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How do you use a box and whisker plot?
Box and whisker plots are used to give a visual indication of where quartiles and highest/lowest values fall, so they're useful for visually comparing various sets of data. The "whisker" on the left extends to the lowest value in the data range (the left-most point). The first edge of the "box" indicates the lower quartile, the middle line in the box represents the median quartile, and the upper edge of the box represents the 3rd quartile. The "whisker" on the right extends to the highest value in the data set. Clearly when using many box and whisker plots, and comparing them to each other, it helps greatly if you use the same scale on each plot. Sometimes it may be decided that your lowest/highest data values are "outliers" (anomalous results), in which case they are still included in the box and whisker plot, but they should be demarcated by a hollow circle wherever the outlier is deemed to be.
How do you create a parallel box-and- whisker plots?
Parallel box and whisker plots are regular box and whisker plots, but drawn "one-above-the other" on the piece of paper. To enable to do this easily, draw an x-axis which is big enough for the largest value in the data, and small enough for the smallest value in the data (in the entire collection of data). Plot each box-and-whisker diagram below each other.
Which descriptive summary measures cannot be easily approximated from a box-and-whisker plot?
A box-and-whisker plot provides a visual summary of the median, quartiles, and potential outliers in a dataset, but it does not easily convey measures such as the mean or standard deviation. Additionally, it does not provide information on the distribution shape, skewness, or kurtosis, which are essential for understanding the overall distribution of the data. These summary measures require additional calculations or data representations for accurate approximation.
When should relative frequencies be used when comparing two data sets?
When comparing large data sets.
When would you choose a bar graph to present data?
when i am comparing data
How do you use a box and whisker plot?
Box and whisker plots are used to give a visual indication of where quartiles and highest/lowest values fall, so they're useful for visually comparing various sets of data. The "whisker" on the left extends to the lowest value in the data range (the left-most point). The first edge of the "box" indicates the lower quartile, the middle line in the box represents the median quartile, and the upper edge of the box represents the 3rd quartile. The "whisker" on the right extends to the highest value in the data set. Clearly when using many box and whisker plots, and comparing them to each other, it helps greatly if you use the same scale on each plot. Sometimes it may be decided that your lowest/highest data values are "outliers" (anomalous results), in which case they are still included in the box and whisker plot, but they should be demarcated by a hollow circle wherever the outlier is deemed to be.
A box and whisker plot has 4?
A box and whisker plot has four quartiles in which its data is spread across.
You want to construct a box-and-whisker plot from a data set What is the first thing you should do?
Box-and-whisker plots highlight central values in a set of data. In order to construct a box-and-whisker plot, the first step is to order your data numerically and find the median value.
What conclusions can you draw from comparing two sets of related data displayed in box and whisker plots?
You can determine differences in:the median, a measure of central tendency;the inter quartile range (IQR). This is a measure of the spread of data around the median;skewness;number of outliers.
How do you create a parallel box-and- whisker plots?
Parallel box and whisker plots are regular box and whisker plots, but drawn "one-above-the other" on the piece of paper. To enable to do this easily, draw an x-axis which is big enough for the largest value in the data, and small enough for the smallest value in the data (in the entire collection of data). Plot each box-and-whisker diagram below each other.
How do you find the minimum value of a box and whisker plot?
It i the smallest value in the data set and corresponds to the value of the left-most end of the whisker. Unless there were outliers, in which case it will be an "X" to the left of the left-whisker.
What are advantages of box and whisker plots?
Box and whisker plots effectively summarize a dataset by displaying its central tendency, variability, and distribution shape through quartiles. They visually highlight outliers and provide a clear comparison between multiple groups. Additionally, these plots are useful for identifying the spread of data and understanding skewness, making them an excellent tool for exploratory data analysis. Overall, they condense complex data into a simple visual format that facilitates quick insights.
What word is used for data display that helps you to see the way data are distributed?
box- and - whisker plot
Which word best describes what you can observe about the data displayed by a box and whisker plot?
the data most likely
Is a logical conclusion from comparing the images?
Yes, comparing images can lead to logical conclusions about similarities, differences, patterns, or relationships between them. It can help in making informed decisions, identifying trends, or drawing insights based on visual data.
What is box-and-whisker used for?
A box-and-whisker plot, or box plot, is used to visually summarize the distribution of a dataset by displaying its minimum, first quartile, median, third quartile, and maximum values. It helps identify central tendencies, variability, and potential outliers within the data. This graphical representation is particularly useful for comparing distributions across different groups or categories. Overall, it provides a clear overview of the data's spread and skewness.
Which descriptive summary measures cannot be easily approximated from a box-and-whisker plot?
A box-and-whisker plot provides a visual summary of the median, quartiles, and potential outliers in a dataset, but it does not easily convey measures such as the mean or standard deviation. Additionally, it does not provide information on the distribution shape, skewness, or kurtosis, which are essential for understanding the overall distribution of the data. These summary measures require additional calculations or data representations for accurate approximation.
