第 19786 題：估計量變異數

原題與已知條件

共同有限樣本空間與逐theta estimator sampling law。操作=estvar-second-moment-identity；資料={"laws":[[[1,20],[2,20],[3,20],[14,20]],[[2,24],[3,24],[4,24],[15,24]]],"statistic":[0,1,12,13],"parameter":0}。請依 exact_ordered_tuple_declared_by_interface 欄序回答。

• G001 — 共同有限樣本空間與逐theta estimator sampling law。操作=estvar-second-moment-identity；資料={"laws":[[[1,20],[2,20],[3,20],[14,20]],[[2,24],[3,24],[4,24],[15,24]]],"statistic":[0,1,12,13],"parameter":0}。請依 exact_ordered_tuple_declared_by_interface 欄序回答。

定義與適用範圍

沿用原題明示的論域、假設與來源 MKO 定義。本題已完成第二對話數理與中英語義覆核，並由原始參數逐題獨立重算模型、答案與每個算術步驟；原題仍為 candidate。

• mko-estimator-variance

逐步推導

source operation=estvar-second-moment-identity;original snapshot and mathematics retained

explicit ordered source output fields=[parameter_index, second_moment, expectation, second_minus_squared_mean, variance, equal]

variance parameter0 complete law mass := sum(1/20, 1/10, 3/20, 7/10)

variance parameter0 complete law mass = 1

original parameter SLOT=0;ALL original outcome masses=[1/20, 1/10, 3/20, 7/10];ONE fixed statistic=[0, 1, 12, 13]

complete same-law mass=1;all masses nonnegative;positive-mass support=[0, 1, 2, 3];null outcomes RETAINED

variance parameter1 complete law mass := sum(1/12, 1/8, 1/6, 5/8)

variance parameter1 complete law mass = 1

original parameter SLOT=1;ALL original outcome masses=[1/12, 1/8, 1/6, 5/8];ONE fixed statistic=[0, 1, 12, 13]

complete same-law mass=1;all masses nonnegative;positive-mass support=[0, 1, 2, 3];null outcomes RETAINED

variance parameter0 outcome0 weighted T := mul(1/20, 0)

variance parameter0 outcome0 weighted T = 0

variance parameter0 outcome1 weighted T := mul(1/10, 1)

variance parameter0 outcome1 weighted T = 1/10

variance parameter0 outcome2 weighted T := mul(3/20, 12)

variance parameter0 outcome2 weighted T = 9/5

variance parameter0 outcome3 weighted T := mul(7/10, 13)

variance parameter0 outcome3 weighted T = 91/10

variance parameter0 SAME-law mean := sum(0, 1/10, 9/5, 91/10)

variance parameter0 SAME-law mean = 11

variance parameter0 outcome0 T minus SAME mean := sub(0, 11)

variance parameter0 outcome0 T minus SAME mean = -11

variance parameter0 outcome0 centered square := mul(-11, -11)

variance parameter0 outcome0 centered square = 121

variance parameter0 outcome0 weighted centered square := mul(1/20, 121)

variance parameter0 outcome0 weighted centered square = 121/20

variance parameter0 outcome1 T minus SAME mean := sub(1, 11)

variance parameter0 outcome1 T minus SAME mean = -10

variance parameter0 outcome1 centered square := mul(-10, -10)

variance parameter0 outcome1 centered square = 100

variance parameter0 outcome1 weighted centered square := mul(1/10, 100)

variance parameter0 outcome1 weighted centered square = 10

variance parameter0 outcome2 T minus SAME mean := sub(12, 11)

variance parameter0 outcome2 T minus SAME mean = 1

variance parameter0 outcome2 centered square := mul(1, 1)

variance parameter0 outcome2 centered square = 1

variance parameter0 outcome2 weighted centered square := mul(3/20, 1)

variance parameter0 outcome2 weighted centered square = 3/20

variance parameter0 outcome3 T minus SAME mean := sub(13, 11)

variance parameter0 outcome3 T minus SAME mean = 2

variance parameter0 outcome3 centered square := mul(2, 2)

variance parameter0 outcome3 centered square = 4

variance parameter0 outcome3 weighted centered square := mul(7/10, 4)

variance parameter0 outcome3 weighted centered square = 14/5

variance parameter0 full centered variance := sum(121/20, 10, 3/20, 14/5)

variance parameter0 full centered variance = 19

FIXED parameter SLOT=0;original same law=[1/20, 1/10, 3/20, 7/10];same statistic=[0, 1, 12, 13];its own mean=11;ALL centered deviations=[-11, -10, 1, 2]

ALL nonnegative weighted centered squares=[121/20, 10, 3/20, 14/5];variance=19;variance0 iff EVERY positive-mass outcome has T=THIS mean

variance second moment outcome0 T squared := mul(0, 0)

variance second moment outcome0 T squared = 0

variance second moment outcome0 weighted T squared := mul(1/20, 0)

variance second moment outcome0 weighted T squared = 0

variance second moment outcome1 T squared := mul(1, 1)

variance second moment outcome1 T squared = 1

variance second moment outcome1 weighted T squared := mul(1/10, 1)

variance second moment outcome1 weighted T squared = 1/10

variance second moment outcome2 T squared := mul(12, 12)

variance second moment outcome2 T squared = 144

variance second moment outcome2 weighted T squared := mul(3/20, 144)

variance second moment outcome2 weighted T squared = 108/5

variance second moment outcome3 T squared := mul(13, 13)

variance second moment outcome3 T squared = 169

variance second moment outcome3 weighted T squared := mul(7/10, 169)

variance second moment outcome3 weighted T squared = 1183/10

variance full SAME-law second moment := sum(0, 1/10, 108/5, 1183/10)

variance full SAME-law second moment = 140

variance square of SAME-law mean := mul(11, 11)

variance square of SAME-law mean = 121

variance second moment minus mean squared := sub(140, 121)

variance second moment minus mean squared = 19

FIXED original law at SLOT=0;mass sum1;E[T]=11;E[T^2]=140;mean squared=121

expand sum mass*(T-mean)^2=E[T^2]-2*mean*E[T]+mean^2*1=19=centered variance=19

逐步反向核對

• S036 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S035 · premise_and_result_audit — 以精確有理數重算 variance second moment minus mean squared，不以小數近似替代。
• S034 · premise_and_result_audit — 以精確有理數重算 variance square of SAME-law mean，不以小數近似替代。
• S033 · premise_and_result_audit — 以精確有理數重算 variance full SAME-law second moment，不以小數近似替代。
• S032 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome3 weighted T squared，不以小數近似替代。
• S031 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome3 T squared，不以小數近似替代。
• S030 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome2 weighted T squared，不以小數近似替代。
• S029 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome2 T squared，不以小數近似替代。
• S028 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome1 weighted T squared，不以小數近似替代。
• S027 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome1 T squared，不以小數近似替代。
• S026 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome0 weighted T squared，不以小數近似替代。
• S025 · premise_and_result_audit — 以精確有理數重算 variance second moment outcome0 T squared，不以小數近似替代。
• S024 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S023 · premise_and_result_audit — 以精確有理數重算 variance parameter0 full centered variance，不以小數近似替代。
• S022 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome3 weighted centered square，不以小數近似替代。
• S021 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome3 centered square，不以小數近似替代。
• S020 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome3 T minus SAME mean，不以小數近似替代。
• S019 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome2 weighted centered square，不以小數近似替代。
• S018 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome2 centered square，不以小數近似替代。
• S017 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome2 T minus SAME mean，不以小數近似替代。
• S016 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome1 weighted centered square，不以小數近似替代。
• S015 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome1 centered square，不以小數近似替代。
• S014 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome1 T minus SAME mean，不以小數近似替代。
• S013 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome0 weighted centered square，不以小數近似替代。
• S012 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome0 centered square，不以小數近似替代。
• S011 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome0 T minus SAME mean，不以小數近似替代。
• S010 · premise_and_result_audit — 以精確有理數重算 variance parameter0 SAME-law mean，不以小數近似替代。
• S009 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome3 weighted T，不以小數近似替代。
• S008 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome2 weighted T，不以小數近似替代。
• S007 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome1 weighted T，不以小數近似替代。
• S006 · premise_and_result_audit — 以精確有理數重算 variance parameter0 outcome0 weighted T，不以小數近似替代。
• S005 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S004 · premise_and_result_audit — 以精確有理數重算 variance parameter1 complete law mass，不以小數近似替代。
• S003 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S002 · premise_and_result_audit — 以精確有理數重算 variance parameter0 complete law mass，不以小數近似替代。
• S001 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。

結論

(0, 140, 11, 19, 19, 是)

依上列完整代值及條件核對得到原題答案；原題編號、格式與狀態保持不變。

本題覆核與來源綁定

覆核日期：2026-10-26

原題、答案、步驟表達式、數值 AST 與既有歷史保持原樣；原來的候選狀態屬於來源歷史，不覆寫為形式化證明。