Analyzing QUAN Data

Introduction

The idea of this essay is to represent the author’s ability to use statistical tools during the process of data analysis. The variety of statistical methods, which the author applies in this work, includes Descriptive Statistics, Pivot Table, Frequency Tables, and Charts tools. The data, which the author had selected for the paper, may not be associated within the meaning since he has pursued the goal to show the possibility of using statistical methods in information processing.

 
 

Descriptive Analysis

The purpose of the descriptive statistics method is to provide the researcher with tools, which will allow him to describe, show or summarize the data in a meaningful way. Since the data itself may provide a low amount of useful information, the descriptive analysis improves its performance without attracting additional sources of information. At the same time, this method of analysis does not allow the scholars to make clear conclusions beyond the data. That is why a comprehensive study of the processes on the basis of this method is highly unreliable. The descriptive statistics method is also useful at the study of large data sets. In this way, the described method of analysis provides an opportunity for the scholars to concentrate on the general trends and distribution of large amount of data, freeing them from having a long-term consideration of the unnecessary information. Admittedly, there are two types of statistical measures that the researchers resort to describe the data: measures of central tendency and measures of spread. These facilities can be valuable for the description of the central position of a frequency distribution for a group of data, and to summarize a massive of data by describing the indicators’ distribution.

As for the example of the descriptive analysis usage, the author presents the analysis of three tables with statistical indicators. Original Table 1 “Current price GDP per hour worked” (Office for National Statistics, 2016a) includes dynamic indices of labor productivity in the G7 countries, with respect to the UK index. For this case, the author used the measures of central tendency, which provided him with the information about the average productivity for different countries in comparison with the UK. Such approach to the data analysis allows making a general assumption about the level of economic development of these countries comparing to the UK.

Original Table 2 “Non-UK nationals working in the UK” (Office for National Statistics, 2016b, p. 14) provides quarterly data about the level of migrant workers in the country with an indication of their belonging to the citizenship of the European Union. It is an example of the large-scale data set, from which the scholar can only get a general idea of the change in the number of such workers within the country. To achieve more meaningful information, the author provides the average estimate of the variables for each year and uses the measures of central tendency. Furthermore, to understand the real tendency of the researched process, he also defines the selected variables’ annual growth rate. S a result, it allows determining the growth rate in the number of migrant workers in the country.   In this situation the measures of central tendency also provide more information about the tendency of the process. At the last stage of the analysis, the author estimates the share of each group of migrants in the general value. Such calculations give an opportunity for the author to trace the change in the ratio of migrant workers from EU and other countries, which is an extremely important indicator.

For the Original Table 3 “Value of UK e-commerce sales, by industry sector” (Office for National Statistics, 2015, p. 5) the author also applies the measures of central tendency to evaluate the average indicators of the investments’ level per industry and yearc.

Pivot Table

The pivot table is a significantly important data analysis tool, which permits the researcher to extract the required information from a large scale of data. For this essay, the author presents two pivot tables for different original tables. Each of them describes the various opportunities, which the pivot table tool provides.

The first pivot table uses the data from the original table 3. Here the application of the “industry” variable as a report filter allows displaying the information about the dynamics of investment in the required sectors of the economy.

The second pivot table imports the data from the original table 4 (Office for National Statistics, 2015, p. 6). Here the author implements the ability to search different indeces on columns of the table using the settings in the “Values” field of the pivot table.

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Frequency Table

The frequency tables are used to present the amount of times the data value occurs in the data massive. This tool is especially useful and reliable in the study of massive volumes of information. As soon as this table shows the incidence of the individual values of some indicators, it may be helpful during the formation of a common understanding of the process under the study. At the same time, this method of analysis simplifies the study of the current data set but does not give a general presentation on the mentioned issue.

For this paper, the author represents two frequency tables, which describe the opportunities of this analysis method usage. For the first table, the author imports the data from the original table 1. During the formation of variable intervals the researcher took into account the minimum and maximum values of the variable for all of the studied countries. Thus, the minimum value 76 is common for Japan, and the maximum 136 is the level of Germany. The intervals partition also included the requirement for the end of one of the intervals on the value of 100. This condition provided a possibility to assess the degree of countries lagging behind in comparison to the UK. For example, since for Canada and Japan most of the values are in the range up to 100, it is possible to talk about a lower productivity in these countries as compared to the UK. Thus, one can assume that the higher ranges of the studied variable frequency are, the higher is the productivity in the country, as compared to the UK.

For the second frequency table, the data from the original table 2 is used. Here the author applies individual intervals for two groups of variables, according to their distribution.

Charts

The chart is an effective method of a big data analysis. It provides the compact representation of data graphically. Opportunities provided by the use of charts include a brief introduction to a large volume of data. Since the chart is a graphic description of the statistic, it requires a low amount of time to familiarize with statistical information. For this essay, the author presents six charts, which refer to the various spreadsheets.

In chart 1, he uses a histogram to depict the data from the descriptive chart of the original table 1. Here the researcher can see the visual representation of differences in the level of labor productivity among leading countries. Such way of data presentation allows determining the backward countries by the level of labor productivity, as well as leading countries, immediately. The main advantages of this data representation method are the minimization of the time to become familiar with the information and making conclusions.

Chart 2 is also an example of the descriptive analysis, associated with the original table 2. It gives the viewer an idea of the dynamics of change in the ratio of different groups of migrant workers in the UK. So, the researcher can immediately see how the past 20 years have radically changed the structure of labor migration in the country.

Charts 3 and 4 serve to compare the sales structure in the field of e-commerce in the UK in 2009 and 2014. The usage of pie chart allows the experts to immediately determine whether the volume of e-commerce has grown significantly over the last five years, as it is impossible to talk about significant changes in the electronic trade structure.

Chart 5 is a graphic description of the first frequency table, which is connected with the original table 1. This chart describes in detail how to correlate the level of productivity in various countries in comparison with the UK. The more columns of the single country are presented in the left side of the drawing, the lower the level of productivity in this state is.

Chart 6 is a radar – one of the most interesting and least used. In this essay, the author uses it to report the data from the original table 4. It shows the level of contribution to the development of e-commerce in the country by business representatives from different countries. The larger the area of the region is, the higher is the contribution of a particular group in the development of e-business in the UK. At the same time, this graph allows estimating the contribution to commerce in various categories of production.

Conclusion

To conclude, the descriptive analysis provides more meaningful information about the analysed process and provides the researcher with more useful data. At the same time, it does not give an opportunity to make clear conclusions. The pivot table, however, may not contain qualitatively new information, but it includes the means for an effective processing and presentation of the data available. thus, the example of frequency tables’ usage in this essay shows that this tool of statistical analysis can open the new research facility. Moreover, the charts are also an efficient tool for the selected data presentation, which allows researchers to assimilate information quickly. The examples of such effect are the third and fourth charts.

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