Combine the datas and find outliers or not

  • Thread starter sinyoungoh
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In summary, the purpose of combining data and finding outliers is to identify any unusual or unexpected values in a dataset, as outliers can affect the overall analysis and interpretation of the data. There are various methods for combining data from different sources, such as merging, joining, or appending the data. To detect outliers, statistical methods such as calculating z-scores, using box plots or scatter plots, or performing clustering or regression analysis can be used. An outlier is considered influential if it significantly affects the results of the analysis, which can be determined by examining the effect of removing the outlier on the overall results. Potential causes of outliers in a dataset include data entry errors, measurement errors, or natural variations in the data, and it is important to thoroughly examine
  • #1
sinyoungoh
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I compared the stock returns between Australia and US over the period of 1991 and 1998
I found the summary measures(eg/min,max,Q1,Q2,Q3,coneficient of variation, stdv ect...)
I found the ourtlier for australian data and Us data seperately. There were few outliers for Australian but one outlier for US.

The question was If someone say"why don't we combine the two sets and then determine the outlier" and Ohter says" I'am not sure, I think it is not a good idea to combine"
Briefly write how you I response for these comments...

I have no idea whether i should combine the datas or not and i need a reason as well

Thank you for helping me =)
 
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  • #2
It is not advisable to combine the two data sets in order to determine the outliers. This is because combining the two sets would mask any differences between the two, making it difficult to identify any meaningful outliers. Additionally, combining the two sets could potentially lead to incorrect conclusions or inaccurate results, as the combined set may not be representative of either set alone. Therefore, it is best to analyze the two sets separately in order to identify any meaningful outliers.
 

What is the purpose of combining data and finding outliers?

The purpose of combining data and finding outliers is to identify any unusual or unexpected values in a dataset. Outliers can affect the overall analysis and interpretation of the data, so it is important to identify and handle them appropriately.

How do you combine data from different sources?

There are several ways to combine data from different sources, such as merging, joining, or appending the data. The specific method will depend on the type of data and the desired outcome of the analysis.

What methods can be used to detect outliers?

There are various statistical methods that can be used to detect outliers, such as calculating z-scores, using box plots or scatter plots, or performing clustering or regression analysis. The most appropriate method will depend on the data and the research question.

How do you determine if an outlier is influential?

An outlier is considered influential if it significantly affects the results of the analysis. This can be determined by examining the effect of removing the outlier on the overall results. If the removal of the outlier has a significant impact, it is considered influential.

What are some potential causes of outliers in a dataset?

There are several potential causes of outliers in a dataset, such as data entry errors, measurement errors, or natural variations in the data. It is important to thoroughly examine the data and understand the context in order to determine the cause of outliers.

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