All MCQs

A scatter plot represents each observation by a point positioned using two numerical variables. Patterns in the point cloud can reveal association, clusters, and outliers.

A histogram groups continuous values into intervals and displays their frequencies. Its shape can reveal skewness, multiple modes, gaps, and concentration.

A box plot displays the median, quartiles, and whiskers in a compact form. Placing boxes side by side supports comparison of distributions across groups.

The interquartile range equals the third quartile minus the first quartile. Because it focuses on the central half, it is relatively resistant to extreme values.

The range ignores all observations except the two endpoints. One unusual extreme value can change it substantially.

The mode is the value or category with the highest observed frequency. It is meaningful for both categorical and discrete numerical variables.

The median depends on the ordered middle position rather than the magnitude of every observation. Extreme values therefore have much less influence on it than on the mean.

The arithmetic mean is sensitive to extreme values because every observation contributes to the total. Highly skewed income distributions can therefore have means far above typical incomes.

These measures summarize how much data exists, where values are located, and how dispersed they are. Quantiles also help reveal asymmetry and possible extreme observations.

An initial row preview reveals how fields are arranged and how values are represented. It is a preliminary inspection, not a complete validation of all records.

In pandas, DataFrame shape is reported as `(number of rows, number of columns)`. The dataset therefore contains 1,200 observations and 15 variables.

Numerical, categorical, date, and text variables require different analyses and transformations. An incorrect type can produce invalid calculations or misleading results.