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# Looking at data sets and variables

 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. 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. 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

# Investigating Multiple Variable Pairs (scatterplots)

1) Load data into iNZight Data set

3) Select variables
(as many continuous as wanted)

4) Plot

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

More ideas & information on the iNZight tips & tricks page

# 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