The data set represents the number of movies that a sample of 20 people watched in a year.
121 148 94 142 170 88 221 106 18 67
149 28 60 101 134 168 92 154 53 66

c. Display the data using a relative frequency histogram.

검증된 단계별 안내

Organize the data into intervals (also called bins). To do this, determine the range of the data by subtracting the smallest value (18) from the largest value (221). Then, decide on the number of intervals (commonly 5-10) and calculate the bin width by dividing the range by the number of intervals. Round up to a convenient number if necessary.

Create a frequency table. Count how many data points fall into each interval (bin). This will give you the frequency for each bin.

Calculate the relative frequency for each bin. Divide the frequency of each bin by the total number of data points (20 in this case). The formula is:

\frac{Frequency}{Total}

Construct the relative frequency histogram. On the x-axis, label the intervals (bins). On the y-axis, label the relative frequencies. Draw bars for each bin where the height of the bar corresponds to the relative frequency of that bin.

Ensure the histogram is properly labeled. Add a title to the histogram, label the axes, and ensure the bars are evenly spaced and accurately represent the relative frequencies.

비슷한 문제에 대한 검증된 영상 답변:

이 영상 해법은 위 문제에 도움이 된다고 튜터들이 추천한 것입니다.

영상 길이:

10m

주요 개념

질문에 올바르게 답하기 위해 반드시 이해해야 하는 핵심 개념들은 다음과 같습니다.

Relative Frequency

Relative frequency is the ratio of the number of times a particular value occurs in a data set to the total number of observations. It provides a way to understand the proportion of each category relative to the whole, which is essential for creating a histogram that accurately represents the data distribution.

Histogram

A histogram is a graphical representation of the distribution of numerical data, where the data is divided into intervals (bins) and the frequency of data points within each bin is represented by the height of bars. It visually summarizes the data, making it easier to identify patterns, trends, and outliers.

Binning Data

Binning data involves grouping continuous data into discrete intervals or 'bins' to facilitate analysis and visualization. The choice of bin width can significantly affect the appearance of the histogram and the interpretation of the data, making it crucial to select appropriate bins to accurately reflect the underlying distribution.

The data set represents the number of movies that a sample of 20 people watched in a year.121 148 94 142 170 88 221 106 18 67149 28 60 101 134 168 92 154 53 66c. Display the data using a relative frequency histogram.