The table below shows the time taken, in ms, for 105 op-amps...

The table below shows the time taken, in ms, for 105 op-amps to become fully operational: $$ \begin{array}{|c|c|} \hline \text { Time taken } t(\mathrm{~ms}) & \text { Frequency } \\ \hline 10 \leq t<20 & 10 \\ 20 \leq t<30 & 14 \\ 30 \leq t<40 & 18 \\ 40 \leq t<45 & 16 \\ 45 \leq t<50 & 12 \\ 50 \leq t<55 & 8 \\ 55 \leq t<70 & 15 \\ 70 \leq t<100 & 12 \\ \hline \end{array} $$ Draw a histogram to illustrate this data.

The table below shows the time taken, in ms, for 105 op-amps to become fully operational:
$$
\begin{array}{|c|c|}
\hline \text { Time taken } t(\mathrm{~ms}) & \text { Frequency } \\
\hline 10 \leq t<20 & 10 \\
20 \leq t<30 & 14 \\
30 \leq t<40 & 18 \\
40 \leq t<45 & 16 \\
45 \leq t<50 & 12 \\
50 \leq t<55 & 8 \\
55 \leq t<70 & 15 \\
70 \leq t<100 & 12 \\
\hline
\end{array}
$$
Draw a histogram to illustrate this data.

Key Concepts

Histogram

A histogram is a graphical representation of the distribution of numerical data. It does this by grouping data points into bins or intervals and displaying the frequency (or count) of data points in each bin. Histograms are useful for visualizing the shape, spread, and central tendency of the data, and for identifying patterns such as skewness or the presence of any outliers.

Frequency Distribution

A frequency distribution is a summary of how often different values or ranges of values occur in a dataset. It provides the counts (or frequencies) of observations within each specified class interval and is fundamental to constructing histograms. This distribution helps in understanding how data is spread over various categories or intervals.

Class Intervals

Class intervals, also known as bins, are ranges used to group numerical data into a frequency distribution. Each interval covers a segment of the data, and the frequency of data points falling within each interval is used to plot the height of the bars in a histogram. Proper selection of class intervals is important to ensure that the histogram accurately reflects the underlying distribution.

Data Visualization

Data visualization involves representing information graphically to make data easier to understand and interpret. By using visual tools such as histograms, one can quickly identify trends, patterns, and anomalies in data. This process is an essential aspect of statistical analysis and helps to communicate complex data findings effectively.

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