import numpy as np import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('banco.csv') # Quartiles for the 'age' column q1 = np.quantile(df['age'], 0.25 ...

Daniel Liberto is a journalist with over 10 years of experience working with publications such as the Financial Times, The Independent, and Investors Chronicle. Khadija Khartit is a strategy, ...

In the world of statistics and data analysis, it is important to understand variability and precision. Two fundamental concepts that aid in this understanding are ...

ExcelPivotTable.Calculate(refresh) Calculates the pivot table. Parameter 0 - if true the cache will be refreshed before calculating. If false the existing cache will be used. If no cache exists the ...

Abstract: With the continuous expansion of wind farms, cleaning up abnormal data has become increasingly crucial. This article introduces a quartile based method for clearing abnormal data from wind ...

Quartiles are a useful statistical tool for organizing and interpreting large datasets by dividing the data into four equal parts. They are especially helpful in identifying central tendencies, like ...

Abstract: To address the characteristics that wind power is volatile and difficult to predict, firstly, the abnormal data in the original wind power data set are identified and removed by the ...

If you’re a journalist who reads academic research, you’ve likely seen the term “standard deviation” many times. If you’re not sure what it means or how to explain it to audiences, keep reading, ...