In this course project, we have been provided 5 different variables with 50 samples. They are “location”, a qualitative variable, and 4 quantitative variables, ”income”, “size”, “year” and “credit balance”. Each of them has a kind of relation that can link together, and because of that we can be able to pair out 10 different pairs of variables. In the following report, we are going to discuss 3 set of individual variable and 3 pair of variable by using statistic information.

The 1st individual variable is the “Location” and because it is a qualitative variable by which we may not shown the result by using the five numerical summaries. However, we can still be able to notice that there are 13 out of 50, about 26%, who located at rural, and 15 of those 50 (about 30%) are located at suburban, lastly with the rest of 22 (44%) are located in the urban area. There date can be shown in the pie chart below.

The 2nd individual variable is the “Income” with the unit of thousand. It is a quantitative variable which contained the 50 samples. Among those 50 samples, we can generate the five numerical summaries with the minimum of 25 and maximum of 74. In between, the median among those 50 sample is 44.5, with Q1 is 33 and Q3 is 57.25. Also we noticed that the mean of 46.02 and the standard deviation is 13.88. From the dot plot below, we understand there are more people with the income that is lower than the median number.

“Size” will be the 3rd individual variable to be mentioned. It is also a qualitative variable as we observe from the five numerical summaries with the minimum of 1; the median is 4.5 and maximum of 8 among 50 samples. In addition that Q1 is 2 and Q3 is 7. Also we can calculate the mean of the variable is 4.5 and the standard deviation is 2.525. As we can see from the histogram below, the pattern form by the data is opposite from the normal standard deviation.

Let’s begin to discuss the 1st pair of variables, the relation between “Income($1,000)” by “location”. From the graph below, we can compute each relation as follow: under the location in rural, the mean is 37.54, the standard deviation is 8.069 with the minimum of 25; the median is 36 and maximum of 53 as well as Q1 is 31.5 and Q3 is 43. Underneath Suburban, the “income” variable can be intended as the mean is 47.27, he standard deviation is 15.15 with the minimum of 25; the median is 36 and maximum of 69 as well as Q1 is 32 and Q3 is 64. As for Urban, the result as the minimum of 27; the median is 54 and maximum of 74; as Q1 is 38.75 and Q3 is 61, as well with the mean is 50.18, he standard deviation is 13.99. As a result, we aware that people who located in urban have a higher income earning. However the pattern which more fit with the normal standard deviation is the suburban part.

The 2nd pair of variables is by credit balance versus Income ($1,000). By using the scatter plots graph, we