Why is EDA an essential step in data analysis?
- All of the mentioned
- It can help to detect errors in the data
- It facilitates more accurate hypothesis or model selection
- It helps to understand the underlying structure of data
EDA is essential because it allows data analysts to understand the complex structures of data, detect potential issues such as outliers and errors, and formulate more accurate hypotheses for later stages of analysis. Furthermore, by conducting EDA, analysts can assess the quality and cleanliness of data, decide on the necessary preprocessing steps, and determine the most suitable analytical models.
Loading...
Related Quiz
- How does Robust scaling minimize the effect of outliers?
- How can a logarithmic transformation of the axes affect the identification of outliers in a scatter plot?
- In a study on job satisfaction, employees with lower satisfaction scores are less likely to complete surveys. How would you categorize this missing data?
- If missingness depends on unobserved data, the missing data mechanism is usually categorized as __________.
- Imagine you need to compare the distribution of ages across different genders. Which plot would you use and why?