What does a five-number summary tell you about a data set?
What does a five-number summary tell you about a data set?
A five-number summary is especially useful in descriptive analyses or during the preliminary investigation of a large data set. A summary consists of five values: the most extreme values in the data set (the maximum and minimum values), the lower and upper quartiles, and the median.
How do you tell if a five-number summary is skewed?
To make this determination, compare the median to Q1 and Q3. When the median is: Approximately halfway between Q1 and Q3, your data are symmetrical. Closer to Q1, your data are right-skewed.
Do you include outlier in 5 number summary?
The five numbers are the minimum, the first quartile(Q1) value, the median, the third quartile(Q3) value, and the maximum. The first thing you might notice about this data set is the number 27. This is very different from the rest of the data. It is an outlier and must be removed.
How do you determine an outlier?
Multiplying the interquartile range (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier. If we subtract 1.5 x IQR from the first quartile, any data values that are less than this number are considered outliers.
Which is not part of the 5 number summary?
Terms in this set (5) Heights of all elementary school children. Which of he following is not part of a five-number summary? The mean.
How do you tell if data is skewed left or right?
For skewed distributions, it is quite common to have one tail of the distribution considerably longer or drawn out relative to the other tail. A “skewed right” distribution is one in which the tail is on the right side. A “skewed left” distribution is one in which the tail is on the left side.
When can you ignore an outlier?
If the outlier in question is: A measurement error or data entry error, correct the error if possible. If you can’t fix it, remove that observation because you know it’s incorrect. Not a part of the population you are studying (i.e., unusual properties or conditions), you can legitimately remove the outlier.
What is an outlier in a data set?
An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal.