Why We Should Be Diligent About Checking For Our Outliers
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An outlier is an observation that lies an abnormal distance from other values in random sample from a population. Outliers are data values that are differ greatly from a majority of a set of data. The value that falls outside of an overall trend that a present in the data. Sometimes outliers can be caused by an error and other times outliers indicate the presence of a previously unknown phenomenon. Other reasons that we should feel the need to be diligent about checking for our outliers is because of the descriptive statistics that can be very sensitive to outliers. This is like standard deviation and correlation coefficient for a paired data, for just a few of the types of statistics. While an outlier is a value that differs significantly from the rest of our data, you can careful examination of a set of data to look for outliers causes some difficulty. Although it is easy to see, you can possibly use a stem plot, that some values differ from the rest of data, they are different values that have to be to be an outlier.
Today, the discussion of what constitutes an outlier has been very understanding. You will be giving a specific measurement that will give you an objective standard of what constitutes an outlier. Determining outliers is very simple, you multiplying the interquartile range which is short for (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier. For an example, if you subtract 1.5 x IQR from the first quartile, any data values
For me this question shows the real reason for faillure. Most projects focus on producing a rocket (only focus on delivery). Successful projects however look further: why do we create a rocket? Because we want to go to the moon. If NASA would have only focussed on delivering rockets, they would never have reached the moon and probably had produced a great number of disasters while delivering or using those rockets.
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