Stima della dimensione campionaria necessaria per lo studio sull'energia

Nel settore energetico, ai ricercatori capita spesso di valutare l’efficacia di nuove tecnologie o iniziative per migliorare l’efficienza energetica o ridurre i consumi. Si sta progettando uno studio per confrontare l’impatto di due misure di risparmio energetico: "Smart Thermostats" e "LED Lighting". Per garantire che lo studio abbia sufficiente potenza statistica per rilevare una differenza significativa nel risparmio tra queste due misure, eseguirai un’analisi di potenza.

pandas as pd, numpy as np e from statsmodels.stats.power import TTestIndPower sono già caricati.

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Progettazione Sperimentale in Python

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Istruzioni dell'esercizio

Istanzia un oggetto TTestIndPower.
Esegui l’analisi di potenza per stimare la dimensione campionaria necessaria per ciascun gruppo (Smart Thermostats e LED Lighting) per ottenere una potenza di 0.9, assumendo un effetto moderato (Cohen's d = 0.5) e un alpha di 0.05 con gruppi di uguale dimensione.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Progettazione Sperimentale in Python

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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: Scegliere il test statistico giusto Exercise 2: Scegliere il test giusto: petrolchimici Exercise 3: Scegliere il test giusto: risorse umane Exercise 4: Scegliere il test giusto: finanza Exercise 5: Analisi post-hoc dopo l'ANOVA Exercise 6: ANOVA sui trattamenti per l'ansia Exercise 7: Applicare l'HSD di Tukey Exercise 8: Applicare la correzione di Bonferroni Exercise 9: P-value, alpha ed errori Exercise 10: Analizzare la durabilità dei giocattoli Exercise 11: Visualizzare le differenze di durata Exercise 12: Ruolo dei livelli di significatività Exercise 13: Power analysis: dimensione del campione ed entità dell'effetto Exercise 14: Scopo dell'effect size Exercise 15: Stima della dimensione campionaria necessaria per lo studio sull'energia

Esercizio in corso

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!