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# AS 3.9 Overview

#### First 14 minutes

The Legend Hans Rosling showing the importance of data and the visual anayysis & animation of data.

Review of scatterplots Quiz:
Link to worksheets.tutorvista.com

What is Bivariate data all about?

Celebrity age estimation.

Data sets with multiple variables recorded (Athletes data)

Do some research & investigate the data to decide what two variables to investigate possible relationships (CONTEXT)

Pose a Question, Analyse using scatterplots, make some predictions, and discuss findings.

Establishing relationships is used a lot in society.
Is there a Relationship? How strong? What does it mean?
Is there Causation?
Is there Correlation?

Causation is often implied where it does not apply.
Maybe there is an underlying variable.

Comparing the effect of different explanatory variables can be done.

We can model the relationship and predict values.

And Investigate other variables and relationships

And to see it in real life (link to ajpendo.physiology.org & scroll down the page to about 1/2 way)

Height and Arm span measurement. Data table

 The relationship between athletes weight and height Coloured by % body fat Sized by blood iron levels

Monitoring eel numbers and how to estimate eel age by non invasive methods
(link to maori TV go to 15min 40sec into video)

### Evidence of EACH element in the statistical cycle is required

###### Problem

Pose a Research Question: "I wonder if there is a relationship between the height and arm span of students at Nayland College"

Research the context and justify the choice of variable(s) to investigate from the data set with respect to a clear purpose for the investigation.

Make a prediction: "I think that... because..."

###### Plan

Do some contextual research, investigate variables, reference material.

What variables are needed?

What are the 'Explanatory' & 'Response' variables (if any).

Select & clean data if needed (from a provided multivariate data set )

###### Conclusion

Answer the Research Question.

Comment on the accuracy and usefulness of the model and predictions.

Comment on any assumptions, limitations, related research, other variables, groups, bias, sources or error, usefulness of the investigation

###### Data displays

Produce scatterplots of raw data.

Identifying groups or 3rd variables may be useful.

###### Analysis

Make an initial visual interpretation of any relationship & decide on what model to apply.

Fit a regression line to the scatterplot (or a curve or piecewise function)

Describing and Comment, in Context on the:
- Trend type (linear, non linear, uniform)
- What model to apply and why
- Association (positive, negative)
- Strength of the relationship (strong, moderate, weak, none)
- Features (groups, clusters, 3rd variable)

Make some predictions (from the explanatory variable)

Use technology such as EXCEL and iNZight