1. 2.1Scatterplots A scatterplot displays two quantitative variables for a set of data.

2. 2 nd Test Score 158162144162136158175153 Final Exam Score 145140145170145175170160

3. A response variable measures an outcome of a study. An explanatory variable explains or causes changes in the response variable. We usually use x for explanatory variable and y for response variable.

4. Example How does the fuel consumption of a car change as its speed increases? Speed (km/h)Fuel Used (liters/100 km) 907.57 1008.27 1109.03 1209.87 13010.79 14011.77 15012.83 Speed (km/h)Fuel Used (liters/100 km) 1021 2013 3010 408 507 605.9 706.3 806.95

5. The data seems to form to a quadratic equation (or just a curve). The strength of the relationship is quite strong.

6. MonthSales Year 1 Sales Year 2 January210368 February205358 March202345 April245380 May237391 June290403 July299410 August345430 September326410 October355415 November359435 December371450

7. This form of the data appears to be linear. The direction appears to be positive association.

8. Two variables are positively associated (negatively associated) when above (below) average values of one tend to accompany above (below) average values of the other and vice versa. In other words, positively associated if data looks like it’s increasing and negatively associated if data looks like it’s decreasing. Data can also be neither.

9. Categorical Variables How do we add categorical data into a scatter plot? Here we treat codes as our codes as our categorical data. BTU/HrTempCodes 2.1452 2.55391 2.77422 2.4371 3.31332 2.97341 3.32302 3.39301 3.62202 3.67151 4.07192

10. Both categorical variables have a negative association, and data appears to be linear.