Age Gender Housing Saving accounts Checking account Credit amount Duration
1 67 male own <NA> little 1169 6
2 22 female own little moderate 5951 48
3 49 male own little <NA> 2096 12
4 45 male free little little 7882 42
5 53 male free little little 4870 24
6 35 male free <NA> <NA> 9055 36
7 53 male own quite rich <NA> 2835 24
8 35 male rent little moderate 6948 36
9 61 male own rich <NA> 3059 12
10 28 male own little moderate 5234 30
Purpose Class Risk
1 radio/TV 1
2 radio/TV 2
3 education 1
4 furniture/equipment 1
5 car 2
6 education 1
7 furniture/equipment 1
8 car 1
9 radio/TV 1
10 car 2Lab 3.1
Lincoln Morphis
Summary Statistics: First 10 Rows
Summary Statistics: Last 10 Rows
Age Gender Housing Saving accounts Checking account Credit amount Duration
991 37 male own <NA> <NA> 3565 12
992 34 male own moderate <NA> 1569 15
993 23 male rent <NA> little 1936 18
994 30 male own little little 3959 36
995 50 male own <NA> <NA> 2390 12
996 31 female own little <NA> 1736 12
997 40 male own little little 3857 30
998 38 male own little <NA> 804 12
999 23 male free little little 1845 45
1000 27 male own moderate moderate 4576 45
Purpose Class Risk
991 education 1
992 radio/TV 1
993 radio/TV 1
994 furniture/equipment 1
995 car 1
996 furniture/equipment 1
997 car 1
998 radio/TV 1
999 radio/TV 2
1000 car 1Summary Statistics: Structure of Data Set
'data.frame': 1000 obs. of 9 variables:
$ Age : num 67 22 49 45 53 35 53 35 61 28 ...
$ Gender : chr "male" "female" "male" "male" ...
$ Housing : chr "own" "own" "own" "free" ...
$ Saving accounts : chr NA "little" "little" "little" ...
$ Checking account: chr "little" "moderate" NA "little" ...
$ Credit amount : num 1169 5951 2096 7882 4870 ...
$ Duration : num 6 48 12 42 24 36 24 36 12 30 ...
$ Purpose : chr "radio/TV" "radio/TV" "education" "furniture/equipment" ...
$ Class Risk : num 1 2 1 1 2 1 1 1 1 2 ...Summary Statistics
Credit amount Age
Min. : 250 Min. :19.00
1st Qu.: 1366 1st Qu.:27.00
Median : 2320 Median :33.00
Mean : 3271 Mean :35.55
3rd Qu.: 3972 3rd Qu.:42.00
Max. :18424 Max. :75.00 
Credit amount Age
Credit amount 1.00000000 0.03271642
Age 0.03271642 1.00000000Create a table for categorical variable: Class Risk
1 2
700 300 Scatterplot for X and Y variable
Interpretation and Findings
From our scatter plot we can infer that those who are Class Risk 2, have a higher average credit amount than those who are Class Risk 1, and they tend to be younger. Those who are class risk two tend to increase in credit amount as their age increases as well. For those who are in class risk 1, their age remains constant with credit amount.
Improvements Made to Chart
After reviews from my peers I added more separate pages for each of my summary statistics to better highlight those. I clarified my categorical variable in my “Create a table for categorical variable” page for further clarification.
Final
End of Report.