Estadística Práctica para Ciencia de Datos con Python: Un Enfoque de Alta Calidad
Statistical Experiments and Significance Testing: Covers the principles of experimental design (like A/B testing) to determine if observed effects are truly significant or just random noise. Estadística Práctica para Ciencia de Datos con Python:
A preliminary step involving simple statistics and visualizations (plots, graphs) to understand a dataset before modeling. Data and Sampling Distributions: limitado a n<
_, p_value = stats.normaltest(normales) # H0: los datos son normales print(f"p-valor para normales: p_value:.5f") # >0.05, no rechazamos H0 usamos D'Agostino) _
Estadística Práctica para Ciencia de Datos con Python: Un Enfoque de Alta Calidad
Statistical Experiments and Significance Testing: Covers the principles of experimental design (like A/B testing) to determine if observed effects are truly significant or just random noise.
A preliminary step involving simple statistics and visualizations (plots, graphs) to understand a dataset before modeling. Data and Sampling Distributions:
_, p_value = stats.normaltest(normales) # H0: los datos son normales print(f"p-valor para normales: p_value:.5f") # >0.05, no rechazamos H0