1. Fitting data collection into your Stats lessons
Jared Hockly

2. Data collection 1 Groups of about 4.
Plan a quick data collection that would help you answer:
Does the size of a scrunched piece of paper affect how far we can flick it?
Quickly note down some details of your plan. Then collect some data.

3. Describe the interesting features in this data ( not asking for an inference)
Page 350 of Gamma mathematics

4. Census at school data
Students will be better prepared to analyse data if the’ve done data collection for it before.
Statsnz has some great data sets for use, but students may struggle to make the connections we’re hoping they will.

5. Intermission Why don’t we do as much data collection as we should.
Why should we find more opportunities to do it?

6. Position - Time spent on data collection will pay for itself
It is a natural way to develop contextual knowledge.
I believe student are more able to justify and show statistical insight when they have regularly participated in (and planned) data collection
Our 1.11 topics is taught to year 10s in the last part of term 4 (now)
Our unit plan is to basically cycle through multiple investigations. A good range. Obviously have some skills taught along the way.

7. Learn from errors with data collection
Design poor data collection yourself. Teach students to be critical
When you design data collection for the class, ask them why you might of made the decisions you did.
PPDAC->PPDAC

8. Data collection 2 Guessmyage.net
Set up a little table. I’ll show you a series of pictures, you have to guess their age also note down whether they were male or female. When you see their actual age figure out how wrong you were.
Before we start, is there anything else we want to record for each datapoint we collect.

9. NCEA 1.10 - comparing two samples 2.8 Questionnaires 3.8 Time series
1.11 Bivariate
2.9 Informal inference
3.9 Bivariate
E1.12 data and chance
2.10 Experiment design
3.10 formal inference
1.13 Probability experiments
2.11 Stats Report writing
3.11 experiment design
2.12 Probability methods
E3.12 reports
2.13 Simulations
E3.13 Prob concepts
E3.14 Prob distributions

10. nzc 1
gathering, sorting and counting, and displaying category data 2
gathering, sorting, and displaying category and whole-number data 3
gathering, sorting, and displaying multivariate category and whole-number data and simple time-series data to answer questions 4
determining appropriate variables and data collection methods
gathering, sorting, and displaying multivariate category, measurement, and time-series data to detect patterns, variations, relationships, and trends 5
determining appropriate variables and measures
considering sources of variation
gathering and cleaning data 6
justifying the variables and measures used
managing sources of variation, including through the use of random sampling
1.11,
1.13 7
conducting surveys that require random sampling techniques, conducting experiments, and using existing data sets
evaluating the choice of measures for variables and the sampling and data collection methods used
2.8, 8
conducting experiments using experimental design principles, conducting surveys, (and using existing data sets)
3.11 (all the rest)

11. Generating a sample (or full) dataset with your class
Divide and conquer to get a decent dataset
(sample) - randomly generates for you
Sampling from an online list (google play top 500)
Oscars
Top 100 games, most followed twitter accounts …