The heights, in m, of 40 students are shown below: 1.68 1.67 1.53 1.70 1.69

step 1 Okay, in this question we are given a data where we are given height of certain students and we

The heights, in m, of 40 students are shown below: 1.68...

The heights, in $\mathrm{m}$, of 40 students are shown below: $\begin{array}{llllllll}1.68 & 1.67 & 1.53 & 1.70 & 1.69 & 1.71 & 1.71 & 1.80 \\ 1.81 & 1.85 & 1.76 & 1.66 & 1.91 & 1.95 & 1.87 & 1.80 \\ 1.82 & 1.76 & 1.84 & 1.55 & 1.61 & 1.85 & 1.93 & 1.88 \\ 1.95 & 1.57 & 1.97 & 1.56 & 1.99 & 1.83 & 1.74 & 1.83 \\ 1.86 & 1.88 & 1.93 & 1.64 & 1.89 & 1.90 & 1.72 & 1.95\end{array}$ Construct a frequency distribution with an equal class width of $0.1$.

The heights, in $\mathrm{m}$, of 40 students are shown below:
$\begin{array}{llllllll}1.68 & 1.67 & 1.53 & 1.70 & 1.69 & 1.71 & 1.71 & 1.80 \\ 1.81 & 1.85 & 1.76 & 1.66 & 1.91 & 1.95 & 1.87 & 1.80 \\ 1.82 & 1.76 & 1.84 & 1.55 & 1.61 & 1.85 & 1.93 & 1.88 \\ 1.95 & 1.57 & 1.97 & 1.56 & 1.99 & 1.83 & 1.74 & 1.83 \\ 1.86 & 1.88 & 1.93 & 1.64 & 1.89 & 1.90 & 1.72 & 1.95\end{array}$
Construct a frequency distribution with an equal class width of $0.1$.

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Key Concepts

Frequency Distribution

A frequency distribution is a tabular or graphical representation that displays the number of observations within different intervals or classes. It helps in summarizing the data by showing how frequently each range of values occurs, thus providing an overview of the distribution of the data points.

Equal Class Width

An equal class width means that each interval or class in a frequency distribution covers the same range of values. This standardization simplifies the analysis and comparison of data, making it easier to identify patterns and trends in the dataset.

Data Grouping (Binning)

Data grouping, or binning, involves dividing continuous numerical data into distinct intervals or classes. This technique transforms raw data into a more manageable form, enabling clearer insights into the distribution and variability of the dataset.

Class Limits and Boundaries

Class limits refer to the smallest and largest values that can belong to a given class, while class boundaries are the actual cutoff points that separate classes without gaps or overlaps. Establishing clear limits and boundaries ensures that each data point is accounted for in only one class and improves the accuracy of the frequency distribution.

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