n=2,3,4,5和6的二项式表
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发布时间:2020-12-02 08:01:31
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点击:1907
一个重要的离散随机变量是二项式随机变量。这种类型的变量的分布,称为二项分布,完全由两个参数决定:n和p。这里n是试验次数,p是成功的概率。下表用于n=2,3,4,5和6。每个概率四舍五入到小数点后三位。
在使用表格之前,确定是否应使用二项式分布非常重要。为了使用这种类型的分布,我们必须确保满足以下条件:
- 我们有有限数量的观察或试验。
- teach试验的结果可以分为成功或失败。
- 成功的可能性保持不变。
- 观察结果彼此独立。
二项分布在总共n个独立试验的实验中给出r成功的概率,每个试验具有p的成功概率。概率通过公式C(n,r)pr(1-p)n-r其中C(n,r)是组合的公式。
表中的每个条目按p和r的值排列。对于n。的每个值有一个不同的表
其他表格
对于其他二项式分布表:n=7至9,n=10至11。对于n p和n的情况(1-p)大于或等于10,我们可以使用二项式分布的正态近似。在这种情况下,近似非常好,不需要计算二项式系数。这提供了很大的优势,因为这些二项式计算可能非常复杂。
示例
为了了解如何使用该表,我们将从遗传学考虑以下示例。假设我们有兴趣研究两个父母的后代,我们都知道他们都有隐性和显性基因。后代将继承两个副本的概率隐性基因(因此具有隐性特征)为1/4。
假设我们想考虑一个六口之家中一定数量的孩子具有这种特征的概率。设104 X 105为具有这种特征的孩子的数量。我们看一下表格中的106 n 107 n 6和108 p 109 0.25列,见下文:
0.178、0.356、0.297、0.132、0.033、0.004、0.000
这就意味着我们的例子
- P(X=0)=17.8%,这是没有一个孩子具有隐性特征的概率。
- P(X=1)=35.6%,这是其中一个孩子具有隐性特征的概率。
- P(X=2)=29.7%,这是两个孩子具有隐性特征的概率。
- P(X=3)=13.2%,这是三个孩子具有隐性特征的概率。
- P(X=4)=3.3%,这是四个孩子具有隐性特征的概率。
- P(X=5)=0.4%,这是五个孩子具有隐性特征的概率。
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