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(Solved): Please answer questions 2 and 4. Thank you! An overview of AAA Washington was provided in Case 5-5 ...



Please answer questions 2 and 4. Thank you!

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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 analysis performed in Case 5-5 showed that the pattern in emergency road service call volume was probably somewhat cyclical in nature. In Case 6-6, four variables were investigated: unemployment rate, average daily temperature, amount of rainfall, and number of members in the club. Average daily temperature, rainfall, and perhaps, unemployment rate were determined to be significant variables. The number of members in the club was not a significant variable. A conversation with the manager of the emergency road service call center led to two important observations: (1) Automakers seem to design cars to operate best at 65 degrees Fahrenheit and (2) call volume seems to increase more sharply when the average temperature drops a few degrees from an average temperature in the 30s than it does when a similar drop occurs with an average temperature in the 60s. This information suggested that the effect of temperature on emergency road service was nonlinear. Michael DeCoria had stated in Case 6-6 that he believes the number of road service calls received is related to the general economic cycle and that the Washington State unemployment rate is a good surrogate measurement for the general state of Washington's economy. Now he has observed that the cyclical trend of the time series seems to be lagging behind the general economic cycle. Data on the average monthly temperature and the Washington State unemployment rate are presented in Table 7-22. CHAPTER 7 Multiple Regression Analysis 329 QUESTIONS 1. Develop a multiple regression equation using unemployment rate and average monthly temperature to predict 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. Create a new unemployment rate variable and relate it to emergency road service. Give unemployment a lagged effect on emergency road service by using the unemployment rate for the month (1) three months prior to the current month and (2) 11 months prior to the current month as the data for the unemployment independent variable. Which model is better for prediction? Are the signs on the coefficients for the independent variables what you would expect them to be? Are the coefficients of the independent variables significantly different from zero? 4. Develop a multiple regression equation using the transformed average temperature variable created in step 2 and the lagged unemployment variable created in step 3 . Is this a good model? Have any of the underlying assumptions been violated?


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