Whitners Autoplex Prices Differ from the National Average
Team D
RES 341
April 18, 2012
Dr. Leroy Paul

Whitners Autoplex Prices Differ from the National Average Intro
Research Process Consumers purchase new vehicles for many reasons: practicality, functionality, or as a status symbol. Trying to fill that need, Whitners Autoplex is an auto dealer that sells import and domestic automobiles (Lind, Marchal & Wathen, 2008). Buyers, ages 20 to 59, have purchased vehicles at Whitners. Team D, after reviewing the provided data, knows that over the past couple years the car dealer’s average selling price for an automobile was $22,000. Team D’s null hypothesis states that the selling prices in the data set remain unchanged from the established $22,000 average. However, Team D has faith that its alternate hypothesis that the average has changed will prevail. The following will provide the purpose of the research, problem definition, research hypothesis, and a look ahead to the following weeks concerning the study. Using historic and current data, Team D intends to prove the $22,000 average price of a car has changed, and most likely increased due to many factors. If Team D’s alternate hypothesis proves true, then Whitners Autoplex has been charging far more less than surrounding, or even nationwide, auto dealers. Knowing the average cost of a vehicle is important to auto dealers because profit is what allows a business to prosper and grow.
Problem
Team D, believes the prices of vehicles has changes among the years. Team D, wants to gather information in order to find out why is it that the prices for a vehicle has varied among the years and among car dealers. Whitners Autoplex average price for a new vehicle is $22,000, while the national average for a vehicle in the United States is $30,748. As one can see, there is a great difference between the average at Whitners Autoplex and the national average. This research will helps Team D discover the variables involved in the difference between averages.
Hypothesis
When looking into the possible outcomes one can see that there are various. The hypothesis Team D chose is Whitners Autoplex prices differ from the national average. One outcome can be that Team D it is correct in the hypothesis established. Second outcome can be that it is inconclusive because of lack of reliable data. Third outcome is that the hypothesis it is not true and there is no conclusive reason the prices are different. Besides observing various possible outcomes, Team D also has to take a look at the variables involved. The variables involved in the data provided are age groups, amount of money spent, and domestic versus imported vehicle. The age group is measured in a interval scale since is from age 20-59. This scale does not have an absolute zero. Because no one under the age of 20 has purchased a vehicle at Whitners, there is no absolute zero. The amount of money spent it is in ratio scales. This is because money has an absolute zero. All age groups have purchased vehicles at Whitners; therefore, sales transactions have taken place. The domestic versus imported vehicle is in nominal scale. In this case, the nominal scale data give two options which are domestic or imported vehicles. This data provided does not give the team enough information in order to prove or disprove the hypothesis. For this reason, the team needs to gather further information in order to conclude the outcome of the research.
Peer Reviewed Articles Team D researched four peer-reviewed studies and articles that helped support the team’s assumption that Whitner Autoplex auto prices of vehicles sold differed from the national average. The team set out to prove the hypothesis by researching the many factors that affect the price of an automobile. These factors are eco-friendly vehicles, warranties, added luxury items, and how the Internet changes the average price of a car. Customers who wished to purchase environmentally friendly vehicles must expect to pay more for this type of automobile (Royne, Levy & Martinez, 2011). Cars that get more miles to the gallon, use alternative energy, or contribute less waste to the environment cost more than vehicles that do not (Royne, Levy & Martinez, 2011). “Evidence suggests that environmentally concerned consumers were generally younger, more affluent, more educated, more urban, and more politically liberal than other consumers” (Royne, Levy & Martinez, 2011, para. 9). The researchers’ findings suggested the auto industry use educational messages targeted toward older shoppers to encourage the purchase of environmentally friendly vehicles to that particular target market (Royne, Levy & Martinez, 2011). Another possible explanation for changes in auto prices could be the warranties that increase the total price of the automobile. Manufacturers add warranties that increase the cost of cars and trucks (Chu & Chintagunta, 2011). The auto insurance industry measures consumer risk aversion, or the level of risk a person is willing to take. For instance, a married couple with young children takes fewer risks (risk aversion) and because of this, they value longer warranties (Chu & Chintagunta, 2011). Studies also show that the older consumers get the more risk averse they become, thus buying longer warranties (Chu & Chintagunta, 2011). This could mean that because consumers are willing to pay more for warranties the auto industry will include the warranties to the price of the automobile being sold, changing the original price of the car. Team D also speculated that increasing luxury items in a vehicle would increase its price. Many older consumers prefer luxury items in their vehicles. As such, the auto industry may cater more to the needs of its older consumers. Throughout recent history, the auto industry has targeted younger drivers. This trend has changed because baby boomers, those born between 1946 and 1964, can afford new cars whereas the younger crowd has lagged behind in sales (Coughlin, 2004-2005). J.D. Power and Associates predicted the market for new cars and trucks for young people to grow less than 1% for the next decade, while older consumers have become the largest market for luxury automobiles (Coughlin, 2004-2005). The baby boomers account for more than 40% of the large luxury-car market (Coughlin, 2004-2005). For the auto industry, this means catering to an entirely different demographic, which will raise the cost of automobiles. The industry must offer vehicles that fit the needs of its older buyers, to include the following: • Spacious and high-roof vehicle optimization • Easy accessibility to enter and leave the vehicle • Steering wheels that slide in and out of the dashboard • Adjustable floor pedals • Larger dials and instrumentation In reviewing its fourth peer-reviewed article, Team D researched how the Internet affects the average price of an automobile. With matched survey and transaction data on 1,500 cars purchased in California, researchers proved that the Internet lowers prices for automobiles (Zettlemeyer, Morton & Silva-Risso, 2006). Consumers gained information from the Internet about dealer invoice prices, and the referral process of online buying services helped consumers gain lower prices (Zettlemeyer, Morton & Silva-Risso, 2006). The combined information and referral price shaved 1.5% to 22% off the cost of the car (Zettlemeyer, Morton & Silva-Risso, 2006). As of March 2012, the average price of a new care had reached an all-time high of $30,748, up from $28,771 a year prior (Eisenstein, 2012). The recession forced many automobile manufacturers to discount heavily their products, but those days are over (Eisenstein, 2012). The improved equilibrium between supply and demand means fewer discounts (Eisenstein, 2012). In addition, auto manufacturers incorporate style and technology in the newer vehicles, which also increases pricing for these upgrades (Eisenstein, 2012).
Sampling Design Team D used a mathematical formula to compute the mean of the 80 vehicles that Whitners Autoplex sold. Figuring on no other variables, the mean was $22,000. Once the mean was computer, Team D wondered how this compared nationally. To obtain this figure, Team D decided to research studies that have already sampled the average cost of new automobiles sold nationwide. A sample involves studying only some elements selected from a large population, for example, the average cost of automobiles sold throughout the United States (Doane & Seward, 2007). Research revealed the average cost of a car sold in America is $30,748 (Eisenstein, 2012). The information gathered from the sampling involved the use of the 2010 Consumer Expenditure Survey, information collected for the U.S. Bureau of Labor Statistics by the U.S. Census Bureau. As such, Team D has full confidence about Census Bureau statistical information. Had the team been forced to conduct its own statistical research, unconscious bias may have occurred either through the selection process or the overestimation or underestimation of set parameters (Doane & Seward, 2007). Team D would only have a few weeks to conduct a nationwide survey of average car prices in America. Lacking time and resources, the statistics would have lacked both reliability and validity. Reliability is a measurement repeatable over time. Validity involves content validity. The team members wonder if they can provide sufficient operational definitions to measure the hypothesis and construct validity. Furthermore, team members wonder if they are measuring what they intended to measure. In the end, Team D finds the information in the 2010 Consumer Expenditure Survey to be both reliable and valid.
Data
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Average Price of a Car
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Ethical Concern Gathering data for the research should not bring about ethical concerns. Nobody is gathering personal information of customers during the data collection process. Team D gleaned the national average from information based from the 2010 Consumer Expenditure Survey, and Whitners Autoplex only provided numerical data about the numbers of automobiles sold and the cost of those vehicles. Team D is violating no rights or rules while conducting this survey research project.
Data Analysis Central Tendency Mean: 23,218.16.00 Median: 22,831.00 Mode: 20,642.00 Dispersion Range = 20,379 [maximum (35,925) – minimum (15,546)) Interquartile range= 5,659 [3rd Qtr (25,787.00) – 1st Qtr(20,128)] Skew: 0.73 EXTRA information Sample Standard Deviation: 4,354.44 Minimum = 15,546 Maximum = 35,925 Skew: 0.73 1st Qtr: 20,128 Median: 22,831 3rd Qtr: 25,787

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Frequency Distribution and Histogram
|Frequency Distribution - Quantitative |

| | | | | | | | | | | | |Price | | | | | |Cumulative | | | lower | |upper |midpoint |width | frequency |percent | frequency |percent | | |14,000 |< |16,000 |15,000 |2,000 |3 |3.8 |3 |3.8 | | |16,000 |< |18,000 |17,000 |2,000 |5 |6.3 |8 |10.0 | | |18,000 |< |20,000 |19,000 |2,000 |10 |12.5 |18 |22.5 | | |20,000 |< |22,000 |21,000 |2,000 |19 |23.8 |37 |46.3 | | |22,000 |< |24,000 |23,000 |2,000 |11 |13.8 |48 |60.0 | | |24,000 |< |26,000 |25,000 |2,000 |13 |16.3 |61 |76.3 | | |26,000 |< |28,000 |27,000 |2,000 |8 |10.0 |69 |86.3 | | |28,000 |< |30,000 |29,000 |2,000 |5 |6.3 |74 |92.5 | | |30,000 |< |32,000 |31,000 |2,000 |2 |2.5 |76 |95.0 | | |32,000 |< |34,000 |33,000 |2,000 |2 |2.5 |78 |97.5 | | |34,000 |< |36,000 |35,000 |2,000 |2 |2.5 |80 |100.0 | | | | | | | | | | | | | | | | | | |80 |100.0 | | | |
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Central Tendency

References
Chu, J., & Chintagunta, P. K. (2011, March). An empirical test of warranty theories in the U.S. computer server and automobile markets. Journal of Marketing, 75(2). Retrieved (April 16, 20120) from https://ehis.ebscohost.com/eds/detail?vid=8&hid=115&sid=b84b4ed0-f78c-4bef-a1c1c58843d205de%40sessionmgr11&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=bth&AN=58009550
Coughlin, J. F. (2004/2005, Winter). Not your father's auto industry? Aging, the automobile, and the drive for product innovation. Generations, 28(4). Retrieved (April 16, 2012) from https://ehis.ebscohost.com/eds/detail?vid=6&hid=115&sid=f72cd27a-4e88-489b-b4ec-cca5c83ec523%40sessionmgr110&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=f5h&AN=16590863
Doane, D. P., & Seward, L. E. (2007). Applied Statistics in Business and Economics. Boston, MA: McGraw-Hill/Irwin.
Eisenstein, P. (2012). New car prices high all-time record. Retrieved (April 16, 2012) from http://www.thedetroitbureau.com/2012/04/new-car-prices-hit-all-time-record/
Paul, Roy. (2012). Research and Evaluation I [PowerPoint slides]. Retrieved from https://classroom.phoenix.edu/afm211/secure/view-thread.jspa?threadID=43394746.
Royne, M. B., Levy, M., & Martinez, J. (2011, Summer). The public helath implications of consumers' environmental concern and their willingness to pay for eco-friendly product. Journal of Consumer Affairs, 45(2). Retrieved (April 16, 2012) from: https://ehis.ebscohost.com/eds/detail?vid=4&hid=3&sid=66279ad2-841e-46348040ea5d18010491%40sessionmgr13&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=eoh&AN=1244244
Zettelmeyer, F., Morton, F. S., & Silva-Risso, J. (2006, May). How the Internet lowers prices: Evidence from matched survey and automobile transaction data. Journal of Marketing Research, 43(2). Retrieved (April 16, 2012) from: https://ehis.ebscohost.com/eds/detail?vid=2&hid=3&sid=8c354f3c-770f-4651-8d01- 6189d4025afc%40sessionmgr4&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=bth&AN=209493 78