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

2. To put your footer here go to View > Header and Footer 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 Excels 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. To put your footer here go to View > Header and Footer 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. To put your footer here go to View > Header and Footer 4 The essence of a stem and leaf plot Stem and leaf plot Stacked dot plot The leaf shows the next digit. This can be useful in the exploration phase data 5.3 5.4 6.0 ….. 11.1 11.9

5. To put your footer here go to View > Header and Footer 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. To put your footer here go to View > Header and Footer 6 Tanzania agriculture survey This is the variable we wish to explore. It is a value between 0 and 100

7. To put your footer here go to View > Header and Footer 7 The data in Excel The variable to explore before analysis

8. To put your footer here go to View > Header and Footer 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. To put your footer here go to View > Header and Footer 9 The pivot table

10. To put your footer here go to View > Header and Footer 10 Some results

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12. To put your footer here go to View > Header and Footer 12 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

13. To put your footer here go to View > Header and Footer 13 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

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15. To put your footer here go to View > Header and Footer 15 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

16. To put your footer here go to View > Header and Footer 16 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

17. To put your footer here go to View > Header and Footer 17 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

18. To put your footer here go to View > Header and Footer 18 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

19. To put your footer here go to View > Header and Footer 19 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

20. To put your footer here go to View > Header and Footer 20 Dot plots - yield by variety Outliers (typing errors) are clear, but only because of the 2 nd variable They are not outliers overall

21. To put your footer here go to View > Header and Footer 21 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!

22. To put your footer here go to View > Header and Footer 22 Swaziland data was for checking

23. To put your footer here go to View > Header and Footer 23 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!

24. To put your footer here go to View > Header and Footer 24 Codes for the column Seems clear enough. Zeros and blanks still a puzzle

25. To put your footer here go to View > Header and Footer 25 Selecting the blank records i.e. serious problems with the whole record Missing also Too young and all the same Crop code not recognised Areas too large

26. To put your footer here go to View > Header and Footer 26 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

27. To put your footer here go to View > Header and Footer 27 Checking data quality and EDA WhereWhyHowBy Whom Before data entry To ensure complete data set received Manual check supervisor During data entry To highlight anomalies Filter, dot plots etc Supervisor and helpers Before analysis Double checkAs above Analyst/ statistician During analysis Remain criticalResidualsAnalyst/ statistician

28. To put your footer here go to View > Header and Footer 28 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

29. To put your footer here go to View > Header and Footer 29 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

30. To put your footer here go to View > Header and Footer 30 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