An agent for a residential real estate company in a suburb located outside of Washington, DC, has the business objective of developing more accurate estimates of the monthly rental cost for the apartments. Toward that goal, the agent would like to use the size of an apartment, as defined by square footage to predict the monthly rental cost. The agent selects a sample of 48 one-bedroom apartments and collects and stores the data in Q1 sheet.
a. Construct a scatterplot. What does it show?
b. Write down the linear regression model.
c. Interpret the meaning of b0 and b1.
d. Predict the mean monthly rent for an apartment that has 800 square feet.
e. Why would it not be appropriate to use the model to predict the monthly rent for apartments that have 1500 square feet?
f. Determine the coefficient of determination (r2) and interpret its meaning.
g. Your friends Jim and Jennifer are considering signing a lease for a one-bedroom apartment in this residential neighborhood. They are trying to decide between 2 apartments, one with 800 square feet for a monthly rent of $1130 and the other with 830 square feet for a monthly rent of $1410. Based on your answer from parts (a) to (d), which apartment do you think is a better deal?
Size (Square feet)
Rent ($)
524
1110
616
1175
666
1190
830
1410
450
1210
550
1225
780
1480
815
1490
1070
1495
610
1680
835
1810
660
1625
590
1469
675
1395
744
1150
820
1140
912
1220
628
1434
645
1519
840
1105
800
1130
804
1250
950
1449
800
1168
787
1224
960
1391
750
1145
690
1093
840
1353
850
1530
965
1650
1060
1740
665
1235
775
1550
960
1545
827
1583
655
1575
535
1310
625
1195
749
1200
634
1185
641
1444
860
1385
740
1275
593
1050
880
1650
895
1340
692
1560
Age
Income (1000s)
Cards have
Response
Felix
ED
Age
Income
Cards
Response
45
98
3
?
66
134
6
0
34
177
4
0
18
191
6
0
60
169
1
0
53
104
1
1
35
79
9
0
29
115
5
1
75
115
10
0
50
141
1
0
75
102
10
0
49
63
10
0
49
186
7
1
23
135
3
1
19
22
7
1
69
165
4
0
64
36
1
1
40
122
6
1
27
27
7
1
59
122
5
1
45
140
8
1
43
120
5
0
28
77
1
0
38
57
10
0
64
98
4
0
51
83
9
0
25
31
9
0
51
131
2
0
50
49
6
0
74
175
8
1
55
14
0
0
50
37
9
1
42
22
4
1
47
113
4
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