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Microsoft Excel でセグメンテッド・リグレーション・モデルをフィッティングすることによって,値検出
Amy J Hopper1, Angus M Brown1,2
1School of Life Sciences, University of Nottingham, Nottingham NG7 2UH, UK.
MethodsX
|September 2, 2025
まとめ
この研究では,Microsoft ExcelのSOLVERを使用した,ユーザーフレンドリーなセグメンテッド回帰分析法が紹介されています. このアプローチは,高度なプログラミングスキルを必要とせずに,実験データにおける値検出を簡素化します.
科学分野:
- バイオ統計学
- データ分析
- 科学的コンピューティング
背景:
- セグメンテッド回帰分析は,データの移行点を特定するために不可欠ですが,しばしば特殊なプログラミング知識が必要です.
- MATLABとRの既存のツールは,その複雑さのために多くの研究者がアクセスできません.
研究 の 目的:
- セグメンテッド・リグレッション分析のための一般に適用可能な方法を示します.
- 簡単に入手可能なソフトウェアを使用して,臨界値を検出し,実験データを異なる機能に合わせることを研究者に可能にする.
- 様々な機能タイプを組み込む方法の柔軟性を実証する.
主な方法:
- マイクロソフト エクセルの SOLVER アドインを利用して最小二乗を繰り返す.
- 実験データの簡単な入力と分析のためのスプレッドシートテンプレートを開発しました.
- 2つの異なるセグメントされた線形関数と推定された移行点をデータに合わせる方法が適用されます.
主要な成果:
- Microsoft Excel の SOLVER を使用したセグメントリグレッション分析の方法を成功裏に実証しました.
- この方法は,実験データの移行点を効果的に推定します.
- このアプローチは,線形関数と非線形関数の組み合わせを含むように拡張された.
結論:
- この方法は,プログラミングの専門知識のない研究者に適した,セグメンテッド・リグレーション・アナリストのためのユーザーフレンドリーな代替手段を提供します.
- このアプローチは,実験的な研究におけるデータの迅速な処理と値検出を容易にする.
- この方法の柔軟性は,データ分析における多様な機能的関係に対応するための修正を可能にします.
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