Insurance is an integral part of peoples' life. It is a flourishing industry from a business point of view. This industry is mainly divided into two sub sectors namely life insurance and general insurance. Life insurance companies are keenly interested in predicting how long their customers will live, because their premium and profitability depends on such numbers. An actuary for one insurance company gathered data from 100 recently deceased clients. He recorded the age at death, ages at death of their mothers and fathers, number of years of employment and gender. You are required to gather 80 observations from the given data set

Observation No	Age at death	Mother's age at death	Father's age at death	No of years of employment	Gender
16	81	82	77	30	Female
17	66	80	63	35	Female
18	68	72	71	33	Female
19	72	71	66	30	Male
20	71	76	61	35	Female
21	62	65	57	36	Female
22	71	71	71	41	Female
23	67	68	68	25	Female
24	67	69	66	37	Female
25	79	88	73	20	Male
26	67	73	62	30	Male
27	71	76	67	25	Male
28	73	79	71	40	Male
29	72	66	75	40	Male
30	74	78	68	25	Female
31	74	82	66	20	Male
32	89	86	77	20	Male
33	83	90	72	20	Female
34	78	82	72	33	Male
35	71	78	64	40	Female
36	73	69	80	35	Male
37	74	79	71	35	Female
38	71	73	72	41	Female
39	77	72	72	20	Male
40	77	79	74	25	Male
41	74	77	64	15	Male
42	72	78	64	33	Male
43	77	85	77	18	Male
44	66	74	64	38	Female
45	70	67	65	38	Female
46	63	71	56	38	Female
47	82	83	78	25	Female
48	66	64	66	36	Male
49	74	76	66	35	Male
50	74	82	71	41	Male
51	77	85	69	31	Male
52	69	72	63	32	Female
53	72	77	69	40	Male
54	67	68	69	38	Female
55	74	71	76	45	Male
56	67	76	62	25	Male
57	68	74	62	30	Female
58	68	74	79	30	Female
59	71	71	60	28	Male
60	84	80	90	15	Male
61	78	82	70	40	Male
62	76	82	65	22	Female
63	68	71	61	35	Female
64	70	66	75	36	Female
65	77	82	76	30	Male
66	73	67	71	25	Male
67	72	78	70	30	Male
68	77	75	66	25	Male
69	75	73	69	30	Female
70	71	77	70	30	Male
71	70	77	60	41	Female
72	65	75	60	25	Female
73	68	72	66	31	Female
74	71	70	70	43	Female
75	68	75	63	32	Female
76	59	59	62	35	Female
77	83	82	82	30	Male
78	66	69	68	38	Male
79	76	80	70	31	Male
80	65	66	63	33	Male
81	70	66	68	25	Female
82	76	88	69	30	Male
83	74	79	70	25	Male
84	73	80	75	41	Male
85	75	76	72	25	Male
86	70	75	57	38	Female
87	80	84	75	18	Male
88	72	72	77	35	Male
89	80	74	77	20	Male
90	72	85	60	25	Male
91	72	73	69	33	Male
92	64	71	67	30	Female
93	73	88	63	30	Male
94	80	85	78	30	Male
95	72	78	64	30	Male

Construct suitable graphs to represent the relationship between dependent variables and each quantitative independent variable in your data set.