Estimer la taille d’échantillon nécessaire pour l’étude énergétique

Dans le secteur de l’énergie, les chercheurs doivent souvent évaluer l’efficacité de nouvelles technologies ou initiatives visant à améliorer l’efficacité énergétique ou à réduire la consommation. Une étude est en cours de conception pour comparer l’impact de deux mesures d’économie d’énergie : « Smart Thermostats » et « LED Lighting ». Pour garantir que l’étude dispose d’une puissance suffisante pour détecter une différence significative d’économies d’énergie entre ces deux mesures, vous allez réaliser une analyse de puissance.

pandas as pd, numpy as np, et from statsmodels.stats.power import TTestIndPower sont chargés.

Cet exercice fait partie du cours

<cours>Conception expérimentale en Python</cours>

Instructions de l’exercice

Instanciez un objet TTestIndPower.
Réalisez l’analyse de puissance pour estimer la taille d’échantillon nécessaire pour chaque groupe (Smart Thermostats et LED Lighting) afin d’atteindre une puissance de 0,9, en supposant un effet modéré (Cohen’s d = 0,5) et un alpha de 0,05 avec des groupes de taille égale.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Instantiate a TTestIndPower object
power_analysis = ____

# Conduct a power analysis to determine the required sample size
required_n = power_analysis.solve_power(
    effect_size=____, 
    alpha=____, 
    power=____, 
    ratio=____)

print(required_n)

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Conception expérimentale en Python</cours>

IntermédiaireNiveau de compétence

4.8+

Commencer le cours gratuitement

Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Factorial designs: principles and applications Exercise 2: Understanding marketing campaign effectiveness Exercise 3: Heatmap of campaign interactions Exercise 4: Factorial designs and randomized block designs Exercise 5: Randomized block design: controlling variance Exercise 6: Implementing a randomized block design Exercise 7: Visualizing productivity within blocks by incentive Exercise 8: ANOVA within blocks of employees Exercise 9: Covariate adjustment in experimental design Exercise 10: Importance of covariates Exercise 11: Covariate adjustment with chick growth

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

Exercise 1: Choisir le bon test statistique Exercise 2: Choisir le bon test : pétrochimie Exercise 3: Choisir le bon test : ressources humaines Exercise 4: Choisir le bon test : finance Exercise 5: Analyse post-hoc après une ANOVA Exercise 6: ANOVA sur les traitements de l’anxiété Exercise 7: Appliquer le test HSD de Tukey Exercise 8: Appliquer la correction de Bonferroni Exercise 9: Valeurs p, alpha et erreurs Exercise 10: Analyser la durabilité des jouets Exercise 11: Visualiser les différences de durabilité Exercise 12: Rôle des seuils de significativité Exercise 13: Analyse de puissance : taille d’échantillon et taille d’effet Exercise 14: Objectif de la taille d'effet Exercise 15: Estimer la taille d’échantillon nécessaire pour l’étude énergétique

Exercice actuel

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

Exercise 1: Synthesizing insights from complex experiments Exercise 2: Visualizing loan approval yield Exercise 3: Exploring customer satisfaction Exercise 4: Effectively communicating experimental data Exercise 5: Addressing complexities in experimental data Exercise 6: Check for heteroscedasticity in shelf life Exercise 7: Exploring and transforming shelf life data Exercise 8: Applying nonparametric tests in experimental analysis Exercise 9: Visualizing and testing preservation methods Exercise 10: Further analyzing food preservation techniques Exercise 11: Congratulations!