Question

Outhers are outlying data values, that is, values that are extremely high or low compared to the rest of the date. The simple box-and whisker plot in your textbook does not show outliers but can be modified to do so. The box-and-whisker plot in the tool above illustrates this. Use the Bax Plots feature in the toof to obtain a box-and-whisker plot of the one-day returns. The asterisks on the plot indicate outliers. The boxaand whisker plot shows observations identified as outliers. Lowrvalued outliers correspond to extreme outliers correspond to extreme You can define a maximum lower whisker of the first quartile (Q1) - 1.5 ×10R and a maximum upper whisker of the third quartile (Q3) + 1.5 ×1 IQe for all non-outlying values, where IQR is the interquartile range. Valves below the maximum lower whisker or above the maximum upper whisker are censidered outien. In the abeve box-and-nhisker plot, the outliers are either below the maximum lower whisker of. of Note: Aound Q1, Q3, and IQR to two decimal places before computing Q1−1.5(1QR) and Q3+1:5(IQR). Also round the value of 1.5(1QR) to two decimal places in the intermedate step. Roll your cursor over the endpoints of the whiskers of the box-and-whisker plot to ootain their values, The left whisker extends to right whisker extends to

Answer 1

A box plot visually represents a dataset's distribution and is constructed from the minimum value, first quartile, median, third quartile, and maximum value. Outliers are determined using the interquartile range and rounding calculations to two decimal places.

Step-by-step explanation:

To construct a box plot, which is used to graphically represent the distribution of a data set, you first need to identify the minimum value, first quartile (Q1), median, third quartile (Q3), and maximum value. These elements form the basis of the box plot. The first and third quartiles act as markers for the bounds of the box. The median is indicated within the box, and the whiskers extend from each end of the box to the smallest and largest data values, respectively.

Outliers are data points that lie significantly outside the range of the majority of the data. To determine potential outliers, you calculate the interquartile range (IQR), which is Q3 minus Q1. A data value is classified as an outlier if it is below Q1 minus 1.5 times the IQR or above Q3 plus 1.5 times the IQR. It is important to round the values of Q1, Q3, and the IQR to two decimal places when performing these calculations.

In summary, a box plot gives a visualization of the central 50% of data points and helps identify outliers, enhancing our ability to analyze and interpret datasets.