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Contents Table

Extracts from this document...

Introduction

Automobile Sales ~ Factors Concerning Purchasing                                            SPSS Data Analysis

Contents Table

1        Introduction…………………………………………………………        ………3

2        The Sample………………………………………………………………….4

1. Description of Data……………………………………………..…………...5
2. Bivariate analysis of data……………………………………………………12
3. Conclusion…………………………………………………………………..19

Bibliography & References…………………………………..

1        Introduction

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.

5- 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

 Cases MPG HORSEPOWER SERVICE INT. PRICE 1 24.00 118.00 8000.00 15000.00 2 26.00 120.00 10000.00 18000.00 3 22.00 135.00 8000.00 16000.00 4 28.00 120.00 10000.00 17000.00 5 24.00 125.00 12000.00 18000.00 6 25.00 130.00 8000.00 18000.00 7 29.00 136.00 10000.00 20000.00 8 30.00 135.00 8000.00 22000.00 9 25.00 140.00 12000.00 24000.00 10 24.00 135.00 10000.00 23000.00 11 26.00 120.00 10000.00 18000.00 12 24.00 118.00 8000.00 15000.00 13 24.00 125.00 12000.00 18000.00 14 25.00 130.00 8000.00 18000.00 15 22.00 135.00 8000.00 16000.00 16 30.00 135.00 8000.00 22000.00 17 29.00 136.00 10000.00 20000.00 18 25.00 140.00 12000.00 24000.00 19 24.00 135.00 10000.00 23000.00 20 28.00 120.00 10000.00 17000.00

1. Description of Data

Middle

Valid

20

Missing

0

MPG

 Frequency Percent Valid Percent Cumulative Percent Valid 22.00 2 10.0 10.0 10.0 24.00 6 30.0 30.0 40.0 25.00 4 20.0 20.0 60.0 26.00 2 10.0 10.0 70.0 28.00 2 10.0 10.0 80.0 29.00 2 10.0 10.0 90.0 30.00 2 10.0 10.0 100.0 Total 20 100.0 100.0

The table shows that two most popular groups are 24 (30%) and 25 (20%) accounting for 50% of the data between them. This shows that 2 groups that are very close to each other can satisfy 50% of consumers. The graph shows that data can be mapped in bell shape curve.

Graph 2:        Horse-Power as a Determining Purchasing Factor

H.POWER

 N Valid 20 Missing 0

H.POWER

 Frequency Percent Valid Percent Cumulative Percent Valid 118.00 2 10.0 10.0 10.0 120.00 4 20.0 20.0 30.0 125.00 2 10.0 10.0 40.0 130.00 2 10.0 10.0 50.0 135.00 6 30.0 30.0 80.0 136.00 2 10.0 10.0 90.0 140.00 2 10.0 10.0 100.0 Total 20 100.0 100.0

Table shows data to be spread. But two groups 120 (20%) and 135 (30%) dominate 50% of the data. But there is a gap between these two groups thus spreading the data and making it harder to predict consumer preference. The graph represents this with the curve being flatter and wider than for MPG graph.

Graph 3:        Service Interval as a Determining Purchasing Factor

SERVICE

 N Valid 20 Missing 0

SERVICE

 Frequency Percent Valid Percent Cumulative Percent Valid 8000.00 8 40.0 40.0 40.0 10000.00 8 40.0 40.0 80.0 12000.00 4 20.0 20.0 100.0 Total 20 100.0 100.0

The table shows that there are only 3 groups of data. With 8000 (40%) and 10000 (40%) dominating the responses but the range between 3 responses is large. This would explain why graph curve is flatter. This demonstrates 80% of consumers have preference within 2 ranges.

Graph 4:        Price as a Determining Purchasing Factor

PRICE

 N Valid 20 Missing 0

PRICE

 Frequency Percent Valid Percent Cumulative Percent Valid 15000.00 2 10.0 10.0 10.0 16000.00 2 10.0 10.0 20.0 17000.00 2 10.0 10.0 30.0 18000.00 6 30.0 30.0 60.0 20000.00 2 10.0 10.0 70.0 22000.00 2 10.0 10.0 80.0 23000.00 2 10.0 10.0 90.0 24000.00 2 10.0 10.0 100.0 Total 20 100.0 100.0

The table shows that consumer preference is spread over a large number of options. The data is spread evenly apart from at 18000 because 30% of consumer choose this as preference point. The data has the widest spread out of any option. Thus the graph curve is quite flat and long.

1. Bivariate Analysis of Data

Using the appropriate techniques I will now find out if there is a relationship between the variables. Once that has been done I will attempt to find out if one variable can predict another.

The route that will be taken is the two interval variable on the table as the author has already identified as to why the interval route is to be taken. The reason for bivariate testing is that it explores and identifies relationship between variables.

Two Interval                                              Two Nominal

Variable                                                     Variable

Two Ordinal

Variable

1.Descriptive

2. Inferential

Correlation’s

A Correlation enables me to quantify the strength of the relationship between two variables. A perfect positive correlation is represented by the value +1. This means that the two variables are closely related and as value of one changes the other will change proportionately. Also able to get negative perfect correlation –1 so as one changes other will change negatively proportionately the correlation is best shown by diagram below out of Saunders 1997:

-1                -0.7                -0.3                0                +0.3                +0.7                +1

Perfect         Strong                Weak                Perfect         weak                strong                perfect

Negative                                                                           Positive

Descriptive Statistics

 Mean Std. Deviation N SERVICE 9600.0000 1535.5438 20 H.POWER 129.4000 7.7893 20 MPG 25.7000 2.4730 20 PRICE 19100.0000 2954.0338 20

Conclusion

1. 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:

• Service & MPG- .889
• H.Power & MPG- .815

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

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

Saunders, M. et al. (1997) Research Methods for Business Students,

Harlow, Essex: Financial Times - Prentice Hall

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