The following data give the area (in square feet) and the sale prices (approximated to the nearest $1000) of homes that were sold in a particular city in a 6-week period of 2003.
|
Area: 1123
|
1028
|
1490
|
2172
|
2300
|
1992
|
3200
|
3063
|
3720
|
|
7228
|
720
|
943
|
904
|
912
|
1031
|
1152
|
1482
|
1426
|
|
1491
|
1184
|
1650
|
1392
|
1755
|
2062
|
2495
|
3253
|
5152
|
|
1270
|
1723
|
1161
|
1220
|
837
|
1446
|
2442
|
2300
|
2518
|
|
Price: 75
|
75
|
102
|
149
|
152
|
154
|
327
|
425
|
625
|
|
775
|
53
|
57
|
66
|
68
|
75
|
86
|
90
|
93
|
|
95
|
95
|
104
|
105
|
135
|
159
|
169
|
253
|
725
|
|
67
|
85
|
110
|
65
|
74
|
95
|
156
|
183
|
207
|
(a) Obtain a dotplot and describe the home price data.
(b) Identify any outliers and test for normality with and without outliers for home price data. If the data are not normal, does any simple transformation make the data normal?
(c) Obtain a 95% con?dence interval for home price.
(d) Do parametric or nonparametric methods seem more appropriate for the data?
(e) Obtain a scatterplot between the square-foot area of a home and its price.
(f) Fit a least-squares regression line and run a residual model diagnostics using Minitab.