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Processing single and multiple variables. Module I3 Sessions 6 and 7. Learning objectives. Students should be able to: Provide and interpret the appropriate summary statistics for practical examples of quantitative data. Relate the general ideas of statistics

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Processing single and multiple variables

Processing single and multiple variables

Module I3 Sessions 6 and 7


Learning objectives
Learning objectives

  • Students should be able to:

  • Provide and interpret the appropriate summary statistics

    • for practical examples of quantitative data.

  • Relate the general ideas of statistics

    • in relation to variability, with the measures of variability

  • Recognise the role of statistics in “taming” variability

  • Construct and interpret a simple analysis of variance (ANOVA) table

  • Explain why both the standard deviation and variance

    • are used to summarise variation


  • Contents
    Contents

    • Activity 1: This presentation

    • Activity 2: Practical 1 - Review

      • A further quick check that you are comfortable

      • with the summary statistics

  • Activity 3: Practical 2

    • Apply the summaries to real data

    • And see what happens when there are outliers, etc

  • Activity 4: Practical 3

    • Processing multiple variables

    • To see whether variation can be explained

    • Which introduces the “Analysis of Variance” (ANOVA)

    • As a descriptive tool

  • Activity 5: Review of the ideas


  • Why variation is so important
    Why variation is SO important

    • From D. S. Moore

      • In Statistics: A Guide to the Unknown – 4th Edition

  • “Variation is everywhere

    • Individuals vary.

    • Repeated measurements on the same individual vary.

  • The science of statistics

  • provides tools for dealing with variation”

  • These are the tools we examine here


  • Cast and summary statistics
    CAST and summary statistics

    We continue to use CAST in these sessions


    The aim of practical 1
    The aim of Practical 1

    • Understanding simple formulae remains important

    • See the examples in this session

    • e.g. stdev, mdev, cv

    • Can you calculate them in Excel

    • Using built-in functions

    • Or from first principles?


    Practical 1 using built in functions
    Practical 1 – using built-in functions

    Example using data from the statistics glossary

    These terms all use Excel’s built-in functions

    As we show on the next slide


    Practical 1 review
    Practical 1 - review

    The statistics as Excel functions

    The terms should be (or become) familiar


    Practical 1 continued
    Practical 1 - continued

    Excel functions

    From first principles


    Dfid and climate again
    DFID and climate – again!

    Reducing the vulnerability of the poor to current climate variability is the starting point for adaptation to climate change.

    Climatic variability is a fundamental driver of poverty in poor countries. The climate is changing and it is highly likely that it will worsen poverty and hinder efforts to achieve the Millennium Development Goals.

    The poor cannot cope with current climatic variation in many parts of the world, but this issue is often ignored in poverty assessments or national development planning.

    Responses to existing climatic variability should be mainstreamed into national development plans and processes.

    Current responses by individuals and governments to the impacts of climate variability can be used as the basis for adaptation to the increasing climatevariabilitythat will be associated with longer-term climate change.

    Interpreting variability is so important


    Practical 2 summaries for climatic data
    Practical 2 – summaries for climatic data

    The start of the rains is important to many people

    And is very variable from year to year

    Consider the effect of “oddities” on the summary values


    Practical 3 introducing anova
    Practical 3 – Introducing ANOVA

    • The example of rice yields is used

    • The yields are very variable

      • The lowest is less than 20 (t/ha*10)

      • The highest is more than 60 (t/ha*10)

      • The standard deviation = 11 (t/ha*10)

  • The farmers use different varieties

  • Could knowing the varietyexplain some of the variation?

  • Variation is not so much the problem

  • Unexplained variation IS the problem


  • You use excel and cast
    You use Excel and CAST

    ANOVA table

    Sums of squares

    Mean squares

    Degrees of freedom



    Understanding the terms
    Understanding the terms

    Total corrected sum of squares

    devsq function in Excel – practical 1

    d.f. = (n-1)

    Overall mean square

    This IS the variance


    Understanding the terms continued
    Understanding the terms continued

    Residual (unexplained) or within groups sum of squares

    Is much smaller than the overall SS

    Residual mean square (residual variance)

    Is therefore also much smaller than the overall variance


    Understanding the terms continued again
    Understanding the terms continued again

    Overall standard deviation = √18.07 = 4.25

    Residual (unexplained) standard deviation = √4.97 = 2.2

    Is correspondingly much smaller


    Most used measures of variation
    Most used measures of variation

    • This example is why the standard deviation

      • and the variance

  • Are the most used measures of variation

    • Even if they are not so simple to interpret

  • You can “do arithmetic” with them

  • You can split the variation

    • into explained

    • and unexplained

  • using these terms

  • This doesn’t work with the quartiles

    • or the mean deviation


  • Learning objectives1
    Learning objectives

    • Are you now able to:

    • Provide and interpret the appropriate summary statistics

      • for practical examples of quantitative data.

  • Relate the general ideas of statistics

    • in relation to variability, with the measures of variability

  • Recognise the role of statistics in “taming” variability

  • Construct and interpret a simple analysis of variance (ANOVA) table

  • Explain why both the standard deviation and variance

    • are used to summarise variation


  • Now you know more about variability

    The next sessions show how to interpret the results as statements of risk etc


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