Analyzing Daytona 500 Winning Speeds Using a Histogram

Use the given histogram of winning speeds at the Daytona 500 to answer the following questions:

a) Which interval contains the most data values?

b) How many winning speeds are less than 140 miles per hour?

c) How many winning speeds are at least 160 miles per hour?

A histogram is a powerful tool in descriptive statistics that helps us understand the distribution of a dataset by displaying the frequency of data within different intervals, also known as bins. When approaching problems involving histograms, it is crucial to comprehend how to interpret the height of the bars which represent the frequency of data points within each interval.

In the context of the Daytona 500 winning speeds, we can analyze which speed interval contains the most data by noticing the tallest bar on the histogram. This type of analysis assists in understanding the central tendency and variability in the data. Frequency distribution, as displayed in a histogram, provides visual insights into how data is spread across different ranges without having to inspect each data point individually.

Questions involving cumulative frequency require a cumulative approach, summing frequencies up to a certain limit. For instance, calculating how many winning speeds are less than a specific value involves adding up all the frequencies of intervals below that value. In contrast, determining the number of speeds that are at least a certain value requires summing all the frequencies starting from that interval upwards to capture all higher or equal values. These exercises enhance skills in analyzing data distributions and interpreting statistical charts, foundational in studying descriptive statistics, data analysis, and beyond.