91影视

The Nilson Report dealt with the outstanding debts of the credit card issuers. Outstanding debts always remain numerical, so it has to be quantitative instead of qualitative.

The calculations of the percentage of the credit card issuers in the specified regions are shown below:

$$\begin{gathered} \mathit{P}\mathit{e}\mathit{r}\mathit{c}\mathit{e}\mathit{n}\mathit{t}\mathit{a}\mathit{g}\mathit{e}\mathbf{}\mathbf{=}\frac{\mathbf{\text{Number}}\mathbf{鈥�}\mathbf{\text{of issuers in a region}}}{\mathbf{T}\mathbf{o}\mathbf{t}\mathbf{a}\mathbf{l}\mathbf{}\mathbf{n}\mathbf{u}\mathbf{m}\mathbf{b}\mathbf{e}\mathbf{r}\mathbf{}\mathbf{o}\mathbf{f}\mathbf{}\mathbf{i}\mathbf{s}\mathbf{s}\mathbf{u}\mathbf{e}\mathbf{r}\mathbf{s}}\mathbf{脳}\mathbf{100} \\ \mathit{F}\mathit{o}\mathit{r}\mathbf{}\mathit{A}\mathit{s}\mathit{i}\mathit{a}\mathbf{}\mathbf{-}\mathbf{}\mathit{P}\mathit{a}\mathit{c}\mathit{i}\mathit{f}\mathit{i}\mathit{c}\mathbf{}\mathit{r}\mathit{e}\mathit{g}\mathit{i}\mathit{o}\mathit{n}\mathbf{}\mathbf{=}\frac{\mathbf{48}}{\mathbf{150}}\mathbf{脳}\mathbf{100}\mathbf{=}\mathbf{}\mathbf{32}\mathbf{.}\mathbf{67}\mathbf{\%} \\ \mathit{F}\mathit{o}\mathit{r}\mathbf{}\mathit{C}\mathit{a}\mathit{n}\mathit{a}\mathit{d}\mathit{a}\mathbf{}\mathbf{=}\frac{\mathbf{10}}{\mathbf{150}}\mathbf{脳}\mathbf{100}\mathbf{=}\mathbf{}\mathbf{6}\mathbf{.}\mathbf{67}\mathbf{\%}\mathbf{} \\ \mathit{F}\mathit{o}\mathit{r}\mathbf{}\mathit{E}\mathit{u}\mathit{r}\mathit{o}\mathit{p}\mathit{e}\mathbf{}\mathbf{=}\mathbf{}\frac{\mathbf{34}}{\mathbf{150}}\mathbf{脳}\mathbf{100}\mathbf{=}\mathbf{}\mathbf{22}\mathbf{.}\mathbf{67}\mathbf{\%} \\ \mathit{F}\mathit{o}\mathit{r}\mathbf{}\mathit{L}\mathit{a}\mathit{t}\mathit{i}\mathit{n}\mathbf{}\mathit{A}\mathit{m}\mathit{e}\mathit{r}\mathit{i}\mathit{c}\mathit{a}\mathbf{}\mathbf{=}\frac{\mathbf{29}}{\mathbf{150}}\mathbf{脳}\mathbf{100}\mathbf{=}\mathbf{}\mathbf{19}\mathbf{.}\mathbf{33}\mathbf{\%} \\ \mathit{F}\mathit{o}\mathit{r}\mathbf{}\mathit{M}\mathit{i}\mathit{d}\mathit{d}\mathit{l}\mathit{e}\mathbf{}\mathit{E}\mathit{a}\mathit{s}\mathit{t}\mathbf{/}\mathit{A}\mathit{s}\mathit{i}\mathit{a}\mathbf{}\mathbf{=}\frac{\mathbf{3}}{\mathbf{150}}\mathbf{脳}\mathbf{100}\mathbf{=}\mathbf{}\mathbf{2}\mathbf{\%} \\ \mathit{F}\mathit{o}\mathit{r}\mathbf{}\mathit{U}\mathit{n}\mathit{i}\mathit{t}\mathit{e}\mathit{d}\mathbf{}\mathit{S}\mathit{t}\mathit{a}\mathit{t}\mathit{e}\mathit{s}\mathbf{}\mathbf{=}\frac{\mathbf{26}}{\mathbf{150}}\mathbf{脳}\mathbf{100}\mathbf{=}\mathbf{17}\mathbf{.}\mathbf{33}\mathbf{\%} \end{gathered}$$

The bar graph has been formed with the help of the percentages calculated in Step 2. The vertical axis represents the relative frequencies (in percentages), and in the horizontal axis, the regions are plotted.

From the bar graph, it can be deduced that the Asia-Pacific region has the highest number of credit card issuers because the bar of that region is the tallest. On the other hand, the bar of the Middle East/Africa is the shortest, which indicates that the sample contains the least number of credit card issuers from this region.