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There is no standard procedure for identifying outliers (it varies according to how thorough the statistical analysis has to be). Typically, you find 1.5*range of the data. Now use this number and add it to the Q1, lower quartile, and also minus it from the Q3, the upper quartile. Now, any data that does not fall between these two numbers is considered to be an "outlier".

Obvioulsy minusing 1.5*range from Q3 can leave you with a negative number, which if you're analyisng real data (such as people or time) will never end up negative. (i.e can't have -2 people, or -10 kg weight etc...). In this case you can assume the lower boundary to be zero.

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Q: What should you do when you identify outliers in any set of data?
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How do you Create a box plot of data that does not have any outliers?

Go into your data to determine which values are outliers and if they're significant and random (not an apparent group), eliminate them. This will take them out of your boxplot.


What is the primary disadvantage of using the range to compare the variability of data sets?

The range is very sensitive to outliers. Indeed if there are outliers then the range will be unrelated to any other elements of the sample.


Which is most resistant measures of central tendency?

The midhinge.this because it eliminates 25 percent of the largest data values and the smallest data values.this means any outliers present in the set of data values will be unable to throw the data


What you deciede to do when drawing conclusions about data?

Mostly through statistics, or summaries of the data set (depending on the type of data). There are many different statistical methods used to analyze the many different types of data that come from research studies or experiments. However if you just want a relatively quick and simplistic overview of a set of data than you should follow SOCS: Shape, Outliers, Center, Spread. Shape (the shape of the graphed data points) Outliers (any data points that fall outside the realm of "normal") Center (where the data points are mostly centered around) and Spread (the range of the data points). This should give you some immediate conclusions from your data.


Do you agree that data gathered through research may be misleading in decision making for any problem?

If you follow the steps of the scientific method, you should be able to identify non- misleading data.


What does the whisker in a box-and-whisker plot represent?

The whiskers mark the ends of the range of figures - they are the furthest outliers. * * * * * No. Outliers are not part of a box and whiskers plot. The whiskers mark the ends of the minimum and maximum observations EXCLUDING outliers. Outliers, if any, are marked with an X.


What is the outlier in 64 67 68 69 73 75 79?

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.


What is the relation between quartile deviation and standard deviation?

Strictly speaking, none. A quartile deviation is a quick and easy method to get a measure of the spread which takes account of only some of the data. The standard deviation is a detailed measure which uses all the data. Also, because the standard deviation uses all the observations it can be unduly influenced by any outliers in the data. On the other hand, because the quartile deviation ignores the smallest 25% and the largest 25% of of the observations, there are no outliers.


What are the advantages and disadvantages of range?

Range Advantage - Shows the spread of the results Disadvantage - Does not take into account any 'clustering' of results in a set of data. - It is affected strongly by outliers (very high or very low results).


How do you work out outliers on a box plot?

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) ^


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No. I need to ID one also without any data plate?


What is an outlier in math?

Outliers are basically numbers, in a set of numbers, that don't belong in that set and/or that stand out. For example, in the data set {3, 5, 4, 4, 6, 2, 25, 5, 6, 2} the value of 25 is an outlier. For a set of numerical data (a set of numbers), any value (number) that is markedly smaller or larger than other values is an outlier. This is the qualitative definition. Mathematically, a quantitative definition often given is that an outliers is any number that is more than 1.5 times the interquartile range away from the median. However, this is not definitive and in some cases other definitions will be used.