1. Choose two of your numerical variables between which you think that there might be and association/relationship. Decide which variable is the predictor/ independent variable and which is the response/dependent variable. Be sure to address this completely in your report. Construct a scatterplot of your two variables. Print the scatterplot and include it in your report Include a title on your scatterplot and labels on each axis. Write a clear description of you scatterplot. (See page 147 in our text.) Figure 4.6 shows the relationship between levels of the pollutant ozone in the air (measured in parts per million, or ppm) and air temperature (degrees Fahrenheit) in Upland, California, near Los Angeles, over the course of a year. The trend is fairly flat at first and then becomes steeper. For temperatures less than about 55 degrees, ozone levels do not vary all that much. However, higher temperatures (above 55 degrees) are associated with much greater ozone levels. The curved line superimposed on the graph shows the nonlinear trend. 40 30 Ozone (ppm) 20 - 10 - 30 40 50 60 70 80 90 100 Temperature (OF) Nonlinear trends are more difficult to summarize than linear trends. This text does not cover nonlinear trends. Although our focus is on linear trends, it is very important that you first examine a scatterplot to be sure that the trend is linear. If you apply the techniques in this chapter to a nonlinear trend, you might reach disastrously incorrect conclusions