1. SADC Course in Statistics Graphical summaries for quantitative data Module I3: Sessions 2 and 3

2. To put your footer here go to View > Header and Footer 2 Learning Objectives Students should be able to: Present data in a histogram –and interpret data when shown a histogram Present data in a boxplot –and interpret data in a boxplot Recognise advantages and limitations –of each method of presentation Explain what is gained and lost from data summary Interpret graphical summaries to answer questions –concerning proportions, –extremes, –medians –and quartiles Resolve simple problems with graphical displays –when real data (not text-book examples) are used

3. To put your footer here go to View > Header and Footer 3 Session Overview Activity 1: Presentation to introduce the sessions Activity 2: Demonstration on histograms in Excel Activity 3: Practical covering the same ideas –CAST Chapter 2.1 –Histograms –Population pyramids Activity 4: Practical on boxplots and percentage points –CAST Chapter 2.2 –Boxplots in Excel Activity 5: Presentation continued

4. To put your footer here go to View > Header and Footer 4 Activity 1- Case studies Case studies used in these sessions are: –Rice Survey –Zambia Rainfall Data –The Swaziland Crop Cutting Survey The were introduced in Modules B1 and B2 –so for many this will be a reminder. The rice survey and the Swaziland Crop Cutting –were already used in Session 1 of this Module.

5. To put your footer here go to View > Header and Footer 5 Rice survey Used repeatedly in slightly different forms In CAST as shown here In demonstration And in Excel Qualitative and quantitative variables to analyse

6. To put your footer here go to View > Header and Footer 6 Zambia Rainfall data Farmers are migrating from Southern Zambia –citing climate change as the reason –they can no longer grow the crops as before A local NGO –acknowledges climate change in general, –but believes improved farming practices is more important –wherever farmers locate They question the evidence –for climate change in the pattern of rainfall –as it affects farming strategy

7. To put your footer here go to View > Header and Footer 7 Data analysed in these sessions Total rainfall from 1 Jan to 31 March – called SeasonTot Number of rain days in the same period - Scount

8. To put your footer here go to View > Header and Footer 8 Original daily values are also available Not needed for analysis here, but used for checking Also useful if further questions posed

9. To put your footer here go to View > Header and Footer 9 Zambia rainfall continued: Here we use the annual data –on the final worksheet –preparing data for analysis is done in Module I2 We have access to the raw data –for checking purposes –and in case other variables are needed on a later occasion

10. To put your footer here go to View > Header and Footer 10 Swaziland Crop Cutting Survey Annual survey –Of agricultural holdings –And areas planted –Then yields from a crop-cutting exercise Data from 2005 made available

11. To put your footer here go to View > Header and Footer 11 Person-level data Ages will be analysed in these sessions

12. To put your footer here go to View > Header and Footer 12 Yield data The dry weights will be analysed Analysis overall and just for maize The zero yields cause a slight problem

13. To put your footer here go to View > Header and Footer 13 Activity 2 – demonstration of histograms Before the practical (which is activity 3) Follow the demonstration On histograms and population pyramids in Excel It shows Use of Excel But keeping control – you must remain in charge And not be limited by the software So it shows how to resolve problems As well as how to use Excel

14. To put your footer here go to View > Header and Footer 14 Activity 3 – practical with CAST and Excel Using CAST to understand histograms Then use Excel to construct them Being observant – as was shown in the demonstration And keeping control Remember the aims Are more to understand statistics Rather than to practice with Excel Excel is just the tool

15. To put your footer here go to View > Header and Footer 15 An example with CAST Practice with small data sets – as shown here and also with large data sets

16. To put your footer here go to View > Header and Footer 16 Population pyramid in CAST How close to this display can you get with Excel? Interpret this display

17. To put your footer here go to View > Header and Footer 17 Activity 4: Boxplots and data summary Now do the demonstration And these two practical exercises Then return to the remaining slides For a discussion of the key points

18. To put your footer here go to View > Header and Footer 18 Topics for the class discussion What was interesting? What did you discover What was difficult? What needs further discussion?

19. To put your footer here go to View > Header and Footer 19 Boxplots and histograms – CAST page 2.2.3 Are you clear how boxplots and histograms relate?

20. To put your footer here go to View > Header and Footer 20 Boxplots show outliers – CAST page 2.2.4 And this makes them very useful for data exploration as well as summary

21. To put your footer here go to View > Header and Footer 21 You can calculate percentage points The formula r * (n + 1)/100 for the rth % point Should now hold no fears For example –when there are 11 observations –and you want the median –use 50 * (11+1)/100 = 6 –the median is the 6 th highest in the sorted data If necessary look again at practical 2

22. To put your footer here go to View > Header and Footer 22 Practical problems with real data This always happens You saw a problem –with the rainfall data –and with the crop yields The solution always involves being observant You can follow guidelines for a good analysis But not always simply obey rules Become a data detective instead!

23. To put your footer here go to View > Header and Footer 23 Learning Objectives Are you now able to: Present data in a histogram –and interpret data when shown a histogram Present data in a boxplot –and interpret data in a boxplot Recognise advantages and limitations –of each method of presentation Explain what is gained and lost from data summary Interpret graphical summaries to answer questions –concerning proportions, –extremes, –medians –and quartiles Resolve simple problems with graphical displays –when real data (not text-book examples) are used

24. To put your footer here go to View > Header and Footer 24 These sessions were largely on graphical summaries The next sessions consider numerical summaries of the data