SADC Course in Statistics Analysing Data Module I3 Session 1

To put your footer here go to View > Header and Footer 2 Contents of this Module Session 1 2 & 3 4 & 5 6 & 7 8 & 9 10 to to & & 20 Contents Review of concepts from Basic Level Graphical summaries for quantitative data Numerical summaries for quantitative data Processing single and multiple variables Risks and return periods Tables for frequencies and other statistics Introducing statistics packages Coping with common complications Group project Presentation and evaluation

To put your footer here go to View > Header and Footer 3 Modules at basic and Intermediate levels Module B2 From the data to the report Intermediate level Module I1 – collecting data (follows from B1) Modules I2 to I4 follow from B2 Module I2 – organising the data Module I3 – analysing the data Module I4 – presenting results

To put your footer here go to View > Header and Footer 4 Module Objectives Successful students will be able to: Use descriptive analysis tools to answer practical questions. Produce descriptive statistical analyses including summary statistics, tables and graphs. Interpret common summary statistics, particularly measures of variation. Produce summary statistics in a range of ways to suit different types of user. Suggest ways of coping with common complications when analysing survey data. Work constructively in a team to produce an analysis on time. Evaluate team skills of themselves and others.

To put your footer here go to View > Header and Footer 5 Pre-requisites - computing Assume Basic Level or equivalent Mainly use of Excel: Importance of having data in list format Pivot tables and pivot graphs Calculations to produce percentages and proportions from frequencies Familiarity with SSC-Stat add-in Some familiarity with Word and Powerpoint Though manly needed for Module I4

To put your footer here go to View > Header and Footer 6 Pre-requisites - statistics Assumes Basic Level or equivalent What is statistics? Module B2 Session 3 Use of CAST Interactive statistics textbook Types of data and appropriate summaries Categorical and numerical Enthusiasm and no fear Module B2 showed statistics was logical and not so difficult?

To put your footer here go to View > Header and Footer 7 Session Overview Activity 1:Introduction –This PowerPoint presentation Activity 2 to 5:Practical –CAST –Excel for Tables –Dot plots in CAST and Excel –The objectives of an analysis Activity 6: Summary of key ideas –This presentation continued

To put your footer here go to View > Header and Footer 8 Learning objective Answer questions expected of students who have taken module B2.

To put your footer here go to View > Header and Footer 9 Practical work You use CAST At basic level To review tables and also dot plots You use Excel To produce and edit pivot tables And to produce dot plots You view demonstrations To remind you of Excel And also statistics Then the key ideas are discussed and some of the case studies are re-introduced

To put your footer here go to View > Header and Footer 10 This is a problem based course Examples are used throughout: Skills and tools are introduced to solve problems Survey of Principles of Official Statistics Used extensively in B2 Also useful to remember the principles Are countries applying them yet? Rice survey data Used in B2 to illustrate many ideas and produced in the paddy (simulation) game in I1 Tanzania and Swaziland agriculture data Large surveys Will be used again in this module

To put your footer here go to View > Header and Footer 11 CAST CAST is an electronic textbook It was used extensively in Module B2 It covers key topics in an interactive way. –Some from the course –Others related to but not covered by the course As the course progresses students are expected to –Work independently more and more –Read around –Use books to enrich the course materials

To put your footer here go to View > Header and Footer 12 Editing a pivot table How did you do? Was it easy? What questions do you have?

To put your footer here go to View > Header and Footer 13 Rice Survey Case Study - objectives Overall objectives are: To estimate the total production in the district To examine the relationship with inputs

To put your footer here go to View > Header and Footer 14 Analyses corresponding to simple objectives

To put your footer here go to View > Header and Footer 15 More complicated objectives Objectives require analysis of a single column or variable Some variables are categorical Others are numerical Objectives require analysis of multiple variables

To put your footer here go to View > Header and Footer 16 Using Excel effectively Dot plots are not on Excels menus Dot plots are not in Excels help But you decided to do dot plots in Excel! You therefore need to understand them better So you can construct them yourself And this understanding is good anyway And helps with effective data analysis It is an example Of you controlling the software And not being limited by it That applies to all software

To put your footer here go to View > Header and Footer 17 Jittered dot plots in CAST and Excel CAST EXCEL Rainfall data: 608, 746, 767, … , 1425, 1482

To put your footer here go to View > Header and Footer 18 Jittered dot plots in CAST and Excel CAST EXCEL Why are the vertical heights different in the 2 cases?

To put your footer here go to View > Header and Footer 19 Excel for analysis and training Excel is not designed as a training resource Unlike CAST – that is all CAST is for Excel is to support data organisation and analysis But we used it also to support training With dot plots And stem and leaf plots Neither of which are in the Excel menus

To put your footer here go to View > Header and Footer 20 Data exploration Before and during formal analysis For all variables But particularly for numerical variables That are treated extensively in this module Review data exploration from Module B2

To put your footer here go to View > Header and Footer 21 Dot plots - yield by variety Outliers (typing errors) are clear, but only because of the 2 nd variable They are not outliers overall

To put your footer here go to View > Header and Footer 22 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 Module B2 Session 3! So analysis is not just a set of rules It is a thoughtful process Where you become the data detective!

To put your footer here go to View > Header and Footer 23 Swaziland data was for checking

To put your footer here go to View > Header and Footer 24 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!

To put your footer here go to View > Header and Footer 25 Codes for the column Seems clear enough. Zeros and blanks still a puzzle

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

To put your footer here go to View > Header and Footer 27 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

To put your footer here go to View > Header and Footer 28 Tanzania agriculture survey This is the variable we wish to explore. It is a value between 0 and 100

To put your footer here go to View > Header and Footer 29 The data in Excel The variable to explore before analysis

To put your footer here go to View > Header and Footer 30 How to explore this value Try a pivot table a powerful feature in Excel used previously on categorical data Used here for a numerical variable

To put your footer here go to View > Header and Footer 31 Some results

To put your footer here go to View > Header and Footer 32 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

To put your footer here go to View > Header and Footer 33 Are you now ready for module I3? To continue to build skills for data analysis