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Exploratory Data Analysis (EDA) in the data analysis process

Exploratory Data Analysis (EDA) in the data analysis process. Module B2 Session 13. Learning Objectives. students should be able to Construct a dot plot for a numeric variable split by a categorical variable Apply EDA concepts to a large dataset Explain the use of Excel’s pivot tables

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Exploratory Data Analysis (EDA) in the data analysis process

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  1. Exploratory Data Analysis (EDA) in the data analysis process Module B2 Session 13

  2. Learning Objectives students should be able to • Construct a dot plot for a numeric variable • split by a categorical variable • Apply EDA concepts to a large dataset • Explain the use of Excel’s pivot tables • and filters, in the EDA process • Explain the importance of EDA • for data checking and at the start of the analysis • Relate EDA • to the principles of official statistics ….

  3. EDA with small and large data sets • Session 12: • Stressed the importance of EDA • Introduced 2 new tools (dot and stem) • Practiced with small data sets • In this session we scale up • Look at large data sets • The tools do not scale up easily • But the concepts do scale up • EDA becomes even more crucial • Most data sets are large! • at least compared with teaching examples

  4. The essence of a stem and leaf plot The “leaf” shows the next digit. This can be useful in the exploration phase data5.35.46.0…..11.111.9 Stem and leaf plot Stacked dot plot

  5. What are the key points? • We look at individual data points • not summaries at this stage • this is general for EDA • The stem and leaf plot in particular • keeps the actual numbers as far as possible • This can be important • An example uses the Tanzania survey

  6. Tanzania agriculture survey This is the variable we wish to explore. It is a value between 0 and 100

  7. The data in Excel The variable to explore before analysis

  8. How to explore this value • Can we do a stem and leaf plot? • “By hand” in Excel – but there are 16628 values! • Even if automated, that is too many! • The essence of a stem and leaf plot • is to look at all the possible values • Try a pivot table • a powerful feature in Excel • used previously on categorical data

  9. The pivot table

  10. Some results

  11. What do you deduce? • There are oddities in “rounding” • Perhaps enumerator differences • Can this question be answered to 1%? • So what should be done before analysis? • First – look further at the data • Excel can help – it can “drill down” to examine individual records • The concept: • Use the table to look for oddities • Then examine them in more detail

  12. Drilling down – an example Make the 6 corresponding to 2% the active cell Then double click to give the detail 4 of these values are from the same village – so same enumerator

  13. What do you conclude – technique/results • Technique • Stem and leaf plots when looking at small datasets • Pivot tables when datasets are large • But the principle is general • Numbers must be looked at carefully! • The principle can be adapted for the data • and explored effectively in Excel • Results • Did enumerators have different interpretations • of the “precision” required in the percentages • This needs further exploration • and the analysis needs to take account of this

  14. Another new element in this session • Exploratory analysis includes • looking for oddities in the data • Unexplained oddities cause variation • that can make it difficult to detect the pattern • because they add unnecessary noise to the data • How do you “tame the variation” • One way is to examine related variables • This is important in the analysis • the next slide is a repeat from Session 3 • It is also a key weapon in data exploration • and is covered in the practical

  15. Slide from Module B2 Session 3 • To do good statistics you must • fight the curse of variation • Two main strategies to overcome variation • 1. Take enough observations • In the Tanzania survey there were 3223 households just from this one region • 2. Measure characteristics that explain variation • Variation itself is not necessarily the problem • Variation you do not understand is the problem • Here we start understanding variation • at the exploration stage

  16. Practical – three parts • Tanzania data • practice what has been done in these slides • Dot plots – split by a factor • demonstration and practice • Swaziland data • apply the concepts • checking factors • as well as numeric columns • Then the key points are reviewed

  17. Points for review after the practical • Looking for individual problems • And surprising patterns • Exploratory graphics • need to help the analyst and data checker • see dot plots on next slide • Tables are also useful • especially with the facility to drill down • Look at individual variables • and at records as a whole • Trust your common sense • It is useful to estimate results • And question the computer if they are very different

  18. Dot plots - yield by variety Outliers (typing errors) are clear, but only because of the 2nd variable They are not outliers overall

  19. EDA is a continuous process • EDA effectively is a continuation of the data checking process • The example on the previous slide shows • how some oddities only become clear once the analysis is undertaken • This continues into the formal analysis • where it involves looking at the “residuals” • They are the unexplained variation • As discussed in Session 3! • So analysis is not just a set of rules • It is a thoughtful process • Where you become the data detective!

  20. Swaziland data was for checking

  21. Investigating the column called Presence What does 0 mean? Why are there blanks? Next steps: 1. Look at the questionnaire 2. Select these records You are becoming detectives!

  22. Codes for the column Seems clear enough. Zeros and blanks still a puzzle

  23. Selecting the blank records Missing also Too young and all the same Crop code not recognised Areas too large i.e. serious problems with the whole record

  24. Dot plot of area by Presence Odd crop areas were ALL associated with odd codes for the column PRESENCE It was found to be a data transfer problem with one byte missing in these records

  25. Checking data quality and EDA

  26. Importance – principles of official statistics • Principle 2: Professional standards • It is unprofessional to analyse the data and report results without exploring critically at all stages • Principle 4: Prevention of misuse • We risk misusing the data unless we explore the data critically • Principle 5: Sources of statistics • Includes a requirement to avoid undue burden on respondents • We must process the data fully and effectively. This needs EDA • Otherwise the burden imposed on respondents is to some extent wasted

  27. Can you now: • Apply EDA concepts to a large dataset • Explain the importance of EDA for data checking and at the start of the analysis • Relate EDA to the principles of official statistics

  28. Now you can organise the data for analysis And then do an exploratory analysis We show next how the analysis is easy IF your objectives are clear

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