It revised various tools that construct graphical data in addition to performing various statistical tests. Furthermore, it has many other facilities to enhance the quality of statistical information. This assignment will demonstrate two groups of sample data acquired from two large sectors of businesses. This information was obtained from a database called FAME. Each set of data contains 34 variables from companies within the sectors. The first sector is printing, publishing & media, also known as sector 22 and the second sector is transport, sector 63. The variable acquired from this database was company turnover.

SPAS uses two sets of samples in order to analyses and compare. In order to present an evaluation on the companies, a box plot (box & whisker) will be created. A box plot is particularly useful when observing two sets of data as it shows the distribution, median, maximum and minimum values, and the lower and upper quartiles which can be comparable. A box plot will also expose any outliers as they will be displayed outside the whisker bounds. (BUGBEAR 2005). This assignment also contains a time series of 20 values consisting of the number of exports outside the I-J.

This is obtained from the office of national statistic website. Results & Discussion: SECTION A Cross sectional data AY Variable Definition The variable is represented as the turnover I. E. Sales. (Valerie J. Eaton & John McCollum 2013). This can be calculated as gross profit plus cost of sales or selling price multiplied by sales volume. The figures presented are from the latest annual accounts from these companies. Findings from the analysis AY)I) Comparative box plot:- A postbox has been created between the two sets of data; sector 22 and sector 63 sing the ASPS software.

Appendix 1 displays both company names and their turnover. By observing both medians, sector 22 (media) has portrayed a lower turnover than sector 63 (transport). In addition, both foxtrots consist of negative skews as the median line is closer to the lower quartile. However it is portrayed more prominently in sector 22. Also sector 22 has one extreme value whereas sector 63 has 3. This can be seen as asterisks outside the whisker bounds. AY)ii) Summary measures These measures were tabulated using the SPAS package. The data used was the remover variables of sector 63 and 22.

Furthermore, it is statistically improper to compare values kurtosis Just by looking at the value. Therefore, further statistical tests should be performed in order to gain a more accurate outcome. The minimum and maximums in both sectors are significantly different. For instance, sector 63 has a larger minimum and maximum figure than of 22. This could indicate that the transport industry is operating on a much larger scale than printing, publishing and media. The standard deviation is also larger in sector 63 SECTION B Time series data Bal Variable Definition A time series represents historical data. (BUGBEAR 2005).

From this data, a trend can be denoted as there are 20 values, each value symbolizing 1 year. The variable that I have chosen is the investments in transportation and distribution which dates back from 1990 to present. 82 Findings from the analysis BE)I) Time series to show the investments made in transportation and distribution. (ONES. GOB. UK) This time series illustrates a positive correlation between the investment in the transportation and distribution up until 2010. The time series shows a steady rise ever the years in the investments, however it is evident that we see a big downfall in 2011 and 2012.

This could be due to economic factors such as recession. In this years, the I-J had suffered a double dip in recession. (BBC online source) Moreover, I there’s less money available, then the investments would not be as significant because the industry would be down. However, we see a slight growth in the investments, this could be due to a recovery state in the economy. Conclusions From the results obtained, the box plots show a negative skew in which the variables in sector 63 are greater than those of sector 22.

Also by comparing the maximum and minimums, sector 63 generates a bigger turnover figure than sector 22. Based on the postbox and the time series, there is a positive relationship between them. This is because as more investments are made, there is more chance of potential turnovers. However this conclusion could be strengthened by analyzing turnover figures in the past years as if investment are low, then transport and distribution companies cannot utilities this finance to generate turnover. The improvements that I would make in the future may be to find a stronger time series that may show a clearer relation.