Visualizar la productividad dentro de los bloques por incentivo

Siguiendo con el ejemplo de productividad de personas trabajadoras, explorarás si las puntuaciones de productividad están distribuidas en los datos como cabría esperar con una asignación aleatoria del tratamiento. Ten en cuenta que este es un paso de comprobación previa; aún no se ha realizado el tratamiento ni el análisis posterior sobre el impacto de los tres tratamientos.

seaborn y matplotlib.pyplot están cargados como sns y plt, respectivamente.

Este ejercicio forma parte del curso

Diseño experimental en Python

Instrucciones del ejercicio

Visualiza las puntuaciones de productividad dentro de los bloques según el tratamiento usando un boxplot con 'block' para x, 'productivity_score' para y y 'Treatment' para hue.

ejercicio interactivo práctico

Prueba este ejercicio completando este código de ejemplo.

# Make a plot showing how productivity_score varies within blocks
sns.____(x='____', 
            y='____', 
            hue='____', 
            data=prod_df)

plt.show()

Editar y ejecutar código

Este ejercicio forma parte del curso

Diseño experimental en Python

IntermedioNivel de habilidad

4.8+

Empieza el curso gratis

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: Diseños factoriales: principios y aplicaciones Exercise 2: Comprender la eficacia de las campañas de marketing Exercise 3: Mapa de calor de las interacciones de la campaña Exercise 4: Diseños factoriales y diseños de bloques aleatorizados Exercise 5: Diseño de bloques aleatorizados: control de la varianza Exercise 6: Aplicación de un diseño de bloques aleatorizados Exercise 7: Visualizar la productividad dentro de los bloques por incentivo

Ejercicio actual

Exercise 8: ANOVA en bloques de empleados Exercise 9: Ajuste de covariables en el diseño experimental Exercise 10: Importancia de las covariables Exercise 11: Ajuste de covariables con el crecimiento de los pollitos

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!