- #数学|统计
求1-1统计tutor（有偿）
enennnnnn3201
最近在准备DS面试，但是我统计真的很差，看面经有时候答案都看不懂，就想问问有没有统计大神愿意跟我一起go through一下面经题。对于懂的人来说应该不难，我主要的问题就是什么时候该用什么distribution，以及下面这两段我都看不懂。我真的很想补一下。可以付$50一小时（可商量）有时间有兴趣的朋友可以联系我 participant024@gmail.com
1) 一共有N个conference room from no.1 to no.N。有K个meeting独立随机分配到这N个 conference room。现在已知1号conference room里面被schedule了一个meeting,问1 号conference room里面被schedule的总共的meeting的数量（已知1号房存在一个 meeting，也就是1号房不为空。在这个条件下求1号房总的meeting数的期望。把 meeting的集合写成 M1,M2,...,Mk，对任意的i, 利用Bayes公式计算条件概率）
4) estimate distribution of number of payments in a month (x轴是number of payments, y是
number of users); 求这个distribution的mean和median, 是不是 会有偏差？ distribution with a long right tail / right skewed distribution
* 指数分布不行，因为是连续的
* 泊松分布：方差和平均数必须一样(the probability of a given number of events occurring in a fixed interval of time or space. If these events occur with a known constant mean rate and independently of the time since the last event)
* zero inflated negative binomial: modeling count variables with excessive zeros and it is usually for overdispersed count outcome variables. Furthermore, theory suggests that the excess zeros are generated by a separate process from the count values and that the excess zeros can be modeled independently.
1) 一共有N个conference room from no.1 to no.N。有K个meeting独立随机分配到这N个 conference room。现在已知1号conference room里面被schedule了一个meeting,问1 号conference room里面被schedule的总共的meeting的数量（已知1号房存在一个 meeting，也就是1号房不为空。在这个条件下求1号房总的meeting数的期望。把 meeting的集合写成 M1,M2,...,Mk，对任意的i, 利用Bayes公式计算条件概率）
4) estimate distribution of number of payments in a month (x轴是number of payments, y是
number of users); 求这个distribution的mean和median, 是不是 会有偏差？ distribution with a long right tail / right skewed distribution
* 指数分布不行，因为是连续的
* 泊松分布：方差和平均数必须一样(the probability of a given number of events occurring in a fixed interval of time or space. If these events occur with a known constant mean rate and independently of the time since the last event)
* zero inflated negative binomial: modeling count variables with excessive zeros and it is usually for overdispersed count outcome variables. Furthermore, theory suggests that the excess zeros are generated by a separate process from the count values and that the excess zeros can be modeled independently.
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