Sales of single-family houses have been brisk in Mid City this year. This has especially beentrue in older, more established neighborhoods, where housing is relatively inexpensive comparedto the new homes being built in the newer neighborhoods. Nevertheless, there are alsomany families who are willing to pay a higher price for the prestige of living in one of the newer neighborhoods. For each sale, the file shows the neighborhood(1, 2, or 3) in which the house is located, the number of offers made on the house,the square footage, whether the house is made primarily of brick, the number of bathrooms,the number of bedrooms, and the selling price. Neighborhoods 1 and 2 are more traditionalneighborhoods, whereas neighborhood 3 is a newer. more prestigious neighborhood.YOUR TASK: Using the various techniques learned in the class, develop and estimate a regressionmodel that analyzes the factors contributing to the pricing structure of these housesales in Mid City. Write a professional report of your analysis that should also include answersto the following considerations. Your report should clearly outline what your model istrying to capture, what your independent and dependent variables are, why you picked thoseindependent variables-what is the rationale behind (in 1-2 sentences for each independentvariable). Comment on and correct the pre-estimation issues as much as you can: check forregression assumptions, normality, correlations, outliers, heteroscedasticity, multicollinearity,nonlinearity, etc. Interpret the estimated coefficients, comment on the overall fit of yourmodel, etc. Some considerations your report should include:a. Do buyers pay a premium for a brick house, all else being equal?b. Is there a premium for a house in neighborhood 3, all else being equal?c. Is there an extra premium for a brick house in neighborhood 3, in addition to the usualpremium for a brick house?d. For purposes of estimation and prediction, could neighborhoods 1 and 2 be collapsedinto a single "older" neighborhood?DELIVERABLES: (i) Excel File containing the regression model or models and showing thesolutions for the questions posed above, (ii) professionally written (typed) report not to exceed5 pages (double spaced, 12 pt, including images, tables, references etc.)
|
60
|
3
|
1
|
2090
|
No
|
4
|
2
|
155400
|
|
61
|
3
|
1
|
2200
|
No
|
3
|
3
|
180900
|
|
62
|
1
|
2
|
1610
|
No
|
2
|
2
|
100900
|
|
63
|
3
|
2
|
2220
|
No
|
4
|
3
|
161300
|
|
64
|
2
|
2
|
1910
|
No
|
2
|
3
|
120500
|
|
65
|
3
|
2
|
1860
|
No
|
3
|
2
|
130300
|
|
66
|
1
|
1
|
1450
|
Yes
|
2
|
2
|
111100
|
|
67
|
1
|
4
|
2210
|
No
|
3
|
3
|
126200
|
|
68
|
2
|
3
|
2040
|
No
|
4
|
3
|
151900
|
|
69
|
1
|
4
|
2140
|
No
|
3
|
2
|
93600
|
|
70
|
3
|
3
|
2080
|
No
|
4
|
3
|
165600
|
|
71
|
3
|
3
|
1950
|
Yes
|
3
|
3
|
166700
|
|
72
|
3
|
1
|
2160
|
No
|
4
|
2
|
157600
|
|
73
|
1
|
3
|
1650
|
No
|
3
|
2
|
107300
|
|
74
|
2
|
2
|
2040
|
No
|
3
|
3
|
125700
|
|
75
|
3
|
3
|
2140
|
No
|
3
|
3
|
144200
|
|
76
|
1
|
2
|
1900
|
No
|
2
|
2
|
106900
|
|
77
|
3
|
2
|
1930
|
No
|
3
|
2
|
129800
|
|
78
|
3
|
3
|
2280
|
Yes
|
4
|
3
|
176500
|
|
79
|
1
|
3
|
2130
|
No
|
3
|
2
|
121300
|
|
80
|
3
|
1
|
1780
|
No
|
4
|
2
|
143600
|
|
81
|
2
|
4
|
2190
|
Yes
|
3
|
3
|
143400
|
|
82
|
3
|
2
|
2140
|
Yes
|
4
|
3
|
184300
|
|
83
|
3
|
1
|
2050
|
Yes
|
2
|
2
|
164800
|
|
84
|
2
|
2
|
2410
|
No
|
3
|
3
|
147700
|
|
85
|
1
|
3
|
1520
|
No
|
2
|
2
|
90500
|
|
86
|
3
|
2
|
2250
|
Yes
|
4
|
3
|
188300
|
|
87
|
1
|
4
|
1900
|
No
|
4
|
2
|
102700
|
|
88
|
3
|
1
|
1880
|
Yes
|
3
|
3
|
172500
|
|
89
|
1
|
2
|
1930
|
No
|
3
|
3
|
127700
|
|
90
|
1
|
4
|
2010
|
No
|
2
|
2
|
97800
|
|
91
|
3
|
2
|
1920
|
No
|
4
|
2
|
143100
|
|
92
|
2
|
2
|
2150
|
No
|
3
|
2
|
116500
|
|
93
|
3
|
2
|
2110
|
No
|
3
|
2
|
142600
|
|
94
|
2
|
2
|
2080
|
No
|
3
|
3
|
157100
|
|
95
|
3
|
3
|
2150
|
Yes
|
4
|
3
|
160600
|
|
96
|
3
|
1
|
1970
|
Yes
|
2
|
2
|
152500
|
|
97
|
2
|
3
|
2440
|
No
|
3
|
3
|
133300
|
|
98
|
2
|
1
|
2000
|
Yes
|
2
|
2
|
126800
|
|
99
|
3
|
1
|
2060
|
No
|
3
|
2
|
145500
|
|
100
|
3
|
2
|
2080
|
Yes
|
3
|
3
|
171000
|
|
101
|
1
|
5
|
2010
|
No
|
3
|
2
|
103200
|
|
102
|
2
|
5
|
2260
|
No
|
3
|
3
|
123100
|
|
103
|
2
|
4
|
2410
|
No
|
3
|
3
|
136800
|
|
104
|
3
|
3
|
2440
|
Yes
|
4
|
3
|
211200
|
|
105
|
2
|
4
|
1910
|
No
|
3
|
2
|
82300
|
|
106
|
3
|
4
|
2530
|
No
|
4
|
3
|
146900
|
|
107
|
1
|
4
|
2130
|
No
|
3
|
2
|
108500
|
|
108
|
2
|
1
|
1890
|
Yes
|
3
|
2
|
134000
|
|
109
|
2
|
3
|
1990
|
Yes
|
3
|
3
|
117000
|
|
110
|
2
|
3
|
2110
|
No
|
3
|
2
|
108700
|
|
111
|
1
|
1
|
1710
|
No
|
2
|
2
|
111600
|
|
112
|
1
|
2
|
1740
|
No
|
2
|
2
|
114900
|
|
113
|
2
|
2
|
1940
|
Yes
|
2
|
2
|
123600
|
|
114
|
1
|
3
|
2000
|
Yes
|
3
|
2
|
115700
|
|
115
|
2
|
2
|
2010
|
No
|
4
|
3
|
124500
|
|
116
|
1
|
3
|
1900
|
No
|
3
|
3
|
102500
|
|
117
|
3
|
1
|
2290
|
Yes
|
5
|
4
|
199500
|
|
118
|
1
|
2
|
1920
|
No
|
3
|
2
|
117800
|
|
119
|
1
|
3
|
1950
|
Yes
|
3
|
2
|
150200
|
|
120
|
1
|
4
|
1920
|
No
|
2
|
2
|
109700
|
|
121
|
1
|
3
|
1930
|
No
|
2
|
3
|
110400
|
|
122
|
2
|
3
|
1930
|
No
|
3
|
3
|
105600
|
|
123
|
2
|
1
|
2060
|
Yes
|
2
|
2
|
144800
|
|
124
|
2
|
3
|
1900
|
Yes
|
3
|
3
|
119700
|
|
125
|
2
|
3
|
2160
|
Yes
|
4
|
3
|
147900
|
|
126
|
1
|
2
|
2070
|
No
|
2
|
2
|
113500
|
|
127
|
3
|
1
|
2020
|
No
|
3
|
3
|
149900
|
|
128
|
1
|
4
|
2250
|
No
|
3
|
3
|
124600
|