A random sample of 20 car users were consulted about four factors that determined their choice of car; miles per gallon, horsepower, service interval and price. Utilising SPSS, the subsequent report will aid in describing, manipulating, analysing and interpreting this sample of collected data. It will see if there is a relationship between variables and if one variable can predict another. The report will be split into the following sections: 2- Display of data that is to be used in SPSS. 3- Description of data using appropriate descriptive statistics 4- Using appropriate techniques to see if there is a relationship between variables and to see if one can predict another.

The report will conclude with a summary of all the findings. 2 The Sample The following is the data drawn at random from 20 car users. The choice of car was determined by four factors- miles per gallon, horsepower, service interval and price. I will use SPSS to analyse the data for meaning. Table 2: Random Sample of 20 Car Users From the tables above it can be seen that none of the above pairs has a strong significance and correlation. There are strong significance held by MPG and H. Power & MPG and Service but both have a weak significance. The standard deviation variance range also shows very little that has not been discussed before. Therefore hypothesis for testing proven to be wrong as no links were found as although significance high the correlation was very low at .056.

Conclusion To conclude the report it can be clearly identified that consumer preferences on Price and Service are very spread in range. Where as consumer preferences on MPG and Horse power are closer together enabling the company to focus roughly around the mean area as the standard deviation shows. This shows that consumers want the same thing in these areas. The bivariate analysis showed that there is a close relationship between: Therefore there is a close relationship between these. This correlation testing proves that they are very closely linked But the secondary testing showed that one variable cannot directly predict the another. Even though have significant areas they are not correlated enough to sustain a link.

Bibliography & References

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Bajpai, A. C. et al. (1974) Engineering Mathematics, London: Wiley & Sons Ltd Foster, J. J. (1993) Starting SPSS/PC+ and SPSS for Windows – A beginners guide to data analysis, Wilmslow, United Kingdom: Wiley & Sons Ltd Jankowicz, A.D, (1995) Business Research Projects. 2nd Edition, Cornwall, United Kingdom: Thompson Business Press

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