Health The cholesterol levels of 80 adults were recorded and...

Health The cholesterol levels of 80 adults were recorded and then displayed in the box-and-whiskers plot shown at the right. a. How many adults had a cholesterol level above $217 ?$ b. How many adults had a cholesterol level below $254 ?$ c. How many cholesterol levels are represented in each quartile? d. What percent of the adults had a cholesterol level of 198 or less?

Health The cholesterol levels of 80 adults were recorded and then displayed in the box-and-whiskers plot shown at the right.
a. How many adults had a cholesterol level above $217 ?$
b. How many adults had a cholesterol level below $254 ?$
c. How many cholesterol levels are represented in each quartile?
d. What percent of the adults had a cholesterol level of 198 or less?

Key Concepts

Box-and-Whiskers Plot

A box-and-whiskers plot is a graphical representation of a dataset's summary statistics, including the minimum, first quartile (Q1), median, third quartile (Q3), and maximum. It is used in exploratory data analysis to visually depict the distribution, central tendency, and spread of the data, as well as to identify potential outliers.

Quartiles

Quartiles divide an ordered dataset into four equal parts, each containing 25% of the data points. The first quartile (Q1) marks the 25th percentile, the median marks the 50th percentile, and the third quartile (Q3) marks the 75th percentile. Understanding quartiles is essential for determining how many data points lie above, below, or within particular segments of the data.

Percentages in Data

Calculating percentages in a dataset involves determining what proportion of the data meets a specific criterion relative to the whole dataset. This concept is used to analyze and interpret the distribution of data, such as identifying the percent of individuals with values below or above a given threshold.

Data Distribution Interpretation

Interpreting the distribution of data based on summary measures like the five-number summary helps in estimating counts or percentages for different segments of the dataset, such as estimating the number of data points that exceed a specific value or fall within a determined range. This interpretation is key in making inferences about the underlying data.

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