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Support for School Statistics from Statistics NZ. Statistics New Zealand Auckland Maths Assoc, University of Auckland Tue 25 Nov 08. Achievement objectives for today:. Participants will: Use Stats NZ resources to deliver curriculum objectives

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Support for School Statistics from Statistics NZ

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Support for School Statisticsfrom Statistics NZ

Statistics New Zealand

Auckland Maths Assoc, University of Auckland

Tue 25 Nov 08

Achievement objectives for today:

  • Participants will:

  • Use Stats NZ resources todeliver curriculum objectives

  • Feel more confident and have more funwith teaching the stats in Mathematics and Statisticsin the NZ Curriculum

  • Find out (if time)what do (some Stats NZ) statisticians reallydo!

Activities with

  • After an introductory ramble:

  • Schools cornerStatZing!SURFs 1, 2, 3CensusAtSchool (a mention)

  • Table Builder (= TB); esp Census data

  • Infoshare:Time series galore

  • Hot Off The Presses (= HOTPs): HOTPs and Statistical Literacy

  • QuickStats: about your place etc

  • Then: what do (some) statisticians really do!

Curriculum and Stats NZ Resources 1:

  • The threads in the Stats and Probability strand:

  • Statistical investigationphenomena involving: multivariate (case) datasets time-series datasets

  • Statistical literacyreports with words, numbers, graphsrisk

  • Probabilitydistributionsdependence etc

Curriculum and Stats NZ Resources 2:

  • The threads … and resources for them:

  • Statistical investigationSchools Corner,StatZing!phenomena involving:HOTPsmultivariate (case) datasets:SURFs,TB,CaS time-series datasets: Infoshare

  • Statistical literacyNZ in Profilereports with Quickstats

  • words, numbers, graphs:HOTPsrisk, relative risk:HOTPs, Tables

  • Probabilitydistributions:Tables dependence etc:Tables; 2 way

Curriculum and Stats NZ Resources 3:

  • Some are designed for schools

  • Some are(a big one) inadvertently useful for schools!EG: The HOTPs (Hot Off The Presses):EG: a rich source of real (we hope) info:

  • New Zealand Income Survey: June 2008 quarter (a big one)

  • Highlights  |  Commentary  |  Technical notes  |  Erratum  |  Tables  | 

Stat investigation: Story, Data

Time series

Stat investigation: Methodology:

defining questions, sampling methods,

errors (samp and non)

etc etc etc etc

Stat Literacy:

Evaluate stat reports

(L 6,7,8)


One-way tables Two-way tables

Statistical Enquiry Cycle: PPDAC … PPDAC … PPDAC … PPDAC … PPDAC …

Why be nice to schools??

  • Stats NZ:The World:

  • We need our clients to be informed & positive

  • School stats is a vital way to achieve this

Respondents: People Businesses






Users: Public Professional Technical

Two groups with converging interests:


Mathematics and Statistics

Education Community


Official Stats


Vision: an informed society using statistics.

Curriculum: students will be: thinking mathematically and statistically; solving problems, modelling situations.

A small problem:



  • Unit-record multivariate datasets:Teachers need them!Official Stats agencies have lots but can’t release them!




Some smart solutions:


  • CensusAtSchool (sort-of)SURFs for Schools: 1, 2, 3Tables by geographical Area

Census at school 2009

New dates:3 March 2009 until 9 April 2009

Register online:

If you have previously registered, OK. Confirmation in November.

Funded:X% by Stats NZ(1-X)% by MoE

Expertise:Lots of it; from UoA

New for 2009

Teachers get their class results back if they choose.

Early in year so 2009 data can be used for 2009 teaching.

New questions: from consultations:Dept of Stats UoA, MoE, Stats NZ, teachers nationwide

Questionnaire critiqued by StatsNZ Questionnaire design team

4 Releases by title: HOTPs

3 Infoshare

2 Table Builder

5 QuickStats

1 Schools Corner: StatZing!, SURFs

Find Info by/for …


StatZing! Latest Sec (Economics)


SURFs for Schools

  • Synthetic Unit Record Files:

  • Multivariate datasets from Stats NZ surveys

    • Income supplement from the 2004 Household Labour Force Survey

    • 2001 Household Savings Survey

    • Coming soon – 2006 Census

2001 Household Savings Survey SURF

  • Based on a survey that collected information including income, assests, debt,net worth.

  • 300 synthetic people representing the 5000+ people who responded to the survey.

2001 Household Savings Survey SURF

  • Variables include:

Using the SURF

  • Teacher page for each activity

  • Curriculum links

  • Possible answers

  • Available as a PDF document

What can we improve?

For teachers

For students

Census: SURF 3

  • Under development; final checking

  • Based on 2006 Census of Population and Dwellings

  • Contains unit record datasets for each of New Zealand’s 16 main Regional Authorities

  • 300 synthetic people who represent everyone that responded for each region

Census: SURF 3

  • Variables included

    • Sex

    • Work and Labour force status

    • Qualification

    • Ethnicity

    • Income

    • Age Group

    • Mode of transport to work

    • Hours worked

    • Cigarette smoking behaviour

    • Access to a cellphone/mobile phone

    • Access to the internet

Preserved relationships

Limitations: SURF 3: Census

  • Synthetic data

  • Not all relationships and patterns are preserved

  • Joining tables together does not represent the whole of New Zealand

  • However, you can compare regions!

Battle for the ‘greener suburb’:an example using case data

  • Compare the ‘traveling to work’ habits of geographic areas.

  • Which area has the ‘greener’ workers?

    • Walking / Running / Cycling

    • Public transport

    • Carpooling???

    • Working at home?

      (Graphic from CensusAtSchool)

Battle for the ‘greener suburb’:where to find the data

  • We want a data source that contains information about modes of travel to work by area units.

  • Luckily, we have the 2006 Census of Population and Dwellings on Table Builder!

2 Table Builder

2006 Pop Census

Selected tables

Age by Sex; soon …

Travel to Work

Travel to work in the four Auckland cities

Travel to work in Kapiti and Wellington

Table Builder: Datasets on Area Units:

  • At this pointthe screen-shots

  • stop.

  • But there’s a

  • 2-slide

  • summary … for schools: short guide: p1

  • Schools Corner

  • SURF (No. 2)

  • About the data source | The dataset | Activities(copy the dataset and paste into your spreadsheet)

  • StatZing! (the latest Activities)

  • Find by …(find old StatZing!s etc)

  • Table Builder

  • 2006 Population Census

  • Selected tables

  • Travel to Work

  • Expand (find the Areas you want) Tick (use the ticks above and to left)

  • Click the Table icon

  • Actions, download to XL format

  • (then copy and paste into your spreadsheet package)

  • Age by Sex for 1996, 2001, 2006 (then as above) for schools: short guide: p2

  • Infoshare

  • Browse

  • Work, Income and Spending

  • Linked Employer-Employee Dataset

  • Age and ANZIC96 (ANZ Industry Classification 1996)

  • Select a few items, and for Time, Select All

  • Go

  • Pivot clockwise, to get data into a column

  • Save as xls

  • (copy the dataset and paste into your spreadsheet)

  • Releases by Title (Takes you to Hot Off The Presses)

  • NZ Income Survey

  • NZ Income Survey; June 2008

  • (then explore these:)

  • Highlights|Commentary|Technical notes|Erratum|Tables 

  • QuickStats about a Place

  • (and also see QuickStats about a Subject, and NZ in Profile)

  • Place List (and find your suburb)

  • (and use the 12 tabs).

What Statisticians do all day: an eg:

  • The new Immigration Survey:Pop: 36,620 approved immigrants in 2004Sample: 7,125 of them

  • We find Estimates (via ‘resampling’) with Sample Errors (= half the confidence interval)

Sample Error vs Estimate:

Hmmmmmm: what does that show?

Sample Error (up) vs Estimate (across):

For cells from the Immigration Survey NZ

Sample Error fromthe data by Jackknife

(ie resampling)

Standard Deviationfrom binomial model

= √ (p (1-p) N)

Sample error has lots of variation: Can we explain some of it? How? What function might it fit?

For the lower (blue) points, what did we forget?

Some consultation:

  • We asked Pat:

You need to multiply by the 2 value:

Sample Error = z * Standard Error

= 1.96 * Standard Error

They forgot to multiply by 2

(or 2 ish)

Why are they so dumb in Wellington??

Sample Error vs Estimate:

Sample Error fromthe data by Jackknife

(ie resampling)

Sample Errorfrom binomial model

= z * √ (p (1-p) N)

Hmmmmmm: how does that look?

To confidentialise, we added noise: ± 4

Noise fromconfidentialising is about this big:

Noise from samplingvaries, but is this big

Does the noise from confidentialising matter?

What do we do all day?

  • We hope you enjoy statistical discovery as we do!!

In a Stats office

In a Stats classroom

Find a problem that matters

Find some Data (Evidence)

Talk, scratch heads

Do some graphs

Try models using Maths

Make mistakes

Consult with wise heads

Do more graphs

Make decisions

Communicate results in:

words, numbers, graphs

Find a problem that matters

Find some Data (Evidence)

Talk, scratch heads

Do some graphs

Try models using Maths

Make mistakes

Consult with wise heads

Do more graphs

Make decisions

Communicate results in:

words, numbers, graphs

Gender Balance in Waitakere:

The Maths and Stats teacher’s vital role:

  • If the next cohorts of adults can handle statistical evidence and thinking:

  • That’ll be nice for Statistics NZ!which produces: social, economic and environmental stats

  • That’s utterly essential for solutions to NZ’s and the Earth’s challenges.

  • Enjoy!!

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