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The importance of statistical analysis in modern world can hardly be overestimated: it allows tracing connections between various factors and data that influence business processes, making predictions for valuable economic indicators etc. For global companies, the analysis of statistical data may serve as the basis for marketing and production decisions.
I have selected for research the statistics of cell phone usage and labor/unemployment statistics. For companies operating in cell phone industry (cell operators, cell phone producers, companies offering additional services) the analysis of potential and actual outlet is the most important indicator for development strategy. The solvent part of customers is usually the main source of income for companies operating in cell phone sphere. Thus, the factors influencing the amount of cell phones in different countries, and in particular the relation between the employed (and unemployed) number of able-bodied citizens and the number of cell phones is of great interests for marketing departments of the companies.
Problem definition: to find out whether there is a significant relation between the level of employment/unemployment and the amount of cell phones in the country. The suggestion is that the higher the level of unemployment is, the lower is the amount of cell phones in the country (inverse relation). The outcomes of such analysis may be used for various purposes, for example:
1) if the company wants to expand the market, it may use the statistics of unemployment for choosing the best place for new development;
2) if the company wants to correct its marketing strategy within a country, it may use data showing unemployment dynamics for adjusting the sales and advertising strategy;
3) the information concerning unemployment and the total number of cell phones may also help the companies to choose the right pricing strategy and right products for each country.
The dataset contains 3 columns to be analyzed: cell phone usage per country, labor force and percent of unemployment. Operational definitions are the following: labor force is measured as the amount of nonmilitary people who are officially employed or unemployed. Unemployed (for the measurement date) are considered those who are over 16 years old, were neither “at work” nor “with a job but not at work” during the reference week, were looking for work during the last four weeks, and were available to start a job (Cloqq & Eliason & Leicht, 2001). Those who expect to be recalled to work and are on temporary layoff are also considered unemployed. Cell phone usage is the amount of cell phones registered within the country by the measurement date.
Cell phone usage is expressed in millions of units, unemployment is given as percentage (compared to labor force) and labor force is expressed in millions of citizens. Thus, all measures are quantitative (Myers & Well, 2003). In order to deal with comparable variables, it is necessary to count percentage of cell phone use (compared to labor force); it is calculated by dividing the cell phone usage number by labor force number and multiplying by 100. Thus, in fact there are two actual variables in the research: the unemployment percent and cell phone use percent.
The results of the comparison of two variables are presented in Table 1:

Country Labor force Cell phones % cell. Unemployment Correlation:
Argentina 15 3 20,00 15,00 -0,178
Bolivia 4,2 1,401 33,36 8,00
Brazil 79 4,4 5,57 7,10 Linear:
Chile 3,6 6,45 179,17 7,40 -1,37 72,78
Columbia 20,5 6,189 30,19 11,80 1,02 12,35
Ecuador 4,6 2,394 52,04 11,20 0,03 53,09
French Guiana 0,059 0,138 233,90 22,00 1,79 55,00
Guyana 0,418 0,087 20,81 9,10 5051,92 155011,72
Paraguay 2,7 1,77 65,56 16,00
Peru 9,1 2,908 31,96 8,70
Suriname 0,104 0,168 161,54 17,00
Uruguay 1,5 0,652 43,47 12,00
Venezuela 9,9 2 20,20 14,00
Algeria 9,1 0,034 0,37 30,00
Australia 9,5 6,4 67,37 6,40
Austria 3,7 4,5 121,62 5,40
Belgium 4,34 1 23,04 8,40
Canada 16,1 4,2 26,09 6,80
China 700 65 9,29 10,00
Cyprus 0,37 0,418 112,97 3,50
Czech Republic 5,2 4,3 82,69 8,70
Denmark 2,9 1,4 48,28 5,30
Estonia 0,67 0,881 131,49 9,20
Finland 2,6 2,2 84,62 9,80
France 25 11,1 44,40 9,70
Germany 40,5 15,3 37,78 9,90
Greece 4,32 0,937 21,69 11,30
Hungary 4,2 1,3 30,95 9,40
Iceland 0,16 0,066 41,25 2,70
Indonesia 99 1 1,01 17,50
Iran 17,3 0,265 1,53 14,00
Ireland 1,82 2 109,89 4,10
Italy 23,4 20,5 87,61 10,40
Japan 67,7 63,9 94,39 4,70
Kuwait 1,3 0,21 16,15 1,80
Latvia 11,11 1,219 10,97 8,80
Libya 1,5 0 0,00 30,00
Lithuania 1,61 2,17 134,78 5,30
Luxembourg 0,248 0,215 86,69 2,70
Mexico 39,8 2 5,03 2,20
Netherlands 7,2 4,1 56,94 2,60
New Zealand 1,88 0,6 31,91 6,30
Nigeria 66 0,027 0,04 28,00
Norway 2,4 2 83,33 3,00
Poland 17,2 1,8 10,47 12,00
Portugal 5 3 60,00 4,30
Russia 66 2,5 3,79 10,50
Slovakia 2,62 3,68 140,46 11,50
Slovenia 0,92 1,74 189,13 9,80
South Africa 17 2 11,76 30,00
South Korea 22 27 122,73 4,10
Spain 17 8,4 49,41 14,00
Sweden 4,4 3,8 86,36 6,00
Switzerland 3,9 2 51,28 1,90
Turkey 23 12,1 52,61 5,60
United Kingdom 29,2 13 44,52 5,50
United States 140,9 69 48,97 4,00
Table 1. Correlation of cell phone use percentage and unemployment

The analysis has been done using correlation analysis (in MS Excel) and additional statistics given by “linear” function (r-coefficient and F-value in particular). The value of correlation coefficient is -0,178. Thus, there is no valuable relation between unemployment rate and cell phone usage, though the negative value of the coefficient shows that slightly inverse tendency of cell phone use and unemployment takes place. Moreover, the r-coefficient is 0,03, very close to zero – it is possible to state that there is no relation between the analyzed variables (Freedman & Pisani & Purves, 2007). Low value of F-statistics also proves this conclusion. Therefore, the initial suggestion about inverse relation between the variables is not true (McClave & Sinsich & Mendenhall).
The outcomes from the performed analysis are the following: the companies operating in cell phone industry should not regard unemployment in the country as the factor which may influence call phone usage, and should not consider information related to unemployment issues when making strategic business decisions.
Sources
Cloqq, Clifford S. & Eliason, Scott R. & Leicht, Kevin T. (2001). Analyzing the Labor Force: Concepts, Measures, and Trends. Springer
Freedman, D. & Pisani, R. & Purves, R. (2007). Statistics. W.W. Norton & Co.
McClave, James & Sinsich, Terry & Mendenhall, William. (2008). Statistics. Prentice Hall.
Myers, Jerome L & Well, Arnold. (2003). Research design and statistical analysis. Routledge.