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Iori retweeted
データが極端に少ない状況下で、広告の露出頻度やリーチ率を正確に推定するための確率モデル(特にNegative Binomial Distribution)をRで実装する方法を、理論から実践まで解説。 
・Poisson分布+Gamma事前分布からNegative Binomial分布を導出し、過分散・ゼロ過剰なカウントデータをモデリングする考え方
・限られたCM露出データからリーチ率(r_g = 1 - P(F=0))や累積リーチ曲線を推定するRコード(dnbinom / pnbinomの活用例)
・lm()などの単純回帰では対応しにくいスパースデータへの実践的なアプローチと、推定精度を上げるポイント 
顧客の購入頻度・サイト訪問回数など、他のビジネスにおけるカウントデータの過分散問題にもそのまま応用可能。階層モデルに拡張して「グループ間で情報を借りる」手法のヒントも得られます。 
マーケティングの実務課題を題材に、理論の導出 → Rでの実装 → 具体的なデータ例まで一貫して解説されている。Rでの実践的な発表資料で「すぐに試せる」レベルが高いです。 speakerdeck.com/dropout009/j…
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Partytime Products (HK) Co., Ltd retweeted
First binomial formula.
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why am i sitll in binomial combination hell its been a month😐😐
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Replying to @tedlieu
You suck at probability and stats. I bet you've never even heard of binomial probability.
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No wonder you are binomial
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Replying to @Socrates_Arg
el primero es distribución binomial no
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SeoulJack retweeted
The #binomial distribution was first studied in the context of games of pure chance, and it's now widely used to analyze data in many fields. It models the number of successes when performing n trials of an experiment with a probability of success p. #MathType #math #mathfacts
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Replying to @Math_files
The binomial looks great in Spanish 😁
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