Outliers are typically found in the first and fourth quartiles, outside the interquartile range (IQR). Specifically, any data point that falls below Q1 - 1.5 × IQR or above Q3 + 1.5 × IQR is considered an outlier. Therefore, outliers can exist in both the lower and upper extremes of the data distribution.
It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.
If the result is 1.5 x Inter Quartile Range (or more) above the Upper Quartile or 1.5 x Inter Quartile Range (or more) below the Lower Quartile.
the number in your piece of data = n lower quartile, n+1 divided by 4 upper quartile, n+1 divded by 4 and times by three interquartile range(IQR) = upper quartile - lower quartile outliers(O) = interquartile range x 1.5 lower than IQR-O is an outlier (h) above IQR+O is an outlier (h) the outliers on your box plot are any numbers that are the value i have named (h) ^
Both ends of the the box (the "whiskers") plot determine the range of your data, without including outliers. (Outliers are marked by an asterisk). The end of the left side of the box is the lower quartile. The line in the box is the median. The other end of the box represent the upper quartile.
These are sometimes considered outliers but there is no formal definition for them.
It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.
If the result is 1.5 x Inter Quartile Range (or more) above the Upper Quartile or 1.5 x Inter Quartile Range (or more) below the Lower Quartile.
the number in your piece of data = n lower quartile, n+1 divided by 4 upper quartile, n+1 divded by 4 and times by three interquartile range(IQR) = upper quartile - lower quartile outliers(O) = interquartile range x 1.5 lower than IQR-O is an outlier (h) above IQR+O is an outlier (h) the outliers on your box plot are any numbers that are the value i have named (h) ^
The whiskers go from the minimum to the maximum though outliers may be excluded. The box, itself, goes from the lower quartile to the upper quartile.
Both ends of the the box (the "whiskers") plot determine the range of your data, without including outliers. (Outliers are marked by an asterisk). The end of the left side of the box is the lower quartile. The line in the box is the median. The other end of the box represent the upper quartile.
When you are looking for a simple measure of the spread of the data, but one which is protected from the effects of extreme values (outliers).
They are 1: the minimum 2: the lower quartile 3: the median 4: the upper quartile 5: the maximum. Sometimes the extrema (minimum and maximum) are plotted AFTER excluding outliers.
By definition a quarter of the observations are below the lower quartile and a quarter are above the upper quartile. In all, therefore, half the observations lie outside the interquartile range. Many of these will be more than the inter-quartile range (IQR) away from the median (or mean) and they cannot all be outliers. So you take a larger multiple (1.5 times) of the interquartile range as the boudary for outliers.
These are sometimes considered outliers but there is no formal definition for them.
There is no agreed definition of outliers. However two common criteria to identify outliers are: Method I: If Q1 is the lower quartile and Q3 the upper quartile then any number smaller than Q1 - 1.5*(Q3 - Q1) or larger than Q3 + 1.5*(Q3 - Q1) is an outlier. By that criterion there is no outlier. Method II: Assume the numbers are normally distributed. then outliers are with absolute z-scores greater than 1.96. Again, there are no outliers.
Ohms
The difference between the third quartile (Q3) and the first quartile (Q1) in a five-number summary is called the interquartile range (IQR). It represents the range of the middle 50% of the data, providing a measure of statistical dispersion. The IQR is useful for identifying outliers and understanding the spread of the dataset.