How do R Histograms help in detecting outliers in a dataset?

a) By showing bars that represent unusually high or low frequencies in specific bins

b) By displaying the relationship between two variables

c) By comparing categorical data frequencies

d) By illustrating trends over time

Answer:

a) By showing bars that represent unusually high or low frequencies in specific bins

Explanation:

R histograms help in detecting outliers by showing bars that represent unusually high or low frequencies in specific bins. Outliers are data points that fall far outside the normal range of values in a dataset. In a histogram, outliers can appear as isolated bars that are significantly distant from the other bars, indicating that these data points are not consistent with the majority of the data.

# Example of a histogram detecting outliers
data <- c(rnorm(100), 50, -30)  # 100 normal values with two outliers
hist(data, col = "lightcoral", main = "Histogram with Outliers", xlab = "Value", ylab = "Frequency")

In this example, the histogram includes two outliers (50 and -30) that stand out as isolated bars on the far right and left of the distribution. These outliers can be easily identified in the histogram, helping analysts to understand the data’s range and variability. Detecting outliers is essential for accurate data analysis, as they can significantly affect statistical calculations and models.