Question 1

What is NCEA AS 91035 about?

Accepted Answer

You need to investigate a dataset with multiple variables (like age, income, and location all in one dataset) by working through the statistical enquiry cycle. This means asking a comparison question, creating displays to visualize the data, calculating summary statistics, and then writing up what you found about the patterns and differences you discovered.

Question 2

What do students need to do for AS 91035?

Accepted Answer

1. Ask a clear comparison question based on the data you've been given 2. Choose and create appropriate displays (graphs, charts) to show the relationships between variables 3. Calculate summary statistics (median, quartiles, minimum, maximum, spread) 4. Describe and compare the distribution features in context (shape, centre, spread, overlap, any unusual patterns) 5. Write a conclusion that explains what you found, backed up by the statistics and graphs as evidence

Question 3

What's the difference between Achieved, Merit, and Excellence for AS 91035?

Accepted Answer

Achieved: You work through each step of the statistical enquiry cycle and make comparisons, using displays and summary statistics to support your findings. Merit: You link your findings back to the context and population, making supporting statements that reference specific summary statistics, data values, or features visible in your displays. Excellence: You combine statistical knowledge with understanding of the context throughout your entire investigation, and you reflect on your process or discuss alternative explanations for what the data shows.

Question 4

What mistakes do students typically make in AS 91035?

Accepted Answer

1. Not linking statistical findings back to the actual context and population being studied — saying 'the median is 5' without explaining what that means in real terms 2. Creating displays but not actually using them to describe features like spread, shape, or overlap between groups 3. Missing key summary statistics like quartiles or spread, or calculating them but not discussing what they reveal about the data

Multivariate data

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