ANALYSIS OF DATA SET USING SPSS

Analysis of data set using SPSS

Data analysis

Simple Random Sample

To select 10 companies out of total 24 companies we have used random sampling.

Simple random sampling refers to a sampling method that has the following properties.

The population consists of N objects.

The sample consists of n objects.

All possible samples of n objects are equally likely to occur.

The main benefit of simple random sampling is that it guarantees that the sample chosen is representative of the population. This ensures that the statistical conclusions will be valid.

There are many ways to obtain a simple random sample. One way would be the lottery method. Each of the N population members is assigned a unique number. The numbers are placed in a bowl and thoroughly mixed. Then, a blind-folded researcher selects n numbers. Population members having the selected numbers are included in the sample (Treynor 2008).

The selected companies are:

1

Abcam PLC

2

Antisoma PLC

3

Ark Therapeutics Group PLC

4

Immupharma PLC

5

Kiotech International PLC

6

Osmetech PLC

7

Regen Therapeutics PLC

8

Reneuron Group PLC

9

Senetek PLC

10

Vernalis PLC

Graphical representation for the Close Price

From the above graph it can be found out that that there is increasing trend.

It can be seen that there is a increasing trend in the close price for this company.

there is a decreasing trend in this data set.

It can be seen that there is decreasing trend in this data set.

There is not such an obvious trend in this data set.

Again in this data set it can be seen that there is no trend.

From the above graph it can be seen that that there is an decreasing trend.

Descriptive statistics

Descriptive Statistics

N

Minimum

Maximum

Sum

Mean

Std. Deviation

Variance

Antisoma

60

14.00

53.25

1662.75

27.7125

9.14535

83.637

Ark

60

14.50

149.02

4959.52

82.6587

32.60473

1.063E3

Immupharma

60

42.50

111.75

3838.00

63.9667

16.49618

272.124

Kiotech

56

.88

11.00

246.77

4.4066

2.30798

5.327

Osmetech

60

1.88

30.00

977.21

16.2868

9.14804

83.687

Regen

60

2.13

187.50

4234.16

70.5693

55.91218

3.126E3

Reneuron

54

2.75

40.00

783.57

14.5106

9.33517

87.145

Senetek

60

.90

3.20

108.43

1.8072

.52022

.271

Vernalis

60

1.00

999.76

1.44E4

2.4063E2

367.14619

1.348E5

Valid N (listwise)

54

The above table provides descriptive statistics for all the 10 given companies.

One way ANOVA

A One-Way Analysis of Variance is a way to test the equality of three or more means at one time by using variances.

Assumptions

The populations from which the samples were obtained must be normally or approximately normally distributed.

The samples must be independent.

The variances of the populations must be equal.

Hypotheses

The null hypothesis will be that all population means are equal, the alternative hypothesis is that at least one mean is different.

Ho: the average closing price for all the 10 companies is equal or there is no significance different in the average closing price.

H1: there is a significant difference in the average closing price.

ANOVA

Company

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

1.009E7

9

1120863.538

70.837

.000

Within Groups

8686886.586

549

15823.109

Total

1.877E7

558

From the above table it can be figured out that there is a significant difference in the average closing price since the level of significance difference is less than p value.

Post-hoc analysis

Post-hoc analysis, in the context of design and analysis of experiments, refers to looking at the data—after the experiment has concluded—for patterns that were not specified a priori. It is sometimes called by critics data dredging to evoke the sense that the more one looks the more likely something will be found. More subtly, each time a pattern in the data is considered, a statistical test is effectively performed. This greatly inflates the total number of statistical tests and necessitates ...