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NCEA Standard:
91944 Explore data using a statistical enquiry process (5 credits)
STEP ONE: CHOOSE A PURPOSE FOR YOUR INVESTIGATION
Describe the context for your report and what you are setting out to investigate. Include your specific statistical question(s):
Does <the variable for one group> tend to be higher than <the same variable for a different group> for <a particular population>?
What is the nature of the relationship between <one variable> and <another variable> in a specific context?
How has <variable> changed over time in a specific context?
An appropriate comparison question is posed which identifies the population involved, the groups to be compared, & the variable for comparison. The possible direction of comparison is indicated.
or
An appropriate relationship question is posed which clearly describes the two numeric variables. A hypothesis is given of what you are expecting to find in your analysis.
or
An appropriate time series question is posed which clearly describes the variable to be investigated over time, including the timeframe and context.
The below options are just a starting point. You can use any of these options, or you can discuss any other statistical purpose, as long as it has a sustainability context (investigating environmental, social, or economic data).
Legend:
📦 Comparison (multivariate) investigation
📈 Relationship (bivariate) investigation
🕘 Time series investigation
Quality of life and how we use our time
Quality of life and how we use our time
Wind energy
Wind energy
Cars
Cars
Sunflowers
Sunflowers
📦 Use CODAP to explore the gender gap in use of time in this data from countries in the OECD.
EXTENSION: Use this link to open the dataset in a sampling tool. Choose your sample size, click "Get a random sample" then scroll to the bottom to explore in iNZight Lite.
📈 Collect your own data via survey to explore relationships in student time use e.g. paid work hours vs study hours or sleep vs screen time…
📦 Use CODAP to explore this NIWA dataset on wind speeds at 3 different wind farm locations around NZ (comparison and/or relationship)
📈 Carry out your own wind turbine experiment for a relationship investigation
📦Use CODAP to explore data on cars manufactured in 2015, particularly looking at engine size, fuel efficiency, and the difference in hybrid and non-hybrid cars.
EXTENSION: Use this link to open the dataset in a sampling tool. Choose your sample size, click "Get a random sample" then scroll to the bottom to explore in iNZight Lite.
📈 Use TradeMe to explore the relationship between engine size or odometer reading and price in NZ.
📦 Use CODAP to explore differences in how well sunflowers remove contaminants from polluted soil.
📈 Collect your own data on the diameter of the seed head vs the height of a sunflower, or how long a cut sunflower takes to wilt in water depending on the stem length.
📈 STEP TWO: Plan your investigation
Describe how you will collect data and manage sources of variation for your relationship investigation
Get your plan to collect data checked by your teacher.
You will provide a plan for finding or gathering data (this may include sample size, number of measures, basic procedure). Note: The teacher should check your plan before you proceed!
Describe all variables, including units. Include how you have considered sources of variation and managed them in the investigation.
Where you might find data: https://new.censusatschool.org.nz/wp-content/uploads/2023/10/DataGems_2023.pdf
📈 STEP THREE: Collect your own data
Play with the data sources above. Paste the graphs into CODAP, into your Slides, Poster or Report document
Carry out your data collection plan, collect your data - this may be done in a group
Graph your data using CODAP
This may include:
a box plot and one other graph that shows more distribution detail for your comparison investigation
a correctly labelled scatter graph, which may be colour coded by another variable to help explain features or sources of variation
a correctly labelled time series graph for your time series investigation
📦📈 STEP FOUR: Data analysis
Compare your box and whisker plots - consider centre, shape, overlap, shift, spread, middle 50%, unusual or interesting features.
Describe your scatter graph - consider type of trend, association, strength of relationship, scatter, outliers and groups
Describe your time series graph - consider long term trend, short term features and variability
Statements that describe the sample distributions are made - at least three comparative features of the sample distributions have been identified and there is strong linking between the observations, context and any statistical considerations
The strength and direction of the bivariate relationship are described in context
Short and long term features of the variation over time are described in context
📦📈 STEP FIVE: Conclusion
Make a call about whether you think there is enough evidence of a difference shown by your box plots
Summarise the strength and direction of the relationship shown in your scatter plot(s), and compare it to your original hypothesis
Summarise the features of your time series plot(s) , and compare to your original hypothesis
Evaluate your plan - did everything go to plan? what changed? what other data might you be interested in if you carried out this investigation again?
Makes a correct informal inference (using the 3/4 - 1/2 rule) about the population from the sample data that shows an understanding of sampling variability and of the context
The question is answered with contextual insight and linked to the purpose or original hypothesis.
The plan may be evaluated in terms of reliability of data, sources of variation evident in the analysis and recommendations for further investigation may be suggested.
EXEMPLAR
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