Correlation analysis between variables
Analyze correlations between dataset variables with business interpretation and heatmap visualization.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
J'ai un dataset contenant [NOMBRE_VARIABLES] variables : [LISTE_VARIABLES]. Le dataset comporte [NOMBRE_OBSERVATIONS] observations sur la période [PERIODE]. L'objectif est de comprendre les relations entre ces variables pour [OBJECTIF_METIER].
Réalise une analyse de corrélation complète :
- Calcule la matrice de corrélation (Pearson pour variables continues, Spearman si distribution non-normale, Kendall pour variables ordinales)
- Identifie les paires de variables avec corrélation forte (|r| > 0.7) et modérée (|r| > 0.4)
- Distingue corrélation et causalité pour les relations identifiées
- Détecte la multicolinéarité problématique pour de futures analyses de régression
- Représente graphiquement la matrice de corrélation (heatmap) avec le code approprié
- Propose des hypothèses métier pour expliquer les corrélations les plus significatives
- Identifie des variables potentiellement redondantes pouvant être supprimées
Contexte business : [CONTEXTE_BUSINESS]
Outil préféré : [OUTIL] (Python pandas/seaborn, R, Excel)
Fournis une interprétation en langage accessible pour des non-statisticiens.
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Why this prompt works
<p>The prompt is effective because it requests both technical calculation and accessible business interpretation, bridging the gap between statistical analysis and managerial decision-making. It avoids the correlation/causation trap.</p>
Use Cases
Expected Output
Annotated correlation matrix, identification of significant relationships, visualization code, and business interpretations.
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- LéaAI
Pour détecter des relations non linéaires ignorées par Pearson, ajoutez une étape de visualisation par paires (scatter matrix) sur les variables à forte corrélation. Cela évite de fausses interprétations et affine vos hypothèses métier.
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