You are asked to develop a multiple regression model that indicates the relationship between a person’s behavioral characteristics and the daily cost of food (daily cost). The predictor variables to be used are subject’s limiting weight (sr did lm wt), subject being a smoker (smoker), subject’s number of hours in front of a TV or computer screen (screen hours), subject’s being sedentary versus active (activity level: note that you will need to recode to a dummy variable), percent of subject’s calories from a fast-food restaurant

(pff), percent of subject’s calories eaten at home (P ate at Home), whether the subject is a college graduate

(col grad), and household income (hh income est).

Also, the model should include a dummy variable to indicate the effect of first versus second interview.

a. Estimate the model using the basic specification variables indicated here.

b. Estimate the model again, but in this case include a variable that adjusts for immigrant versus native person (immigrant).

c. Estimate the model again but in this case include a variable that adjusts for single status versus a person with a partner (single).

d. Estimate the model again, but in this case include a variable that adjusts for participation in the food stamp program (fsp).

Automobile-Fuel Case Study Project You have been asked to conduct a study to determine the variables that influence automobile fuel consumption. Your study is part of a national effort that will develop policies to reduce dependence on fossil fuels. Considerable national discussion and various economic studies have focused on this question for a number of years.

Many economists have argued that an important part of the solution is higher gasoline prices. They point to the fact that for many years European gasoline prices have been much higher, in part because of high taxes on each liter of gasoline sold for automobile consumption. And, European vehicles tend to be smaller and more fuel efficient compared to U.S.

motor vehicles.

Others argue that the automobile is so important in the lives of U.S. citizens that they must drive, and higher prices will merely increase the cost of travel. The limited availability of public transportation compared to Europe is part of this argument.

From this comes the argument that government regulation must be used to establish minimum fuel-consumption standards for all automobiles sold in the United States. It is argued that such CAFÉ (Corporate Average Fuel Economy)

standards place manufacturers on an equal level with regard to fuel economy and avoid competitive vehicle features that would increase fuel consumption. These standards were first introduced in the late 1970s and then essentially ignored until new standards were introduced in 2010.

Another argument is that automobile driving is a central part of the U.S. society and fuel savings will really come only with changes in the overall economy. Changes would include increased use of public transportation, workers closer to work sites, working by electronic communication from home, economic recession, and other societal factors.

Your task is to conduct appropriate statistical analysis to help answer some of the questions posed here and to increase understanding of the question. Your first step was to collect data from national sources and prepare the data file Automobile Fuel Consumption. This data file provides monthly data for a number of measurements collected since 2005 and extending through 2010. The variables contained in this file are shown in the variable description table included in the Chapter 12 appendix.

Your assignment is to prepare a rigorous statistical analysis and to write a report that clearly presents your conclusions and explains your analysis. Your report is limited to two pages, with appropriate supporting material in selected appendices.

The reader should be able to understand your work from the two-page report.

Your professor will also provide various guidelines and analysis recommendations. You might note that when data are collected over time, many analysts will present graphs that indicate the levels of key variables over time. Recall from Chapter 12 how overall price elasticity can be estimated using log transformations.