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#1
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1.¹Ñ¡àÃÕ¹¤¹Ë¹Ö觤ӹdzËÒ¤èÒÊèǹàºÕè§ູÁҵðҹ¢Í§¤Ðá¹¹ÊͺÇÔªÒ¤³ÔµÈÒʵÃì¢Í§à¾×è͹·Ñé§ËÁ´ã¹ËéͧàÃÕ¹ä´éà·èҡѺ 7 ¤Ðá¹¹ áµè¾ºÇèҤӹdz¼Ô´ à¹×èͧ¨Ò¡¹Ó¤èÒÁѸ°ҹÁÒãªéá·¹¤èÒà©ÅÕèÂàÅ¢¤³Ôµ ¶éÒ¤èÒÁѸ°ҹÁÕ¤èÒà·èҡѺ 53 áÅФèÒà©ÅÕèÂàÅ¢¤³ÔµÁÕ¤èÒà·èҡѺ 57 ¤èÒÊèǹàºÕè§ູÁҵðҹ·Õè¶Ù¡µéͧ¤ÇÃÁÕ¤èÒà·èÒäÃ
1.ÃÙ·33 2.ÃÙ·44 3.ÃÙ·55 4.ÃÙ·66 5.ÃÙ·77 2.¨ÐµéͧÃÔ¹ÊÒüÊÁ 60% ÍÍ¡¨Ò¡¶Ñ§ 20ÅԵà à»ç¹¨Ó¹Ç¹¡ÕèÅԵà àÁ×èÍàµÔÁ¹éÓà¢éÒá·¹·Õèã¹»ÃÔÁÒ³·Õèà·èҡѹ ¨Ö§¨Ðä´éÊÒüÊÁ 30% 1.10.83 2.11.67 3.12.5 4.16.45 5.14.17 3.¨Ðµéͧà·ÊÒüÊÁ 40% ÍÍ¡¨Ò¡¶Ñ§ 32ÅԵà à»ç¹¨Ó¹Ç¹¡ÕèÅԵà àÁ×èÍàµÔÁ¹éÓà¢éÒá·¹·Õèã¹»ÃÔÁÒ³·Õèà·èҡѹ ¨Ö§¨Ðä´éÊÒüÊÁ 25% 1.8 2.9 3.10 4.11 5.12 ¢éÍ 2¡Ñº¢éÍ 3 ÇÔ¸Õ·ÓÁѹ¹èÒ¨ÐàËÁ×͹¡Ñ¹ãªèÁÑé¤ÃѺ áµèÇÔ¸Õ·Õè¼Áãªé¡Ñº¢éÍ3 áµèãªé¡Ñº¢éÍ 2 äÁèÁդӵͺ㹪éÍ ÊÁ¡ÒüÁ¹Ð¤ÃѺ 12.8-0.4x / 32 = 25/100 ä´é 12 «Ö觶١¾Í´Õ áµèãªé¡Ñº¢éÍ 3 ´Ñ¹ä´é 10 ÊÃØ»ÊÁ¡ÒüÁ¼Ô´ËÃ×ͪéͼԴ¤ÃѺ ¢éÍáá¹èÒ¨ÐãªéÊٵøÃÃÁ´ÒäÁèãªèËÃ×ͤÃѺ áµèãªéáÅéÇä´é¤èÒàºÕè§ູÁҵðҹãËÁèÃÙ·µÔ´Åº ¼Áä´é «Ô¡ÁÒ x¡ÓÅѧÊͧ Êèǹn ¹éÍ¡ÇèÒ ¤èÒà©ÅÕèÂãËÁè¡¡ÓÅѧÊͧ 仵èÍäÁè¶Ù¡àÅÂ...ú¡Ç¹ªèÇÂ˹è͹ФÃѺ ¢Íâ·É·Õ¤ÃѺ ÇѹËÅѧ¨ÐºÍ¡ª×èÍàÃ×èͧ´éÇÂÅСѹ¹Ð¤ÃѺ 12 ¸Ñ¹ÇÒ¤Á 2011 21:06 : ¢éͤÇÒÁ¹Õé¶Ù¡á¡éä¢áÅéÇ 1 ¤ÃÑé§, ¤ÃÑé§ÅèÒÊØ´â´Â¤Ø³ gon à˵ؼÅ: ãªé»ØèÁá¡éä¢ ¶éÒµéͧ¡ÒõͺµÔ´ æ ¡Ñ¹ ã¹àÇÅÒÊÑé¹ æ ¤ÃѺ. + àÇ鹺Ã÷ѡ´ãËéÍèÒ¹§èÒ |
#2
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¢éÍÊͧ ¤Ô´ã¹ã¨ µÍº 10 ÅÔµÃ
$\frac{60}{100} \times (20-x) = \frac{30}{100} \times 20$ ¢éÍÊÒÁ $\frac{40}{100} \times (32-x) = \frac{25}{100} \times 32$
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ÁÒËÒ¤ÇÒÁÃÙéäÇéµÔÇËÅÒ¹ áµèËÅÒ¹äÁèàÍÒàÅ¢áÅéÇ à¢éÒÁÒ·ÓàÅ¢àÍÒÁѹÍÂèÒ§à´ÕÂÇ ¤ÇÒÁÃÙéà»ç¹ÊÔè§à´ÕÂÇ·ÕèÂÔè§ãËé ÂÔè§ÁÕÁÒ¡ ÃÙéÍÐäÃäÁèÊÙé ÃÙé¨Ñ¡¾Í (¡àÇ鹤ÇÒÁÃÙé äÁèµéͧ¾Í¡çä´é ËÒäÇéÁÒ¡æáËÅдÕ) (áµè¡çÍÂèÒãËéÁÒ¡¨¹·èÇÁËÑÇ àÍÒµÑÇäÁèÃÍ´) |
#3
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¢Íº¤Ø³¤ÃѺ §Ñé¹áÊ´§ÇèÒ ¢éÍ2ªéͼԴÊÔ¤ÃѺ ¢éÍÊͺªéÒ§à¼×Í¡»Õ52¹èФÃѺ
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#4
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$$\frac{\Sigma(x-53)^2}{n}=49 \Rightarrow \Sigma(x-53)^2 = 49n$$
$$\Sigma(x-57+4)^2 = 49n$$ $$\Sigma(x-57)^2 + 8\Sigma(x-57)+\Sigma 16 = 49n$$ $$\Sigma(x-57)^2 + 8 \cdot 0+16n = 49n$$ $$\Sigma(x-57)^2 = 33n$$ $$S.D. = \sqrt{\frac{\Sigma(x-57)^2}{n}} = \sqrt{\frac{33n}{n}} $$
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The Lost Emic <<-- ˹ѧÊ×Íà©Å¢éÍÊͺÃдѺ»ÃжÁ¹Ò¹ÒªÒµÔ EMIC ¤ÃÑ駷Õè 1 - ¤ÃÑ駷Õè 8 ªØ´ÊØ´·éÒ ËŧÁÒ 12 ¸Ñ¹ÇÒ¤Á 2011 21:13 : ¢éͤÇÒÁ¹Õé¶Ù¡á¡éä¢áÅéÇ 1 ¤ÃÑé§, ¤ÃÑé§ÅèÒÊØ´â´Â¤Ø³ gon |
#5
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ÍéÒ§ÍÔ§:
áÅéÇàÍÒÊÁ¡Ò÷Õè¼Ô´á·¹Å§ä» ËÒ¤èÒ «Ô¡ÁÒx¡ÓÅѧÊͧÊèǹn áÅéÇàÍÒÁÒá·¹ãËÁèËÒ¤èÒ S.D. ¶Ù¡äÁèä´éËÃͤÃѺ ¤³ÔµÈÒʵÃì¤×ͤÇÒÁ¨ÃÔ§ ¤Ô´ÇÔ¸Õä˹Áѹ¡çµéͧä´éäÁèãªéËÃͤÃѺ |
#6
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ÍéÒ§ÍÔ§:
áµèÇèÒ·Õèä´éäÁè¶Ù¡ ¡çà¾ÃÒÐÇèҷӼԴ䧤ÃѺ. ¤èҢͧ $\Sigma x^2$ ·ÕèËÒÁÒ¹Ñé¹ à»ç¹¤èÒ·Õè¼Ô´¤ÃѺ ¶éÒ¨ÐàÍÒä»ãªé µéͧËÒ¤èÒ $\Sigma x^2$ ·Õè¶Ù¡àÊÕ¡è͹ |
#7
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ÍéÒ§ÍÔ§:
µÒÁ·Õè¤Ø³ gon ºÍ¡¤ÃѺ ͸ԺÒÂä´é´Ñ§¹Õé $$S.D. = \sqrt{\frac{\Sigma(x-MEAN)^2}{n}} $$ $$S.D. = \sqrt{\frac{\Sigma x^2 + \Sigma MEAN^2 - \Sigma 2x MEAN }{n}} $$ $$S.D. = \sqrt{\frac{\Sigma x^2} {n} + \frac{\Sigma MEAN^2} {n} - \frac{\Sigma 2x MEAN} {n}} $$ â´Â·Õè $$\frac{\Sigma x } {n} = MEAN $$ ¨Ðä´é $$S.D. = \sqrt{\frac{\Sigma x^2} {n} + MEAN^2 - 2MEAN^2} $$ $$S.D. = \sqrt{\frac{\Sigma x^2} {n} - MEAN^2} $$ Êٵôѧ¡ÅèÒÇ ãªéä´é੾ÒеÑÇ¡ÅÒ§·Õèà»ç¹¤èÒà©ÅÕèÂàÅ¢¤³Ôµ¤ÃѺ ¶éÒà»ÅÕè¹µÑÇ¡ÅÒ§à»ç¹ÁѸ°ҹ ¨ÐãªéÊٵùÕéäÁèä´é¤ÃѺ |
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