIs perhaps some of you who seem to object so much to things i offer ... might offer some posts of your own in response to requests from those seeking help ... to make sure that they get the right message ... Radford Neal Radford M. Neal [EMAIL PROTECTED] Dept. of Statistics and Dept. of Computer Science [EMAIL PROTECTED] University of Toronto http://www.cs.utoronto.ca/~radford = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = == 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/ =
