What Is This?:
I've started receiving messages like the following from the newsgroup. Is this a problem with the newsgroup or a problem with my mail program? Ronny Richardson At 02:02 PM 1/31/02 +0800, you wrote: Á貨ÅÆ01713»·±£ÐÍÍòÄܽº ²úÆ·½éÉÜ ´«Í³µÄ¼ÒÍ¥×°ÊÎ×°è«£¬ÃÅ¡¢´°¡¢¼Ò¾ß¡¢×À¡¢ÒΡ¢´²¡¢¹ñ¡¢Ïä°ü¡¢µØ°åµÄÖÆ×÷Óö¤Ã¡¢é ¾Í·Çá£ÓÉÓÚľ½º(°×½º)µÄÎÊÊÀ£¬ÂäºóµÄ²Ù×÷·½Ê½ÒѳÉΪÀúÊ·¡£Ëæ׏ú¼Ò½¨Éè¼°ÃñÓõÄÐ èÒª£¬Æä×°ÊÎ×°è«ÓëÈÕ¾ãÔö£¬µµ´ÎÒàÔ½À´Ô½¸ß¡£Òòľ½ºÔÚÕ³½áÇ¿¶ÈºÍ²Ù×÷ÐÔÄܵȷ½ÃæÓÐÆ ä¾ÖÏÞÐÔ£¬¹Ê´ï²»µ½ÀíÏëµÄ×°è«ÒªÇ󣬽ø¶ø¸÷ÖÖÐͺš¢Æ·ÅƵÄÍòÄܽºÔڶ̶̼¸Äê±ãÕ¼¾ÝÁ ËÒ»¶¨µÄ×°è«Êг¡£¬´Ó¶øÌæ´úÁ˵ÚÒ»´ú(ľ½º)Õ³½á²ÄÁϵÄÆÕ±éÓ¦ÓᣠÎÞ¿ÉÖÃÒÉ£¬´«Í³µÄÍòÄܽºÓÃ;¹ã¡¢Êг¡´ó£¬µ«Ò²¸øÉç»á´øÀ´Á˼«´óµÄ¸ºÃæÓ°Ïì¡£´«Í³µ ÄÍòÄܽºÊÇÓöàÖÖÓлú»¯Ñ§ÖƼÁ¾¹¤ÒպϳɵÄÕ³ºÏ¼Á¡£ÆäÖÐÖ÷ÒªÈܼÁ¼×±½¼°¼×È©ÀàÊͷŵ ÄÓж¾ÆøÌåDZ·üÆÚ³¤£¬ÔÚ¶ÌÆÚÄÚ²»Ò×»Ó·¢´ù¾¡£¬¶ÌÔòÒª°ëÄ꣬³¤ÔòÐèÒ»¡¢¶þÄê²ÅÄܻӷ¢µ ô£¬¼«Ò×ÎÛȾ»·¾³¡£·¿ÎÝ×°è«Íêºó£¬ÈçºÜ¿ìÈëס£¬¶ÔÈËÌåÓÈÆäÊÇСº¢¼°Ì¥¶ù£¬¼«Ò׸ÐȾ ýÐÔ²¡£¬ÉõÖÁÖ»û¡¢Ö°©(°×ѪÇò²¡)¡£ÆäÓж¾ÆøÌå»áÊͷŴ̱ǡ¢´ÌÑÛÆø棬µ¼ÖÂÍ·»è¡¢¶ ñÐÄ¡¢¸¹Í´¡¢¿ÈËÔµÈÖ¢×´¡£Ðí¶àÖØÊÓ»·¾³±£»¤µÄ¹ú¼Ò£¬ÓÈÆäÊÇ·¢´ï¹ú¼Ò£¬¶ÔʹÓüױ½ºÍ¼ ×È©ÀàÈܼÁ»òÊÔ¼ÁÉú²úµÄ²úÆ·ÊÇÃ÷ÎĹ涨ÑϼӿØÖƵġ£È»¶øÎÒ¹ú×÷Ϊ·¢Õ¹Öйú¼Ò£¬ÔÚ»·± £·½Ã棬·¨ÂÉ¡¢·¨¹æ·½Ã滹²»¹»ÍêÉÆ£¬»·±£Òâʶ½Ï²î¡£µ«Ëæ׏ã´óÓû§»·±£ÒâʶµÄÌá¸ß¼ °Óû§ÆÈÇÐÐèÒªÒ»¸ö¶Ô½¡¿µÃ»ÓÐÍþвµÄÇå´¿ÊÀ½ç£¬Ò»Ð©ÂÌÉ«½¨²Ä£¬ÎÞ¶¾»·±£ÐÍÍ¿ÁÏ£¬Õ³º ϼÁÒ²Ó¦Ô˶øÉú¡£ ÎÒ¹«Ë¾ÑÐÖÆ¿ª·¢µÄÂÌÉ«Õ³ºÏ²ÄÁÏ01713ÎÞ±½ÍòÄܽº¸Õ¿çÈëÊг¡£¬¼´Òѵõ½¹ã´óÓû§¼°¾ ÏúÉ̵ÄÓ»Ô¾¶©¹ºÓëºÃÆÀ¡£ÏàÐÅ£¬¾¹ýÓû§Êµ¼ÊʹÓúó£¬ÔÚ½¨Öþ×°è«Êг¡¶¨»áÐγɺ䶯Ч Ó¦¡£ Ìص㼰ÓÃ;: ±¾Æ·²»º¬¼×È©¡¢¼×±½ÀàÓж¾¸±×÷ÓÃÈܼÁ¼°ÊÔ¼Á£¬º¬ÓÐÉÙÁ¿µÄÎÞ¶¾ÈܼÁ£¬µ«ÔÚ¶ÌʱÆÚÄÚ( 3-5Ìì)¼´¿É»Ó·¢µô£¬²»ÊÍ·ÅÓж¾ÆøÌ壬¶ÔÈËÌåÎÞº¦¡£01713»·±£ÐÍÍòÄܽº·Çµ«ÊÇÎÞ¶¾»·± £ÐÍÕ³ºÏ¼Á£¬ÇÒÔÚÕ³½áÇ¿¶ÈµÈÖ÷ÒªÖ¸±êÉϾùÓÐËùÌá¸ß¡£Ëü¾ßÓжÀÌصÄÈÍÐÔ£¬¿Ë·þÁË´«Í³Í òÄܽº´àÐÔµÄȱµã£¬Í¬Ê±£¬ÔÚÊ©¹¤Õ³ºÏ´íλʱ£¬¿ÉÖØÐÂÕ³ºÏ£¬¼õÉÙ²ÄÁÏÀË·Ñ¡£ ¼¼ÊõÖ¸±êÓëÐÔÄÜ ÏîÄ¿ Ö¸±ê ¼ìÑé½á¹û Íâ¹Û Èé»ÆºÖÉ«Õ³³íÒºÌ壬ÎÞ»úеÔÓÖÊ Èé»ÆºÖÉ«Õ³³íÒºÌ壬ÎÞ»úеÔÓÖÊ ¹ÌÌ庬Á¿£¥ 25 26.8 PH Öµ 7+/-0.5 7.1 Õ³¶È(25¡æ)mPa.s 950 974.5 ¼×±½ ÎÞ Î´¼ì³ö ¶þ¼×±½ ÎÞ Î´¼ì³ö Ö¸´¥¸ÉÔïʱ¼ä(25¡æ) 5--18 12 ¼ôÇÐÇ¿¶Èmpa ¡Ý2.5 2.7 °þÀëÇ¿¶Èw/mm ¡Ý3.0 3.2 Éú²úÉÌ£º½ËÕÊ¡³£ÖÝÊÐÁ貨»¯¹¤ÓÐÏÞ¹«Ë¾ ÁªÏµÎÒÃÇ£º½ËÕÊ¡³£ÖÝÊж«ÃÅа²Î÷¹¤ÒµÇø ÁªÏµÈË£º¶ÏÈÉú µç»°(Tel): (0519)8662698 8662415 ´«Õæ(Fax): (0519)8662413 Óʱà(P.C): 213117 http://www.cnlingbo.com E-mail:[EMAIL PROTECTED] = ¸ÃÓʼþʹÓà ¿ÆÌØÓʼþȺ·¢Èí¼þ ·¢ËÍ,ÓʼþÄÚÈÝÓë¿ÆÌØÈí¼þÎÞ¹Ø ¿ÆÌØÈí¼þ http://www.caretop.com = = 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/ =
How To Code Position In A List
I want to analyze data sets where I have two variables, the finishing position (ordinal) and a ratio scale performance variable. I want to see if there is a relationship between the finishing position and the value of the ratio performance variable. This would be about the same as seeing if there was a relationship between the order in which an exam was completed and the resulting score. My problem is that I have multiple groups and so being #15 in one group might put you in the back of the group while being #20 in another group might put you near the front. Is there a way to recode the position variable to make it meaningful in this situation? I have considered percentiles (more specifically dectiles) but I am not sure this is the best way to go. Any suggestions? Ronny Richardson = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
When to Use t and When to Use z Revisited
A few weeks ago, I posted a message about when to use t and when to use z. In reviewing the responses, it seems to me that I did a poor job of explaining my question/concern so I am going to try again. I have included a few references this time since one responder doubted the items to which I was referring. The specific references are listed at the end of this message. Bluman has a figure (2, page 333) that is suppose to show the student When to Use the z or t Distribution. I have seen a similar figure in several different textbooks. The figure is a logic diagram and the first question is Is sigma known? If the answer is yes, the diagram says to use z. I do not question this; however, I doubt that sigma is ever known in a business situation and I only have experience with business statistics books. If the answer is no, the next question is Is n=30? If the answer is yes, the diagram says to use z and estimate sigma with s. This is the option I question and I will return to it briefly. In the diagram, if the answer is no to the question about n=30, you are to use t. I do not question this either. Now, regarding using z when n=30. If we always use z when n=30, then you would never need a t table with greater than 28 degrees of freedom. (n=29 would always yield df=28.) Bluman cuts his off at 28 except for the infinity row so he is consistent. (The infinity row shows that t becomes z at infinity.) However, other authors go well beyond 30. Aczel (3, inside cover) has values for 29, 30, 40, 60, and 120, in addition to infinity. Levine (4, pages E7-E8) has values for 29-100 and then 110 and 112, along with infinity. I could go on, but you get the point. If you always switch to z at 30, then why have t tables that go above 28? Again, the infinity entry I understand, just not the others. Berenson states (1, page 373), However, the t distribution has more area in the tails and less in the center than down the normal distribution. This is because sigma is unknown and we are using s to estimate it. Because we are uncertain of the value of sigma, the values of t that we observe will be more variable than for Z. So, Berenson seems to me to be saying that you always use t when you must estimate sigma using s. Levine (4, page 424) says roughly the same thing, However, the t distribution has more area in the tails and less in the center than does the normal distribution. This is because sigma is unknown and we are using s to estimate it. Because we are uncertain of the value sigma, the values of t that we observe will be more variable than for Z. So, I conclude 1) we use z when we know the sigma and either the data is normally distributed or the sample size is greater than 30 so we can use the central limit theorem. 2) When n30 and the data is normally distributed, we use t. 3) When n is greater than 30 and we do not know sigma, we must estimate sigma using s so we really should be using t rather than z. Now, every single business statistics book I have examined, including the four referenced below, use z values when performing hypothesis testing or computing confidence intervals when n30. Are they 1. Wrong 2. Just oversimplifying it without telling the reader or am I overlooking something? Ronny Richardson References -- (1) Basic Business Statistics, Seventh Edition, Berenson and Levine. (2) Elementary Statistics: A Step by Step Approach, Third Edition, Bluman. (3) Complete Business Statistics, Fourth Edition, Aczel. (4) Statistics for Managers Using Microsoft Excel, Second Edition, Levine, Berenson, Stephan. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: I am teaching the students
The attachment to this message contains a virus. Ronny Richardson At 07:12 PM 11/18/2001 -0500, you wrote: I think it is great for the students to be able to analyze more easily using a calculator, but I feel that they must know what the calculator is finding for them. This unit fits into the overall curriculum for Algebra II as determined by the State Department of Public Instruction. Attachment Converted: d:\eudora\attach\InterScan_SafeStamp.txt = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Help for DL students in doing assignments
At 10:40 PM 10/15/01 +0200, you wrote: ### Dear Mr. Dawson, please send me at least ONE even prime ### and i shall give you $1,000,000. Well, I am not Mr. Dawson but two (2) is both prime and even. You can send the check to the address below. Dr. Ronny Richardson Associate Professor of Management Southern Polytechnic State University Management Program 1100 South Marietta Parkway Marietta, GA 30060-2896 Phone: (770) 528-5542 Fax:(770) 528-4967 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: VENTURE CAPITAL
Actually, he is not thinking at all. I get a ton of spam that comes via this newsgroup. Somehow, the address got on some spam CD. Now, everyone who buys that spam CD sets us the same tired old junk. About half of my total spam, or at least the spam that gets through my filters, somes via this newsgroup. I have come close dropping it several times for this reason. I wish there was some way that some of this spam could be cleaned up before it was sent on to us. Ronny Richardson At 09:11 PM 08/31/2001 -0500, you wrote: You certainly wouldn't expect statisticians to be gullible, now, would you? Jay Gordon D. Pusch wrote: Leo G Simonetta [EMAIL PROTECTED] writes: Wow two copies of the that old chestnut the Nigerian Scam in one day. What I wonder is: Why is he picking on the the 'sc.stat.*' newsgroups and not other newsgroups? Does he think statisticians are stupid ??? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Forecasting Seasonal Indices Question (Long)
The seasonal indices represent the amount by which the seasons vary from after. If there is no seasonality, then you would expect all of the indices to be 1.00 so the total (for quarterly data) should be 4.00. With seasonality, some are above 1.00 and others are below 1.00 but the total should still be 4.00. However, I am getting results that are significantly different. I want to lay out the problem here to see if anyone has any suggestions. (Everything below is has quarterly seasonality but the comments would apply to seasonality.) The approach I have been teaching for finding seasonal indices is the same one that is covered in Forecasting Principles and Applications by Stephen DeLurgio (McGraw-Hill). That approach is to: 1. Take period data and produce a 4-period moving average 2. Take that average and produce a 2-period moving average (This is required because (1+2+3+4)/4=2.5 so these averages are not whole periods. (2.5+3.5)/2=3 so this second moving average gives us whole periods. 3. Compute a percentage as Sales/Moving Average 4. Average all these percentage for period 1 to get the index for period 1. Do the same for the other periods. The first question that comes up for me is should the first moving average in step 1 be centered or forward-ended? DeLurgio shows it centered and that makes a little more sense to me since for the first four periods, (1+2+3+4)/4=2.5 which would be centered. However, I have seen other books where this was treated as a forward-ended average. Since every average will contain one sales figure from each period, you could justify writing down the average either centered or forward-ended. I've tried it both ways and the results are sometimes significantly different. The second question concerns the resulting seasonal indices. Since the indices represent deviations from average, you would expect the average indices to be 1.00 and so quarterly indices should total to about 4.00. Most of the examples I have seen in textbooks total to something near 4.00 and they either scale it to 4.00 as DeLurgio does or they ignore the small difference. However, the series I have been working with produces indices with a total significantly different from 4.00. That series follows: 147, 119, 153, 267, 225, 201, 1,011, 895, 865, 372, 305, 309, 178, 147, 144, 262, 222, 208, 1,297, 1,122, 1,091, 484, 412, 407, 191, 175, 149, 288, 243, 223, 1,468, 1,252, 1,246, 496, 459, 447 Using a centered moving average in step 1, the indices I obtain are: 0.87351 0.54884 1.14164 1.21995 === 3.78393 Using a forward-ended moving average in step 1, the indices I obtain are: 1.13681 0.74235 2.92445 1.44557 === 6.24918 It bothers me that these numbers are so different and that the total for the forward-ended moving average is so different from 4.00. I thought that the difference might be due to scaling so I scaled both series to force them to total to 4.00 and I got: 0.923389 0.58018 1.20683 1.289612 and 0.727654 0.475166 1.871894 0.925286 respectively. These are very different from one another. Several more approaches to seasonal indices are given in Production and Operations Management by Chase, Aquilano, Jacobs (McGraw-Hill.) The first one calls for simply dividing each period by the overall period average and then averaging these factors for each period. This approach forces the total to be 4.00 and the indices were: 1.008696 0.55492 1.23524 1.201144 Another approach is to fit a regression line to the data, find a ratio of actual to trend and then average the indices for each period. That approach yields the indices: 1.01504 0.559943 1.232433 1.184109 3.991525 Scaling everything to total to 4.00 and comparing the results, we have: Forward Average Centerd MA Ended MA DifferenceRegression -- ---- 0.9234 0.7277 1.00871.0172 0.5802 0.4752 0.55490.5611 1.2068 1.8719 1.23521.2350 1.2896 0.9253 1.20111.1866 Now, I understand why the results might be slightly different but it seems to me that they should be closer than they are. Any comments? Dr. Ronny Richardson Associate Professor of Management Southern Polytechnic State University School of Management 1100 South Marietta Parkway Marietta, GA 30060-2896 Phone: (770) 528-5542 Fax:(770) 528-4967 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
How to work a problem
What follows is a question from the test bank for Complete Business Statistics by Amir Aczel. I am embarrassed to say that I cannot figure out how to work the problem. I do know from the test bank program that the answer is 798. Any hints or solutions would be greatly appreciated. "A real estate salesperson wants to prove that 65% of all home-owners change their house in less than 6 years. This salesperson wants to have a 90% probability of success if the true percentage is 60%. If the hypothesis test is carried out at a 5% level of significance, what should be the minimum sample size in a survey conducted to prove this claim?" Dr. Ronny Richardson Associate Professor of Management Southern Polytechnic State University School of Management 1100 South Marietta Parkway Marietta, GA 30060-2896 Phone: (770) 528-5542 Fax:(770) 528-4967 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Text for Transition Course 1/2 POM 1/2 Stats
I have been ask to design and present a transition course in the summer. The course is one of several for incoming business Masters students who do not have the necessary background in management. The course is a 3-hour course and is expected to cover both statistics and operations management. I normally teach both of these courses at the undergraduate level and the easiest thing to do would be to adopt two books, the ones I currently use for undergraduate stats and OM courses. However, I was wondering if anyone knew of a textbook that covered both areas. I am also wondering if anyone has suggestions for this course. I have just now been assigned the task and I've got to decide what to cover from the two courses and what to leave out. Dr. Ronny Richardson Associate Professor of Management Southern Polytechnic State University School of Management 1100 South Marietta Parkway Marietta, GA 30060-2896 Phone: (770) 528-5542 Fax:(770) 528-4967 === This list is open to everyone. Occasionally, less thoughtful people send inappropriate messages. Please DO NOT COMPLAIN TO THE POSTMASTER about these messages because the postmaster has no way of controlling them, and excessive complaints will result in termination of the list. For information about this list, including information about the problem of inappropriate messages and information about how to unsubscribe, please see the web page at http://jse.stat.ncsu.edu/ ===
