Automotive Purchasing Power Comparison

Automotive Purchasing Power Comparison

Abstract

The goal of this statistic analysis is to investigate the automotive purchasing power of La Verne University students. This report include numerical analysis, graph analysis, regression analysis, hypothesis testing. Automotive Purchasing Power has been broken down into few elements to compare and investigate.

Introduction

In recent years, as the economic growth, people’s living conditions become better which lead to an increase in the sales of vehicles. Incentives provided by the manufactures, such as cash rebates and value pricing, were key features of the market during the first quarter of 2012. According to the survey conducted by NADA DATA (2012), there is a 10.2% increase in new light-duty sales which amounted to 12.7 million, compared to the sales in 2010. While, the percentage of the mix of new-vehicles increased due to the rising gasoline prices in late 2011 and early 2012. New-vehicle sales should rise by more than 10 percent during 2012, as vehicle supply increases.

In order to make sure statistic data is effective for investigation, the 200 original survey data have been cut down 39 outliers and the rest of survey data are 161. Firstly, the report will show the data content which collected from the survey. Secondly, this report will introduce the outcome which the survey data can tell. Thirdly the report will break down Purchasing Power into few elements and investigate the Automotive Purchasing Power through those elements.

All the questions we drawn in this report is use to compare the Automotive Purchasing Power between U.S. average and University of La Verne student’s average.

Research Topic and Question

The topic of this report is comparing the La Verne University student’s automotive purchasing power with U.S. average automotive purchasing power, as well as forecasting the future trend of U.S. average automotive purchasing power. A series of survey questions had been set up to find out the answers. The goal of this analysis is to investigate are there any related between budget and spend or consumer age and the year of car what they buy.

Population and parameter

A 200 statistics sample had been drawn in this survey. The population consists of the 3200 students on campus. The parameter of interest in this survey is the mean number of automotive consumed by all the students at the university.

Potential sources of bias

The survey concentrates on the data of automotive consumer’s age, gender information. Furthermore, the analysis also include the information about the consumer’s car spend, budget and year.

Data description

In the 161 survey data can be broken down into 2