n=2,3,4,5和6的二項式表
來源:教育資源網(wǎng)
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發(fā)布時間:2020-12-02 08:01:31
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點擊:1907
一個重要的離散隨機變量是二項式隨機變量。這種類型的變量的分布,稱為二項分布,完全由兩個參數(shù)決定:n和p。這里n是試驗次數(shù),p是成功的概率。下表用于n=2,3,4,5和6。每個概率四舍五入到小數(shù)點后三位。
在使用表格之前,確定是否應(yīng)使用二項式分布非常重要。為了使用這種類型的分布,我們必須確保滿足以下條件:
- 我們有有限數(shù)量的觀察或試驗。
- teach試驗的結(jié)果可以分為成功或失敗。
- 成功的可能性保持不變。
- 觀察結(jié)果彼此獨立。
二項分布在總共n個獨立試驗的實驗中給出r成功的概率,每個試驗具有p的成功概率。概率通過公式C(n,r)pr(1-p)n-r其中C(n,r)是組合的公式。
表中的每個條目按p和r的值排列。對于n。的每個值有一個不同的表
其他表格
對于其他二項式分布表:n=7至9,n=10至11。對于n p和n的情況(1-p)大于或等于10,我們可以使用二項式分布的正態(tài)近似。在這種情況下,近似非常好,不需要計算二項式系數(shù)。這提供了很大的優(yōu)勢,因為這些二項式計算可能非常復(fù)雜。
示例
為了了解如何使用該表,我們將從遺傳學考慮以下示例。假設(shè)我們有興趣研究兩個父母的后代,我們都知道他們都有隱性和顯性基因。后代將繼承兩個副本的概率隱性基因(因此具有隱性特征)為1/4。
假設(shè)我們想考慮一個六口之家中一定數(shù)量的孩子具有這種特征的概率。設(shè)104 X 105為具有這種特征的孩子的數(shù)量。我們看一下表格中的106 n 107 n 6和108 p 109 0.25列,見下文:
0.178、0.356、0.297、0.132、0.033、0.004、0.000
這就意味著我們的例子
- P(X=0)=17.8%,這是沒有一個孩子具有隱性特征的概率。
- P(X=1)=35.6%,這是其中一個孩子具有隱性特征的概率。
- P(X=2)=29.7%,這是兩個孩子具有隱性特征的概率。
- P(X=3)=13.2%,這是三個孩子具有隱性特征的概率。
- P(X=4)=3.3%,這是四個孩子具有隱性特征的概率。
- P(X=5)=0.4%,這是五個孩子具有隱性特征的概率。
表n=2至n=6
142 n 1432
198>p | .01 | .05 | .10 | .15 | .20 | .25 | .30 | .35 | .40 | .45 | .50 | .55 | .60 | .65 | .70 | .75 | .80 | .85 | .90 | .95 | |
0 | .980 | .902 | .810 | .723 | .640 | .563 | .490 | .423 | .360 | .303 | .250 | .203 | .160 | .123 | .090 | .063 | .040 | .023 | .010 | .002 | |
1 | .020 | .095 | .180 | .255 | .320 | .375 | .420 | .455 | .480 | .495 | .500 | .495 | .480 | .455 | .420 | .375 | .320 | .255 | .180 | .095 | |
2 | .000 | .002 | .010 | .023 | .040 | .063 | .090 | .123 | .160 | .203 | .250 | .303 | .360 | .423 | .490 | .563 | .640 | .723 | .810 | .902 |
n=3
p | .01 | .05 | .10 | .15 | .20 | .25 | .30 | .35 | .40 | .45 | .50 | .55 | .60 | .65 | .70 | .75 | .80 | .85 | .90 | .95 | |
r | 0 | .970 | .857 | .729 | .614 | .*** | .422 | .343 | .275 | .216 | .166 | .125 | .091 | .064 | .043 | .027 | .016 | .008 | .003 | .001 | .000 |
1 | .029 | .135 | .243 | .325 | .384 | .422 | .441 | .444 | .432 | .408 | .375 | .334 | .288 | .239 | .189 | .141 | .096 | .057 | .027 | .007 | |
2 | .000 | .007 | .027 | .057 | .096 | .141 | .189 | .239 | .288 | .334 | .375 | .408 | .432 | .444 | .441 | .422 | .384 | .325 | .243 | .135 | |
3 | .000 | .000 | .001 | .003 | .008 | .016 | .027 | .043 | .064 | .091 | .125 | .166 | .216 | .275 | .343 | .422 | .*** | .614 | .729 | .857 |
764 n 765 4
p | .01 | .05 | .10 | .15 | .20 | .25 | .30 | .35 | .40 | .45 | .50 | .55 | .60 | .65 | .70 | .75 | .80 | .85 | .90 | .95 | |
r | 0 | .961 | .815 | .656 | .522 | .410 | .316 | .240 | .179 | .130 | .092 | .062 | .041 | .026 | .015 | .008 | .004 | .002 | .001 | .000 | .000 |
1 | .039 | .171 | .292 | .368 | .410 | .422 | .412 | .384 | .346 | .300 | .250 | .200 | .154 | .112 | .076 | .047 | .026 | .011 | .004 | .000 | |
2 | .001 | .014 | .049 | .098 | .154 | .211 | .265 | .311 | .346 | .368 | .375 | .368 | .346 | .311 | .265 | .211 | .154 | .098 | .049 | .014 | |
3 | .000 | .000 | .004 | .011 | .026 | .047 | .076 | .112 | .154 | .200 | .250 | .300 | .346 | .384 | .412 | .422 | .410 | .368 | .292 | .171 | |
4 | .000 | .000 | .000 | .001 | .002 | 0.004 | .008 | .015 | .026 | .041 | .062 | .092 | .130 | .179 | .240 | .316 | .410 | .522 | .656 | .815 |
1144 n 1145 5
p | .01 | .05 | .10 | .15 | .20 | .25 | .30 | .35 | .40 | .45 | .50 | .55 | .60 | .65 | .70 | .75 | .80 | .85 | .90 | .95 | |
r | 0 | .951 | .774 | .590 | .444 | .328 | .237 | .168 | .116 | .078 | .050 | .031 | .019 | .010 | .005 | .002 | .001 | .000 | .000 | .000 | .000 |
1 | .048 | .204 | .328 | .392 | .410 | .396 | .360 | .312 | .259 | .206 | .156 | .113 | .077 | .049 | .028 | .015 | .006 | .002 | .000 | .000 | |
2 | .001 | .021 | .073 | .138 | .205 | .264 | .309 | .336 | .346 | .337 | .312 | .276 | .230 | .181 | .132 | .088 | .051 | .024 | .008 | .001 | |
3 | .000 | .001 | .008 | .024 | .051 | .088 | .132 | .181 | .230 | .276 | .312 | .337 | .346 | .336 | .309 | .264 | .205 | .138 | .073 | .021 | |
4 | .000 | .000 | .000 | .002 | .006 | .015 | .028 | .049 | .077 | .113 | .156 | .206 | .259 | .312 | .360 | .396 | .410 | .392 | .328 | .204 | |
5 | .000 | .000 | .000 | .000 | .000 | .001 | .002 | .005 | .010 | .019 | .031 | .050 | .078 | .116 | .168 | .237 | .328 | .444 | .590 | .774 |
1570 n 1571 6
p | .01 | .05 | .10 | .15 | .20 | .25 | .30 | .35 | .40 | .45 | .50 | .55 | .60 | .65 | .70 | .75 | .80 | .85 | .90 | .95 | |
r | 0 | .941 | .735 | .531 | .377 | .262 | .178 | .118 | .075 | .047 | .028 | .016 | .008 | .004 | .002 | .001 | .000 | .000 | .000 | .000 | .000 |
1 | .057 | .232 | .354 | .399 | .393 | .356 | .303 | .244 | .187 | .136 | .094 | .061 | .037 | .020 | .010 | .004 | .002 | .000 | .000 | .000 | 1808 2 1809 | .001 | .031 | .098 | .176 | .246 | .297 | .324 | .328 | .311 | .278 | .234 | .186 | .138 | .095 | .060 | .033 | .015 | .006 | .001 | .000 |
3 | .000 | .002 | .015 | .042 | .082 | .132 | .185 | .236 | .276 | .303 | .312 | .303 | .276 | 。236 | .185 | .132 | .082 | .042 | .015 | .002 | |
1900 4 1901 | .000 | .000 | .001 | .006 | .015 | .033 | .060 | .095 | .138 | .186 | .234 | .278 | .311 | .328 | .324 | .297 | .246 | .176 | .098 | .031 | |
5 | 1948.000 1949.000 | .000 | .000 | .002 | .004 | .010 | .020 | .037 | .061 | .094 | .136 | .187 | .244 | .303 | .356 | .393人工智能科普 | .399 | .354 | .232 | 1988年||
6 | .000 | .000 | .000 | .000 | .000 | .000 | .001 | .002 | .004 | .008 | .016 | .028 | .047 | .075 | .118 | .178 | .262 | .377 | .531 | .735 | 2034年2035年2036年