SADC Course in Statistics

Analysing numeric variables

Module B2, Session 15

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Learning Objectives

students should be able to •Group numeric variables

•So they can be analysed like categorical variables

•Summarise numeric variables•Without grouping

•Apply the objectives of Session 14•to the analysis of numeric variables

•Outline a simple table or graph• for a numeric variable

•Complete a simple table or graph• given an objective and an outline.

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Contents

• Introduction of the ideas

• Practical 1• How to process numeric variables

• Practical 2• Using Rice survey and the Tanzania survey

• Discussion

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Overall objectives – small example

• Rice survey

• Two objectives

1. Estimate total rice production • in the district

2. Investigate the possible relationships• between production and cultural practices

• These are both objectives that relate to the yields

• A numeric variable

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The district for the rice survey

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The data ready for analysis

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Objectives and measurements

• The objectives led to the questions• the measurements we took

• 1st objective – total yield in the district• So we measured the yield on sampled plots

• 2nd objective – relationships with cultural practice

• so we asked about size of field, fertilizer, variety

• See how the objectives lead directly to the questions in the survey

• The “students” also gave simple objectives• Just from looking at the data

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For the categorical variables:

These tables and graphs were to satisfy the objectives

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Practical work

• The practical benefits from discussion• So it is good for students to work in pairs

• As in the last session• First the rice survey data are analysed• Then a larger data set is used• This time we start with the rice data• Then move on to the sunshine data• That was organised in Session 10

• Now go and do the practical

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Key points

• We review the key points from the practical work

• Using both the rice data

• And the sunshine data

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For a numeric variable

• Either they can be grouped• The grouped variable is then categorical • The categories are in order• So it is an ordered categorical variable

• Or they can be analysed as they stand

• Both were investigated in the practical work

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Grouping a numeric variable

• The practical illustrates 3 methods

• A simple method • Also gives practice in using logical calculations

• Two alternatives • Make use of IF statements• Which also can be used to add labels to the groups• They are also described in the Excel guide chapter 4• For which there is also a demonstration

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Sunshine data - objectives

• The solar cooker can only be used• when there is sufficient sun

• Morning sun is needed• To cook lunch – 3 hours in period from 9am to 1pm

• Afternoon sun is needed• To cook dinner

• One hour of sun either time• Is sufficient to pasteurise water

• The hourly sunshine data were organised• In Session 10 – given these needs

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The data – ready for analysis

Total sun (hrs) between 9am and 1pm

From these 12 values. What is the chance of

cooking?

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Objectives and analyses again

Simple objectives

A single variable

More complicated objectives

Two variables or more

Apply the same idea to the sunshine data

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Sunshine data - objectives• How often is

cooking possible in the morning?

• How often is cooking possible in relation to the early-morning sunshine

• Simple objective– one variable

• More complicated objective– two variables

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So:

Are you now able to

• Group numeric variables• So they can be analysed like categorical variables

• Summarise numeric variables• Without grouping

• Apply the objectives of Session 14• to the analysis of numeric variables

• Outline a simple table or graph• for a numeric variable

• Complete a simple table or graph• given an objective and an outline.

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