Finishes the translation of library/statistics.po (#474)

* Adds more translations of library/statistics.po

library/statistics.po

@@ -9,7 +9,7 @@ msgstr ""
 "Project-Id-Version: Python 3.11\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-05-03 00:17+0000\n"
-"PO-Revision-Date: 2023-07-14 00:44+0800\n"
+"PO-Revision-Date: 2023-07-17 18:00+0800\n"
 "Last-Translator: Adrian Liaw <adrianliaw2000@gmail.com>\n"
 "Language-Team: Chinese - TAIWAN (https://github.com/python/python-docs-zh-"
 "tw)\n"
@@ -1224,6 +1224,7 @@ msgid ""
 "Dividing a constant by an instance of :class:`NormalDist` is not supported "
 "because the result wouldn't be normally distributed."
 msgstr ""
+"不支援將常數除以 :class:`NormalDist` 的實例，因為結果將不符合常態分布。"
 
 #: ../../library/statistics.rst:877
 msgid ""
@@ -1233,6 +1234,10 @@ msgid ""
 "Sum_of_normally_distributed_random_variables>`_ represented as instances of :"
 "class:`NormalDist`.  For example:"
 msgstr ""
+"由於常態分布源自於自變數的加法效應 (additive effects)，因此可以\\ `將兩個獨立"
+"的常態分布隨機變數相加與相減 <https://en.wikipedia.org/wiki/"
+"Sum_of_normally_distributed_random_variables>`_，並且表示為 :class:"
+"`NormalDist` 的實例。例如："
 
 #: ../../library/statistics.rst:897
 msgid ":class:`NormalDist` Examples and Recipes"
@@ -1250,6 +1255,9 @@ msgid ""
 "determine the percentage of students with test scores between 1100 and 1200, "
 "after rounding to the nearest whole number:"
 msgstr ""
+"例如，給定 `SAT 測驗的歷史資料 <https://nces.ed.gov/programs/digest/d17/"
+"tables/dt17_226.40.asp>`_，顯示成績為平均 1060、標準差 195 的常態分布。我們要"
+"求出分數在 1100 與 1200 之間（四捨五入至最接近的整數）的學生的百分比："
 
 #: ../../library/statistics.rst:914
 msgid ""
@@ -1265,13 +1273,19 @@ msgid ""
 "analytically, :class:`NormalDist` can generate input samples for a `Monte "
 "Carlo simulation <https://en.wikipedia.org/wiki/Monte_Carlo_method>`_:"
 msgstr ""
+"欲估計一個不易透過解析方法求解的模型的分布，:class:`NormalDist` 可以產生輸入"
+"樣本以進行 `Monte Carlo 模擬 <https://en.wikipedia.org/wiki/"
+"Monte_Carlo_method>`_："
 
 #: ../../library/statistics.rst:940
 msgid ""
 "Normal distributions can be used to approximate `Binomial distributions "
 "<https://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample "
 "size is large and when the probability of a successful trial is near 50%."
 msgstr ""
+"當樣本數量夠大，且試驗成功的機率接近 50%，可以使用常態分布來近似\\ `二項分布 "
+"(Binomial distributions) <https://mathworld.wolfram.com/BinomialDistribution."
+"html>`_。"
 
 #: ../../library/statistics.rst:945
 msgid ""
@@ -1281,10 +1295,14 @@ msgid ""
 "talks.  Assuming the population preferences haven't changed, what is the "
 "probability that the Python room will stay within its capacity limits?"
 msgstr ""
+"例如，一場有 750 位參加者的開源研討會中，有兩間可容納 500 人的會議室。一場是"
+"關於 Python 的講座，另一場則是關於 Ruby 的。在過去的會議中，有 65% 的參加者傾"
+"向參與 Python 講座。假設參與者的偏好沒有改變，那麼 Python 會議室未超過自身容"
+"量限制的機率是？"
 
 #: ../../library/statistics.rst:976
 msgid "Normal distributions commonly arise in machine learning problems."
-msgstr ""
+msgstr "常態分布常在機器學習問題中出現。"
 
 #: ../../library/statistics.rst:978
 msgid ""
@@ -1293,19 +1311,24 @@ msgid ""
 "challenge is to predict a person's gender from measurements of normally "
 "distributed features including height, weight, and foot size."
 msgstr ""
+"維基百科有個 `Naive Bayesian Classifier 的優良範例 <https://en.wikipedia.org/"
+"wiki/Naive_Bayes_classifier#Person_classification>`_。課題為從身高、體重與鞋"
+"子尺寸等符合常態分布的特徵量測值中判斷一個人的性別。"
 
 #: ../../library/statistics.rst:983
 msgid ""
 "We're given a training dataset with measurements for eight people.  The "
 "measurements are assumed to be normally distributed, so we summarize the "
 "data with :class:`NormalDist`:"
 msgstr ""
+"給定一組包含八個人的量測值的訓練資料集。假設這些量測值服從常態分布，我們可以"
+"利用 :class:`NormalDist` 來總結資料："
 
 #: ../../library/statistics.rst:996
 msgid ""
 "Next, we encounter a new person whose feature measurements are known but "
 "whose gender is unknown:"
-msgstr ""
+msgstr "接著，我們遇到一個新的人，他的特徵量測值已知，但性別未知："
 
 #: ../../library/statistics.rst:1005
 msgid ""
@@ -1314,10 +1337,16 @@ msgid ""
 "the prior times the product of likelihoods for the feature measurements "
 "given the gender:"
 msgstr ""
+"從可能為男性或女性的 50% `先驗機率 (prior probability) <https://en.wikipedia."
+"org/wiki/Prior_probability>`_ 為開端，我們將後驗機率 (posterior probability) "
+"計算為先驗機率乘以給定性別下，各特徵量測值的概度乘積："
 
 #: ../../library/statistics.rst:1020
 msgid ""
 "The final prediction goes to the largest posterior. This is known as the "
 "`maximum a posteriori <https://en.wikipedia.org/wiki/"
 "Maximum_a_posteriori_estimation>`_ or MAP:"
 msgstr ""
+"最終的預測結果將取決於最大的後驗機率。這被稱為\\ `最大後驗機率 (maximum a "
+"posteriori) <https://en.wikipedia.org/wiki/"
+"Maximum_a_posteriori_estimation>`_ 或者 MAP："