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NZAMT NZQA NZ Grapher NZ Maths Census at School Study It

3.9 Bivariate HOME | Achievement Objectives | Overview | Data sets & Variable Types | Introduction | Scatter plots | Excel | iNZight | Correlation Coefficient & Linear Model | The effect of Groups & Unusual Values | Predictions | Causality | Non-Linear Models | Discussion & Conclusion | Report Writing

Looking at data sets and variables

2

Investigating data sets

Variable types: Quantitative: Which is either Discrete or Continuous
   (Qualitative data is descriptive information)
Bivariate investigations use CONTINUOUS data (measured)

The variable to be predicted (response or dependent variable) plotted on the 'y' axis.

The explanatory variable (used to make the prediction from) on the 'x' axis.

It would be sensible to have one response variable and compare the effect of different explanatory variables. (scope for discussion)

Sometimes there will be no 'causation' between the variables so the two variables are associated, and can go on either axis.

Experimental data will have the explanatory variable being changed, and the dependent variable being measured.

Doing some RESEARCH into your chosen variables is required.

Ensure that if there is a possible causation between the variables then the independent (explanatory) variable is 'x' and the dependent variable is 'y'

EXPLAIN of why you chose them and reasons for deciding if explanatory, dependent or associated.

Investigating Multiple Variable Pairs (scatterplots) using iNZight

eg: R = the amount of rainfall per day in Christchurch, and V = the river volume of the Waimakarere river.

We can assume that the amount of rainfall will cause changes in the river volume, so the rainfall R is the explanatory variable and the river volume V is the dependant variable.

Class notes, Class notes
Class site

More ideas & information on the iNZight tips & tricks page

You will be provided with data that has several columns of values:

You will need to select appropriate pairs of data sets to establish if a relationship exists.

McDonalds data set | Excel version | csv version

McDonalds Example: Investigating Variables


p51 Ex 'C' Purpose statements

Class Exemplar:
Hawai'i Island Chain: Data csv, Information page |
Google Doc write up

Class Exemplar:
American New Cars 1993
Data csv, Information pdf |
Google Doc write up

 

 

The data set in iNZight

 

With Quantitative & Qualitative Variables

(Discrete & Continuous)

 

Investigating a single continuous variable

Athletes Weight

Athletes Height

   

Investigating a single variable with a subset (by a qualitative variable)

Athletes Height grouped by gender

 

 

Investigating Two continuous variables

Athletes Height vs Weight

 

 

Investigating Two continuous variables (Grouped)

Athletes Height vs Weight grouped by Gender

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Investigating Multiple Variable Pairs (scatterplots)

1) Load data into iNZight Data set

2) Advanced, Quick Explore, Pairs

3) Select variables
(as many continuous as wanted)

 

4) Plot

 

Notice the symmetry, and the degree of relationship seen.

Make a VISUAL inspection. This will help you identify possible bivariate combinations to explore further

More ideas & information on the iNZight tips & tricks page

 

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McDonalds Example: Investigating Variables

Product

Serving size (g)

Energy (kJ)

Protein (g)

Fat, total (g)

Carbohydrate total (g)

Sodium (mg)

Hamburger

107

1070

14.2

9.4

28.7

503

Cheeseburger

121

1280

17.1

13.6

29.2

741

Filet-o-fishTM

143

1435

16.1

14.3

37.6

712

McChickenTM

191

2086

19.6

25

47.7

1060

Chicken McNuggetsTM

108

1164

20.1

12.2

21.8

605

Quarter PounderTM

219

2414

33.3

31.3

41

1257

Big MacTM

224

2196

28.6

27.7

37.5

962

Chicken Royale

282

2589

32.4

29.1

56.1

1240

French Fries (medium)

112

1417

4.7

16.3

47.5

314

Bacon & Egg McMuffinTM

148

1301

19.7

13.8

26.8

677

Sausage & Egg McMuffinTM

159

1608

25

19.5

27.3

715

Hash brown

67

578

1.8

7.4

17.2

295

Sundae Chocolate

173

1201

6.6

4.5

55.5

157

 

 

 

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