1. 3.9 BIVARIATE DATA

2. Vocab

3. ARMSPAN V’S HEIGHT Give data to me when finished

4. ARMSPAN V’S HEIGHT 1.Find armspan and height data in student resources 2.Draw scatter plot in excel 3.Explain what is happening in words 4.Describe any features you see

5. PPDAC CYCLE PROBLEM PLAN DATA ANALYSE CONCLUSION

6. SCATTERPLOTS Open “worksheet describing patterns” 1.Write in words what is happening (first 3) 2.Write down as many things as you notice

7. DESCRIBING SCATTERPLOTS To describe the scatterplot: S.T.D Strength Trend Direction Scatter Unusual values Groupings

8. RELATIONSHIP

9. TREND

10. RELATIONSHIP

11. SCATTER

12. UNUSUAL FEATURES

13. IS WHAT YOU THINK THE SAME AS WHAT STATISTICAL DATA PRESENTS? If we were to test a group of labourers and measure their arm strength and grip strength what shaped scatterplot would you expect? Think about strength, direction of the plot. Sketch your prediction.

14. Open “labours problem” to see scatterplot and answer questions Save as we will come back to this question..

15. FEATURES… Petrol consumption vs weight of car

16. WHAT ABOUT NOW.. Petrol consumption vs weight of car

17. DOMESTIC V’S FOREIGN

18. FEATURES..

20. PRACTICE Open describing scatterplots Fully describe all 4 different situations about the temperature (degrees) and average rainfall (ml) in Fiji

21. EXPLANATORY AND RESPONSE VARIABLES Explanatory variable (independent) explains or gives a reason for a change in the response (dependent) variable. The explanatory variable is plotted on your x-axis and the response is plotted on the y-axis. For example: fertiliser and growth of a plant Sometimes we do not know what will explain the other, so we choose the variable that is easer to collect information on as our explanatory variable Example: animal weight and the weight of its brain.

22. IDENTIFY WHICH IS THE EXPLANATORY VARIABLE, RESPONSE VARIABLE AND STATE WHETHER IT WOULD HAVE A POSITIVE OR NEGATIVE RELATIONSHIP. 1.Fitness level and hours of exercise 2.Amount of fertisiler, size of plant 3.Height of sunflower and hours of sunlight 4.Number of crimes and number of policemen 5.Amount of pollutants in a pond and number of fish 6.Weight of person and amount of food eaten 7.Height of child and age of child 8.Death rate and number of doctors 9.Number of schools and literacy rate 10.River height and rainfall

23. PURPOSE STATEMENT When doing a statistical investigation there must be a purpose! Otherwise why are you doing it?? This statement must describe: The variables you are investigating Where the data came from What you are investigating and WHY This should not suggest what you think the outcome will be!

24. PURPOSE STATEMENT Example: I am going to investigate if there is a relationship between the weight of a car in pounds and the petrol consumption (miles per gallon). The source of the data is from … I have taken the weight of the car as my explanatory variable and the miles per gallon as my response variable. This is because the weight of a car is easier to measure and I want to see if I can use this to predict its petrol consumption which is harder to collect information on.

25. WRITING A PURPOSE STATEMENT 1.Armspan and wrist circumference 2.Neck circumference and hair length 3.Mammals body and brain weights 4.The size of a snappers tail and the speed it can travel

26. USING INZIGHT

27. ADDING A REGRESSION LINE The regression line gives you an equation to model and predict information (Use Armspan V Height data for example) At this level we focus on linear relationships and models. You can group that data and have different linear models if necessary. If it is obvious there is a non- linear relationship then talk about it in your reflection and improvements (Excellence)

28. MAKING A PREDICTION You use your trend line and iNZight will give you the equation. Just substitute the value you are wanting to predict into the equation.. And DON’T FORGET UNITS You must discuss how well this trend line models your data. There are two ways: 1.Looking at the trend line and commenting on how much scatter there is around the line. The closer the points are to the trend line the better the model and predictions will be. 2.Predicting a value inside your data range (a value you already know) and commenting on how far the predicted value and the actual value is – in reason. Do at least two.

29. TRY IT Open “Geyser” and complete the activity Then.. Open your labours problem : Insert a trend line Make a prediction Discuss the appropriateness of the model and your prediction.

30. CORRELATION INZight also gives you a correlation coefficient. It is a number that gives the linear relationship between the two variables a value, BUT… DO NOT USE THIS NUMBER TO JUSTIFY THE RELATIONSHIP. Only use it as a guide and to HELP justify the relationship

31. INVESTIGATION Choose ANY two variables that you are interested in that is expected to have a correlation. Draw them on a set of axes (response variable??) Draw the relationship you think may happen Examples: Height and weight, Sunglasses and UV, Fishing and swell…. Now… Go to google correlate (search in google) Type in your explanatory variable and search….. Investigate and record what you found

32. EXPLORING INZIGHT Explore each data set and write your findings.. Draw a scatter plot, features? Write in words what is happening (Brief) Add trend line Correlation coefficient Filter the data if necessary and repeat Compare different variables and comment 1.BEARS 2.Cigarette and alcohol in children's movies 3.Diamonds

33. CAUSALITY Watch Video: Readings: 1.Does chocolate make you cleaver? 2.Hair colour

34. WRITING QUESTIONS Open IQ and Books – complete activity Then.. Open Cereal data… Do some research and find an appropriate question to investigate

35. USING INZIGHT First read article on Mackerel Ask a question Open iNZight Import “mackerel” data Draw Total length v’s mean amplitude (y v’s x) Add trendline – comment Find correlation coefficient Comment on what you find Explore iNZight and test other variables

36. HMMMMM….

37. EXPLORING DATA Open “broadband internet problem”

38. SMART PHONE INVESTIGATION