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青の統計学

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<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2025/01/ベイズ因子-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2025/01/ベイズ因子-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

ベイズ因子とは？──定義と直感的解釈

01/01/2025

<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/07/信用区間-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

信頼区間と信用区間の違いをわかりやすく解説

12/29/2024

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/11/マーケティングミックスモデリング-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/11/マーケティングミックスモデリング-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【完全版】MMMを課題設定から考える｜Google Meridian

12/26/2024

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/12/階層ベイズ-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/12/階層ベイズ-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

階層ベイズをわかりやすく解説｜Google Meridianを例に

12/10/2024

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/12/パス解析-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/12/パス解析-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

パス解析をわかりやすく解説：因果推論からMMMまで

12/07/2024

<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2022/04/算数の勉強方法-2-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【統計学】ポアソン分布についてわかりやすく解説

12/06/2024

<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/06/算数の勉強方法-17-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【完全版】二項分布をわかりやすく説明｜統計学

12/06/2024

<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2023/11/ベルヌーイ分布-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

ベルヌーイ分布の基本を徹底解説！期待値・分散の計算方法とは？

12/06/2024

<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/07/操作変数法-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【内生性の解決】操作変数法と2段階OLSをわかりやすく解説

12/05/2024

<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2022/06/算数の勉強方法-4-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【モデル選択】AIC(赤池情報量基準)についてわかりやすく解説

12/05/2024

New Contents

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2025/01/ベイズ因子-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2025/01/ベイズ因子-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

ベイズ因子とは？──定義と直感的解釈

01/01/2025

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/11/マーケティングミックスモデリング-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/11/マーケティングミックスモデリング-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【完全版】MMMを課題設定から考える｜Google Meridian

12/26/2024

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/12/階層ベイズ-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/12/階層ベイズ-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

階層ベイズをわかりやすく解説｜Google Meridianを例に

12/10/2024

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/12/パス解析-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/12/パス解析-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

パス解析をわかりやすく解説：因果推論からMMMまで

12/07/2024

SARIMAモデルについてわかりやすく解説｜定常時系列解析

11/30/2024

統計検定3級｜合格率や出題範囲、勉強法を徹底解説【2024年最新版】

11/02/2024

統計検定2級の難易度と範囲を徹底解説【2024年11月最新版】

08/31/2024

HPD区間をわかりやすく解説｜信頼区間との違いは？

08/11/2024

【統計検定】偏相関係数の概念と計算方法 – 多変量解析の基礎

08/04/2024

【統計検定でも頻出】母比率の差の検定と具体例

08/03/2024

Tag Cloud

AIC Auto Encorder F統計量 GBDT GLM KL divergence MCMC法 MSE PCA p値 ridge回帰 SVM t検定 word2vec カイ２乗分布ガウス過程フィッシャー情報量ブートストラップ法ベルマン方程式ポアソン分布マルコフ連鎖ラグランジュの未定乗数法ランダムウォークランダムフォレストランダム効果ロジスティック回帰不偏性二項分布交差検証法信頼区間再生性分散分析回帰分析回帰非連続デザイン多重共線性指数分布指数型分布族損失関数最尤法標準偏差標準誤差正規分布決定木深層学習相関係数

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<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2025/01/ベイズ因子-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2025/01/ベイズ因子-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

ベイズ因子とは？──定義と直感的解釈

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<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/07/信用区間-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

信頼区間と信用区間の違いをわかりやすく解説

12/29/2024

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/11/マーケティングミックスモデリング-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/11/マーケティングミックスモデリング-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【完全版】MMMを課題設定から考える｜Google Meridian

12/26/2024

<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/12/階層ベイズ-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/12/階層ベイズ-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

階層ベイズをわかりやすく解説｜Google Meridianを例に

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<img width="100" height="80" data-src="https://statisticsschool.com/wp-content/uploads/2024/12/パス解析-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image lazyload" alt="" decoding="async" loading="lazy" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" style="--smush-placeholder-width: 100px; --smush-placeholder-aspect-ratio: 100/80;"/><noscript><img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/12/パス解析-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

パス解析をわかりやすく解説：因果推論からMMMまで

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<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2024/07/操作変数法-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

【内生性の解決】操作変数法と2段階OLSをわかりやすく解説

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<img width="100" height="80" src="https://statisticsschool.com/wp-content/uploads/2022/06/算数の勉強方法-4-100x80.png" class="attachment-thumbnail size-thumbnail wp-post-image" alt="" decoding="async" loading="lazy"/>

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