How Statistics Became a Model-blind Data-reduction Enterprise? Sewall Wright

Wright studied genetics at Harvard. While he was working at the University of Chicago, he interested in the inheritance of coat colour in guinea pigs. He found that it is nearly impossible to breed an all-white or all-coloured guinea pig, even the most inbred families which contradict the prediction of that time in which a …

How Human (Sapiens) Can Coorpate in huge numbers? Telling and Believing in Fictional Stories.

Date: 20210613 We know from last time that there are at least 6 species of human 50,000 years ago. Human today belongs to a species called Sapiens. Actually, the first time Sapiens met another species, Neanderthals, we lost. Around 70,000 years ago, Sapiens started spreading everywhere around the whole world. They came up with so …

Understanding Human Behaviors from the Perspective of Science │ 柯文哲 台北市長

YouTube 的演算法推薦了柯文哲2015年在台大演講的錄影给我,題目是Understanding Human Behaviors from the Perspective of Science,中文譯做,從科學角度理解人類行為。看著,覺得很有趣,也富有啟發性,所以一看完就寫下一些總結。有興趣的朋友,可以去YouTube觀看: 柯文哲說的生動很多,我的總結比較悶,十分推薦大家去看。 人們常說,我們看到所以相信,但現實上,我們常常只看到自己相信的,甚至相信我們希望它是真的東西。怎麼解決?如何減少bias?這就是這演講的主題。 邏輯運算,具體就是Truth Table,什麼conjunction, disjunction, negation, conditional, biconditional 熟度的好處就是在分析問題的時候能夠有緊密的邏輯,避免過於主觀看問題 當你知道越多,猜對的機率就會更高 這好像很廢話,但其實現實上我們往往忽略了這一步,對著問題有太多可能的原因,通過新的資訊把可能性縮小,就能得出結論 他提到Bayes Theorem,這個Theorem 說的就是如果有多一些information,概率不會比不知道低:P(S|T) = P(S^T)/P(T) where P(T) <= 1 用機率思考問題 能很快判斷一件事情的機率有多大 重複發生的事情的機率很低 但是,人常常受到期望影響,當機率低事件發生時,我們常常都傾向相信它時機制內的低概率事件,但很多時候它反而是機制外的,比如說男朋友常常遲到是因爲堵車,還是他不愛你? 成功是例外,失敗是常態 當處於劣勢是,只有創新才有出路,因為如果方法一樣,你怎麼打敗資源多的人?但是,還是失敗是常態。 除了邏輯運算和機率思考,專業知識也很重要(domain knowledge) 遇到困難,問自己有沒有第三個可能?We are limited by our own imagination 以簡馭繁,做事情有SOP,在任何情況都有70%戰鬥力 當然會有例外,但70%有用。 人生只是一個過程,沒有目的,因為最後都會死亡。 想通了,人生就沒有困難

居屋2020

Date: 12/06/2021 香港的居屋,無論是一手市場還是二手市場,都是供不應求,如果你有幸抽到就好像中了六合彩頭獎一樣。最近,有家人抽到了,所以就分析一下,居屋2020的樓盤。 居屋2020總共有4個新的樓盤,分別是鑽石山(啟翔苑)、火炭(彩禾苑)、馬鞍山(錦駿苑),和粉嶺(山麗苑),在收到通知時,鑽石山的樓盤已經賣得八八九九,所以就不花時間分析,但這個樓盤的確是坐落市區中心,非常不錯,這次主要想看看粉嶺的山麗苑,一個大家都覺得很遠很不方便的地方。 為什麼不看火炭(彩禾苑)和馬鞍山(錦駿苑)呢?是因為火炭(彩禾苑)是單橦大廈,而且走上去要一段長斜路,離地鐵站遠,所以不考慮;而馬鞍山(錦駿苑)的確是可以考慮,但看完粉嶺(山麗苑)就覺得馬鞍山(錦駿苑)很一般。 粉嶺(山麗苑)現時周邊的確是什麼也沒有,離最近的大型商場,聯湖墟,大概有5分鐘的車程,如果要到市區上班的話,就要每天長征。這些不好,大家都知到,而且常常看評論不停的說,如果你問抽居屋的人,十之八九可能都會排粉嶺(山麗苑)到最後,但分析下來,它其實沒有那麼差。 一個發展區的發展是要很長時間的規劃,由2013年政府的文件可以看到粉嶺北是一個十分大的發展區,古洞北「發展大綱圖」涵蓋的規劃區佔地約447公頃,在全面發展後,該區可容納人約114,300新增人口;粉嶺北新發展區涵蓋的規劃區佔地約165公頃。全面發展後,該區可容納約73,800新增人口。雖然粉嶺(山麗苑)[紅色星星]並不在發展區內,但距離最近的發展區,只有5分鐘車程。未來10到20年,這里將會發展成有20萬人口的新市鎮。 由於是用新市鎮的模式出規劃和發展,基建配套和整體的佈局都會有詳細的考慮,比如有新的北環線連接東鐵線和西鐵線,有新的口岸往反內地,新的公路連接各發展區,單車徑(最近不是大家都去嗎?)。所以現在沒有的配套會慢慢的好起來,而且時間不用等太久,第一階段大概在2026年完成,第二階段大概在2031年,5到10年這粉嶺北會有很大的變化。 而且,另外一個有趣的現像是,一河之隔的深圳是萬家燈火,但在河的另一邊就是一片荒蕪,形成強烈的對比,隨這內地經濟發展的厲害,鄰近深圳的地方反而有優勢,口岸不再單單是往反的關口,更可以發展成一個經濟區,當然從規劃上看還是以住宅為主。 看到這里,我就覺得粉嶺(山麗苑),如果你是可以等個5到10年的話,是一個不錯的選擇,而且它價錢便宜5700/ft2,有一個比較大的折扣。

How Statistics Became a Model-blind Data-reduction Enterprise? Karl Pearson

In the last blog post, we have covered Francies Galton and his Galton Board. In this post, we will talk about Karl Peason. Pearson was affected by Galton’s idea on correlation. He believes correlation is bigger than causation. Causation was reduced to nothing more than a special case of correlation. He said, “That a certain …

How Statistics Became a Model-blind Data-reduction Enterprise? Francies Galton.

Date: 5 June 2021 This is Chapter 2 of the book, The Book of Why. This chapter is an account of the history of statistics and how it departs from understanding causation to only correlation. I personally did not validate the accuracy and completeness of the stories in the Chapter. I feel that the Author …

If causation is not correlation, then what is it?

Date: 30 May 2021 I am currently reading a book called The Book of Why. I just finished Chapter 1, The Ladder of Causation, and would like to give you a quick summary on what I have learnt. In the recent advancement and success of machine learning and artificial intelligence, it seems that many problems …

Chapter 3 Summary of Managing a Consumer Lending Business

Date: April 27, 2021 This chapter is not about how to develop a scorecard. Rather, it focuses on the key steps, resources needed, and evaluations a manager should know. I have written two summaries on scorecard development which covers most of the points in this chapter, especially on the key steps and resources needed. You …

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