When a numeric column contains missing values, which approach is recommended for calculations?

When a numeric column contains missing values, which approach is recommended for calculations?

• A. Include Missing Values By Treating Them As Zeros
• B. Exclude Records With Missing Values
• C. Use Only Valid Values And Exclude Missing From Calculations
• D. Impute Missing Values With Random Numbers

Answer: (C)

When a numeric column has missing values, calculations should use only the valid numbers and ignore the missing entries. This keeps results honest about what is actually observed and avoids distorting results by guessing what the missing data might be. For example, if the column has 3, 7, missing, and 5, the mean should be calculated as (3 + 7 + 5) / 3 = 5, using the three non-missing values. Including the missing as zero would yield (3 + 7 + 0 + 5) / 4 = 3.75, which misrepresents the data. Imputing with random numbers or removing records with missing values each have their own drawbacks, such as introducing randomness or reducing the dataset size, respectively. So the best approach is to base calculations on the valid values and exclude the missing ones.

When a numeric column has missing values, calculations should use only the valid numbers and ignore the missing entries. This keeps results honest about what is actually observed and avoids distorting results by guessing what the missing data might be. For example, if the column has 3, 7, missing, and 5, the mean should be calculated as (3 + 7 + 5) / 3 = 5, using the three non-missing values. Including the missing as zero would yield (3 + 7 + 0 + 5) / 4 = 3.75, which misrepresents the data. Imputing with random numbers or removing records with missing values each have their own drawbacks, such as introducing randomness or reducing the dataset size, respectively. So the best approach is to base calculations on the valid values and exclude the missing ones.