Categorical Variables and Data Handling

1. What is a categorical variable?

• 1. A variable that represents numerical values

• 1. A variable that represents categories or groups

• 1. A variable that always has missing values

• A variable that changes over time

1. Which of the following is an example of a categorical variable?

• • Temperature in Celsius

• • Height in centimeters

• • Age in years

• • Blood type (A, B, AB, O)

1. What type of categorical variable has a meaningful order?

• • Nominal

• • Ordinal

• • Continuous

• • Discrete

1. Which of the following is an example of an ordinal variable?

• • Gender (Male, Female)

• • Marital status (Single, Married, Divorced)

• • Education level (High school, Bachelor’s, Master’s, PhD)

• • Eye color (Brown, Blue, Green)

1. How can categorical variables be represented in a dataset?

• • As integers only

• • As floating-point numbers

• • As strings or numbers with labels

• • Only as binary value

1. Which of the following variables would NOT be considered categorical?

• • Zip codes

• • Social Security Numbers

• • Temperature in Fahrenheit

• Country names

15. What is a major drawback of dropping rows with missing values?

• • It is computationally expensive

• • It may lead to biased results

• • It only works for categorical data

• • It increases memory usage

16. Which imputation method is best for filling missing categorical values?

• • Mean imputation

• • Mode imputation

• • Linear regression

• Median imputation

22. Why is mean imputation not always the best strategy?

• • It can distort relationships between variables

• • It is too computationally expensive

• • It is difficult to implement

• It removes useful data

26. Which type of plot is best for visualizing the distribution of a categorical variable?

• • Histogram

• • Bar chart

• • Scatter plot

• Line graph