1. Run four simple linear regression models using total number of emergency road service calls as the dependent variable and unemployment rate, temperature, rainfall, and number of members as the four independent variables. Would any of these independent variables be useful for predicting the total number of emergency road service calls?
2. Create a new temperature variable and relate it to emergency road service. Remember that temperature is a relative scale and that the selection of the zero point is arbitrary. If vehicles are designed to operate best at 65 degrees Fahrenheit, then every degree above or below 65 degrees should make vehicles operate less reliably. To accomplish a transformation of the temperature data that simulates this effect, begin by subtracting 65 from the average monthly temperature values. This repositions "zero" to 65 degrees Fahrenheit. Should absolute values of this new temperature variable be used?
3. Develop a scatter diagram. Is there a linear relationship between calls and the new temperature variable?
4. If a nonlinear relationship exists between calls and the new temperature variable, develop the best model.
An overview of AAA Washington was provided in Case 5-5 when students were asked to prepare a time series decomposition of the emergency road service calls received by the club over five years. The time series decomposition performed in Case 5-5 showed that the pattern Michael DeCoria had observed in emergency road service call volume was probably somewhat cyclical in nature. Michael would like to be able to predict emergency road service call volume for future years.