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セブンイレブンとコープの卵
黄身の重さに違いはあるのか?
回帰分析と仮説検定による考察
目次
 はじめに
 方法と手順
 結果
 まとめと考察
 おわりに
ぶっちゃけどうでもいいと思っている人は
これ以上読み進めないこと
さもないと目を痛めます
且つ時間の無駄です
はじめに
世間的にはこういわれていますが…→
(※卵の規格基準についてはこちら)
実際に検証した方もいらっしゃる通り、
(パックごとの平均)(MS-M-Lサイズ分類による重さの比較)
というデータが得られています。
したがって、ここからは“黄身の重さはサイズによって一定”では
ないという立場で話を進めたいと思います。
では、「どのサイズの卵であればどのくらいの黄身が入った卵を
買えるのか」 また、「セブンイレブンとコープの卵で黄身の大き
さに違いはあるのか」 知りたいですよね??
・・・ということで少し推測してみました(^^♪
方法と手順
・分析に用いたデータ
※こちらの方の測定結果を使わせて頂きました↓↓↓
https://twitter.com/keigomi29/status/552817007918190592
・使用したソフトウェア
Microsoft ExcelおよびJUSE-Statworks/V5(無料体験版)
・統計手法
回帰分析および仮説検定(パラメトリック)
※サイズ関係なくセブンとコープの卵という括りで群分けしています。
手法についての説明は省きます。興味がある人はぐぐれかすww
・手順
層別ヒストグラム・散布図→仮説検定→回帰分析→データの予測
というのが大まかな流れです。
結果(ヒストグラム)
コープ セブン
イレブン
データ数(n) 34 29
平均値(Xbar) 16.41 16.90
最大値(max) 1.690 1.877
最小値(min) -0.099 0.369
標準偏差(s) 1.690 1.877
ひずみ -0.099 0.369
とがり -0.336 0.750
正規分布に
近似して考えて
もよさそう!
平均値は同じぐらい
だから、2つに違いは
ないかも?
結果(散布図)
y = 0.3313x - 0.3398
y = 0.3019x + 0.4282
10
12
14
16
18
20
22
24
40 45 50 55 60 65 70
黄
身
の
大
き
さ
卵の合計重量(黄身+白身)
Yellow(コープ)
Yellow(セブン)
Linear (Yellow(コープ))
Linear (Yellow(セブン))
コープのほうが
やや黄身が重いの
が得られそう??
コープ セブン
データ数(n) 34 29
相関係数
(r)
0.743 0.665
結果(仮説検定)
ということで疑問を解消するためにとりあえず検定
(2つの差に違いがあるかどうかを調べます)
黄身の重さの差は
バラツキの範囲内
のようです…w
⇒セブンとコープの黄身(g)に違いがある
とは言えない(※同じと言えるではない)
⇒※分散未知なので
t検定を選択
2つは同じものとみなして、一つに統合してもよいのですが…
データが50個しか使えない無料体験版なのであえて別でいきます
SSとLLでは
黄身が10g程度
変わるのかも…
結果(回帰分析: の卵)
黄身の予測値(g)
卵の合計重量(g)
上からざっくりと
SS、M、L、LLに相当
結果(回帰分析: の卵)
どちらもデータ予測にある40gと70gは外挿による予測値なので少
し注意が必要です。(※生データにSSとLLが入っていないため)
セブンも同じ傾向。
Mサイズ買うと
黄身12〜18gが
期待できそうです
まとめと考察
 セブンイレブンとコープの卵、どちらほうが黄身の量が
多いということはない(黄身の重さはサイズに依存だ
が、2つの販売店でばらつきはほぼ同じ)
 SSとLLサイズでは黄身の含量が10gぐらい違う可能性
(料理に応じで使い分ける必要がありますが、まぁ値段
と相談ですねw)
 より正確な推定をするなら、SS〜LLごとに一定数サンプ
リングしての測定が必要。また卵の殻を含めた全質量
があると尚良いかなと思います。
おわりに(分析してみて感想)
散布図の傾きからも分かるように鶏卵に占める黄身の割
合は約30%という点(参考)は黄金比みたいになっていて
大変興味深い。他の品種ではこの割合は変わるのだろう
か??(例えば、うずら、烏骨鶏、ダチョウなど)
ここからは憶測。比較的黄身の割合が多い(もしくは小さ
い)卵を生む品種がいるとすれば、それを決定する遺伝
子的な要素があるかもしれない。
あるいは黄身の比率が決まっているとすれば何か発生
過程の上で生物学的意義があると期待。
以上、私のつまらないお遊びながら、
ご閲覧いただきありがとうございました(^^♪

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卵の黄身の重さの予測