Blocco dei dati sperimentali

Stai collaborando con un'azienda manifatturiera che vuole condurre alcuni esperimenti sulla produttività dei lavoratori. Il loro insieme di dati contiene solo 100 righe, quindi è importante che i gruppi sperimentali siano bilanciati.

Questa è un'ottima occasione per usare le tue conoscenze di blocking per aiutarli. Ti hanno fornito un DataFrame productivity_subjects. Suddividi l'insieme di dati in due gruppi uguali da 50 record ciascuno.

Le librerie numpy e pandas sono già state importate rispettivamente come np e pd.

Questo esercizio fa parte del corso

Progettazione Sperimentale in Python

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Istruzioni dell'esercizio

Seleziona casualmente 50 soggetti dal DataFrame productivity_subjects in un nuovo DataFrame block_1 senza reinserimento.
Imposta una nuova colonna block a 1 per il DataFrame block_1.
Assegna i soggetti rimanenti a un DataFrame chiamato block_2 e imposta la colonna block a 2 per questo DataFrame.
Concatena i due blocchi in un unico DataFrame e stampa il conteggio di ciascun valore nella colonna block per confermare che il blocking ha funzionato.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Randomly assign half
block_1 = productivity_subjects.____(____, random_state=42, ____)

# Set the block column
block_1['block'] = ____

# Create second assignment and label
block_2 = ____
block_2['block'] = ____

# Concatenate and print
productivity_combined = pd.____([block_1, block_2], axis=0)
print(productivity_combined['block'].value_counts())

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: Impostare gli esperimenti Exercise 2: Assegnazione non casuale dei soggetti Exercise 3: Assegnazione casuale dei soggetti Exercise 4: Impostazione dei dati sperimentali Exercise 5: Blocco dei dati sperimentali

Esercizio in corso

Exercise 6: Stratificare un esperimento Exercise 7: Quale è stato stratificato?Exercise 8: Dati normali Exercise 9: Normalità visiva in un esperimento agricolo Exercise 10: Normalità analitica in un esperimento agricolo

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: Choosing the right statistical test Exercise 2: Choosing the right test: petrochemicals Exercise 3: Choosing the right test: human resources Exercise 4: Choosing the right test: finance Exercise 5: Post-hoc analysis following ANOVA Exercise 6: Anxiety treatments ANOVA Exercise 7: Applying Tukey's HSD Exercise 8: Applying Bonferoni correction Exercise 9: P-values, alpha, and errors Exercise 10: Analyzing toy durability Exercise 11: Visualizing durability differences Exercise 12: Role of significance levels Exercise 13: Power analysis: sample and effect size Exercise 14: Effect size purpose Exercise 15: Estimating required sample size for energy study

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!