Old folks Here is a stemplot of the percents of residents...

Old folks Here is a stemplot of the percents of residents aged 65 and older in the 50 states: (a) Find and interpret the percentile for Colorado, where $10.1 \%$ of the residents are aged 65 and older. (b) Find and interpret the percentile for Rhode Island, where $13.9 \%$ of the residents are aged 65 and older. (c) Which of these two states is more unusual? Explain.

Old folks Here is a stemplot of the percents of residents aged 65 and older in the 50 states:
(a) Find and interpret the percentile for Colorado, where $10.1 \%$ of the residents are aged 65 and older.
(b) Find and interpret the percentile for Rhode Island, where $13.9 \%$ of the residents are aged 65 and older.
(c) Which of these two states is more unusual? Explain.

Key Concepts

Percentile

A percentile indicates the value below which a certain percentage of observations in a dataset falls. It is a way of understanding the relative standing of a data point within a distribution. For instance, if a value is at the 60th percentile, it means that 60% of the data values are less than or equal to that value. This concept is key in statistical analysis because it provides insight into the distribution and ranking of data points.

Stem-and-Leaf Plot

A stem-and-leaf plot is a graphical tool used to display quantitative data in a way that shows the distribution and frequency of data points. The 'stem' represents a category or range of values, and the 'leaf' represents individual data points. This plot is useful for quickly identifying the shape, spread, and center of the dataset, as well as detecting any potential outliers or unusual observations.

Unusual Observations

Unusual observations, often referred to as outliers, are data points that differ significantly from the majority of the data in a dataset. In the context of percentiles or stemplots, identifying which values are far from most of the data can help determine whether a particular observation is rare or atypical. This concept is important in statistics because it can influence the interpretation of the data and may indicate special cases or errors in data collection.

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