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收起看板(711)
1F推: 你可以自己推導一下最小平方法下的 alpha, beta 估計式02/14 00:02
5F→: 假設估計後模型為 Y = 0.1+2X，當 X<-0.05、X>0.4502/13 13:23
6F→: Y_hat 就有可能違反機率公理，所以才會使用 logit reg02/13 13:24
1F推: 觀念錯誤，樣本大仍然是t分配，只是漸進於常態02/11 10:01
13F推: 大於30之所以查Z表是以前電腦不普及的近似法02/12 16:34
14F→: 另外，如果母體變異數已知，那直接查Z表即可02/12 16:35
15F推: 母體變異數未知，用樣本變異數代替，此時檢定統計量服從02/12 16:37
16F→: t 分配而非 Z 分配02/12 16:37
1F推: 現金流量發生時點在第二、三、四、五年，共四筆才對11/18 01:55
2F→: 大部分的教科書比較傾向 r.v~Normal(μ，變異數)08/27 09:31
3F→: 不過這只是表達方式不一樣，不需要擔心08/27 09:31
1F→: 第一題可以想想看卜瓦松分配和指數分配的關聯性04/04 08:34
2F→: 下一題就只是在考獨立、變異數、共變數的性質04/04 08:36
4F→:恐怕要再把機率論讀熟了....06/26 13:11
5F→:你這樣設的話 n = 2,3,... 跟一開始 N 的定義就不一樣了06/26 13:14
6F→:所以這樣新的 E(N) 根本不會等於 mu_N06/26 13:15
3F→:計算 compound Poisson process 的期望值會用到類似的方法06/21 17:37
5F推:先給定隨機變數N的數值，你不覺得比較容易處理嗎？06/22 11:18
7F→:N屬於正整數，設 n-1 的話還會服從原本的分配嗎06/22 14:53
1F推:由於(1+8+24+32+16)/81 = 1，當作是各個出象的機率直06/14 19:27
2F→:根據期望值的定義，即 X=0 (p=16/81)、X=1 (p=32/81)...06/14 19:28
3F→:以此類推就可得知 X 的分配，故 P(X>2) = (8+1)/81 = 1/906/14 19:29
5F→:利用驗證動差母函數的可積性，就可證明其為離散型變數06/15 12:28
4F→:電阻 = 電壓/電流 這樣有沒有很清楚？03/18 23:42