EXCEL Ch 11 Linear Relationship Start
xlsx
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School
Temple University *
*We aren’t endorsed by this school
Course
2103
Subject
Statistics
Date
Apr 3, 2024
Type
xlsx
Pages
55
Uploaded by nidhipatel200416
Project De
Instruction
Step
1
2
3
Project Desc
providing a using Excel’
Price - Reco
Yes = 1; 6) variables, Pr
Use a cell re
answers. Fo
4
5
6
7
8
9
10
11
12
13
14
15
Linear Relationship CG
escription: ns: Instructions
Start Excel. Download and open the workbook named:
Linear_Relationship_Start.
Inserting Chart
Select the Scatter chart from the provided chart options in the Charts group of the Insert tab of the Ribbon.
Selecting Data Series
Edit Chart Elements
Chart Size and Position
Add the trendline to the data on the chart.
Adding Linear Trendline
Select any point on the chart and right click on it. Select Add Trendline. In the Trendline Options window select Linear with a
Trendline Options
Use the regression model of Data Analysis for the data.
Adding Regression Model
cription: The present study investigates the relationship between living area as measured in square feet of a hom
scatterplot of the relationship where living area may be used to estimate selling price. We will add the ‘least sq
’s scatter graph option. The data we are using was collected from real estate values obtained in an upstate New
orded in dollars; 2) Lot size - measured in acres; 3) Living Area - measured in square feet; 4) Pct College - percen
Bedrooms - Number of bedrooms; 7) Fireplace - Number of fireplaces; 7) Bathrooms - Number of bathrooms. For
rice and Living Area.
eference or a single formula where appropriate in order to receive full credit. Do not copy and paste values or ty
or the purpose of grading the project you are required to perform the following tasks:
In cell C6
insert a Scatter Chart for the Price versus Living Area data from the Data
worksheet.
Then choose Select Data in the Design tab on the Ribbon. Delete any series created automatically using the Remove button
of data on the Data
worksheet. Note that Price should stand for the Y values and Living Area is the X values. Do not add the
Note:
Do not include the titles of the columns to the range of data.
Go to the Add Chart Elements dropdown list in the Design tab of the Ribbon. Delete the legend. Above the chart, add the titl
the chart title. On the Ribbon, select design Style 1.
Go to the Format tab on the Ribbon. Set the chart height and width, and then drag it to position the entire chart so that it fit
In theTrendline Options window check the “
Display equation on chart
”
and “
Display R-squared value on chart
”
boxes. You ca
to any place on the chart so that it is more visible to read.
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Save the workbook. Close the workbook and then exit Excel. Submit the workbook as directed.
Go to the Data
worksheet. Select the Data Analysis in the Analysis group of the Data tab of the Ribbon (note you should add
already). Select Regression and click OK. Select the cells in the Price column as the Input Y Range and the cells in the Living
Data
worksheet as the output range. Do not check any additional boxes in the regression model menu.
Note:
Do not include the titles of the columns to the range of data.
On the 14-21
worksheet, identify the value of the correlation coefficient, r, between living area and selling price to four deci
Interpret the value of the correlation coefficient, r, in terms of direction and strength of the linear model. Choose the correct
In cells E20
and G20
complete the least squares equation. Round the y-intercept to the nearest integer and the slope to two
Identify the value of the y-intercept, b
0
, in cell D22
. Keep the same number of decimal places as the value in the equation a
Interpret the y-intercept, b
0
, in terms of the data. Choose the correct answers from the drop-down menus in cells D24
and H
In cell D26
decide whether the interpretation of the y-intercept is a meaningful estimate in the context of this problem.
Identify the value of the slope, b
1
, in cell D28
. Keep the same number of decimal places as the value in the equation above.
Interpret the slope, b
1
, in terms of the data. Choose the correct answers from the drop-down menu in cell D30
. Round the am
Identify the value of R
2
in cell D32
. Round the percent to two decimal places.
Calculate the expected sale price of a 1500 square foot house in cell D37
using the value(s) found in step 6 for the least squ
dollar.
Calculate the expected price increase for an additional 200 square feet in cell D40 using the value(s) found in step 6 for the
nearest dollar.
Points Possible
0
2
2
4
1
2
1
1
1
1
1
2
1
1
0
20
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Proble
1-3)
4-5)
6-12)
The pr
are bei
Use a c
answer
13-15)
em Housing Data
What is the value of the correlation coefficient and what does this indicate about the r
The correlation between size of a house and selling price is
Larger houses will command
What is the least squares regression equation?
resent study investigates the relationship between living area as measured in square fe
ing transferred to this area and are interested in estimating the expected cost of housin
cell reference or a single formula where appropriate in an effort to receive full credit. rs.
Construct an (x,y) scatterplot to visually display the relationship using living area as t
instructions document. Place the graph in cell C6
.
Identify the value of the correlation coefficient, r, between living area and selling pric
Interpret the value of the correlation coefficient, r, in terms of direction and strength o
and D12
.
Given the scatter of the data appears to model a linear relationship, find the 'trendline
What is the value of b
0
and what does this mean in the context of this data?
What is the value of b
1
and what does this mean in the context of this data?
What is the value of R
2
written as a percentage.
0
1000
2000
3000
4000
5000
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
f(x) = 113.122541846809 x + 13439.3940048898
R² = 0.507499844681924
Predicted sales price,y, for given living area,x
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The predicted selling price for a house whose living area is The selling price of a house is expected to
Suppose that you are interested in buying a house that is 1500 square feet. Use the mo
What is the expected selling price for a 1500 square foot house?
Complete the least squares regression equation. For cells E20
and G20
, round the y-i
Identify the value of the y-intercept, b
0
, in cell D22
. Keep the same number of decima
Interpret the y-intercept, b
0
, in terms of the data. Choose the correct answers from the
Is this a meaningful estimate in the context of this problem? Choose the correct answ
Identify the value of the slope, b
1
, in cell D28
. Keep the same number of decimal plac
Interpret the slope, b
1
, in terms of the data. Choose the correct answers from the drop
Identify the value of R
2
in cell D32
. Round the percent to two decimal places.
Calculate the expected sale price of a 1500 square foot house in cell D37
using the va
Suppose you are looking at two houses in the same neighborhood that are about the s
living area. How much more would you expect to pay for the house with the finished
Calculate the expected price increase for an additional 200 square feet in cell D40 usi
to the nearest dollar.
relationship of living area and sale price?
0.7124
strong.
higher prices.
eet of a home and the sale price of the home in a region in upstate New York. Suppose you ng for a given size home.
Do not copy and paste values or type values, as you will not receive full credit for your the independent variable and sale price as the dependent variable. Follow steps 1-3 in the ce to four decimal places in cell D9
.
of the linear model. Choose the correct answers from the drop-down menus in cells
D11
e' and the R
2
value in Excel.
6000
predicted price =
13,439
+
113.12
(sq ft)
13,439
0 square feet
is estimated to be
$13,439.00.
No
113.12
increase
by about
$113.12
per square foot added to living area.
50.75%
odel to predict how much a house this size in upstate New York would cost.
$183,123
$205,748
intercept to the nearest integer and the slope to two decimal places.
al places as the value in the equation above.
e drop-down menus in cells D24
and H24
. wer from the drop-down menu in cell D26
.
ces as the value in the equation above.
p-down menu in cell D30
. Round the amount in cell G30
to the nearest cent.
alue(s) found in step 6 for the least squares regression equation. .
same size except one of the houses has a finished basement which adds 200 square feet of d basement?
ing the value(s) found in step 6 for the least squares regression equation. Round the value
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Price
Lot Size
Living Area
Pct College
Central Air
Bedrooms
Fireplaces
Bathrooms
132500
0.09
906
35
0
2
1
1
181115
0.92
1953
51
0
3
0
2.5
109000
0.19
1944
51
0
4
1
1
155000
0.41
1944
51
0
3
1
1.5
86060
0.11
840
51
1
2
0
1
120000
0.68
1152
22
0
4
1
1
153000
0.4
2752
51
0
4
1
1.5
170000
1.21
1662
35
0
4
1
1.5
90000
0.83
1632
51
0
3
0
1.5
122900
1.94
1416
44
0
3
0
1.5
325000
2.29
2894
51
0
7
0
1
120000
0.92
1624
51
0
3
0
2
85860
8.97
704
41
0
2
0
1
97000
0.11
1383
57
0
3
0
2
127000
0.14
1300
41
0
3
0
1.5
89900
0
936
57
0
3
0
1
155000
0.13
1300
41
0
3
0
1.5
253750
2
2816
71
1
4
1
2.5
60000
0.21
924
35
0
2
0
1
87500
0.88
1092
35
0
3
0
1
112000
1
1056
35
0
3
0
1
104900
0.43
1600
39
0
3
0
1.5
148635
0.32
1576
39
1
3
0
2.5
150000
0.03
2080
39
0
3
0
2
90400
0.36
1600
39
0
3
0
1.5
248800
4
2224
39
0
4
0
3
135000
1.83
1656
39
0
3
0
1
145000
3
1170
39
0
4
0
1.5
457000
0.43
2461
39
0
4
1
2
140000
0.44
1544
71
1
3
1
1.5
130000
1.24
1220
71
0
2
2
1
187000
0.46
1858
52
0
3
1
2.5
229000
0.87
2219
64
0
3
1
2
227000
1.8
1876
52
0
3
0
2.5
179900
0.46
2026
52
0
4
1
2.5
169900
0.91
1671
71
1
4
1
3
209900
0.46
2060
52
0
4
1
2.5
169900
0.59
1884
52
0
4
1
2.5
293000
7.24
2022
71
0
4
2
3
245900
0.19
2394
71
1
4
1
2.5
157000
0.46
1390
71
0
3
1
1.5
195000
0.41
1954
71
1
4
0
2.5
150000
0.78
1554
71
1
3
1
1.5
234900
0.89
1976
64
1
3
0
2.5
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279550
1.34
2479
71
0
4
1
2.5
246500
1
2714
71
0
4
1
2.5
124000
1
797
52
0
2
0
1
138000
0.27
1500
71
0
3
0
2.5
290000
0.71
1838
71
0
4
0
2
108000
0.9
1042
71
0
2
1
2
134900
0.24
1782
52
1
3
0
2.5
64500
0.06
1480
52
0
2
1
2.5
142000
0.55
1426
52
0
3
1
2
125000
0.34
1404
52
0
3
0
2
88000
0.19
1480
52
1
3
1
1.5
135000
0.23
1344
52
0
3
0
1
90000
0.07
1480
52
1
2
1
2.5
90100
0.09
1480
52
0
3
1
1.5
126900
0.25
1596
52
0
3
0
2.5
175000
0.47
1768
52
0
3
1
2
158000
0.36
1348
52
1
2
1
2
92000
0.07
1480
52
0
3
1
1.5
82800
0.11
1483
52
0
3
0
1
140000
0.23
1576
52
1
4
1
1.5
171000
3.16
1008
52
0
2
0
1
200640
0.08
2332
52
0
4
0
2.5
139000
0.57
1064
52
0
3
1
1
225000
0.5
2428
52
1
4
1
2.5
182000
1
1310
52
0
3
1
2
208767
0.5
1662
52
1
3
0
2
186000
0.55
2000
52
0
4
0
2.5
93000
0.1
1480
52
0
3
1
1.5
257386
0.5
2969
52
1
4
1
4
161000
0.31
1542
52
1
3
0
2.5
92000
0.28
1527
52
0
4
0
2
211002
0.06
2195
52
0
4
0
2.5
115000
0.06
1480
52
0
3
1
1.5
113000
0.46
1452
52
1
3
0
2
216300
0.1
2360
52
0
4
0
2.5
145000
2.12
1440
52
0
3
1
1
114000
0.06
1480
52
0
2
1
2.5
139050
0.21
1879
52
0
3
0
2.5
104000
0.06
1480
52
0
3
0
1.5
169900
0.26
1792
52
0
3
0
2.5
177900
0.29
1386
44
0
3
0
2.5
133000
1.29
1676
44
1
2
1
2
185000
1
768
22
0
4
0
2
115000
0.88
1560
22
1
3
1
1.5
100000
0.52
1000
22
0
3
0
1
117000
0.07
1676
51
0
4
1
1.5
150000
0.32
1656
51
0
3
0
1.5
187500
0.92
2300
51
0
3
1
1.5
107000
0.46
1712
51
0
3
1
1
126900
0.46
1350
51
0
3
0
1.5
147000
0.11
1676
51
0
3
1
2.5
62000
0.4
1317
51
0
3
0
1.5
101000
0.87
1056
51
0
3
1
2
143500
0.59
912
51
0
3
0
1
113400
0.93
1232
44
0
2
0
2
76320
0.15
1380
44
0
3
1
1
110000
0.59
936
44
1
3
0
1
88000
0.33
720
44
0
3
0
1
108000
0.32
1628
44
0
2
1
1.5
114900
0.46
1144
44
0
3
0
1.5
146000
0.48
1827
44
0
3
0
2.5
225000
2.37
1784
44
0
3
1
2
138000
0.83
1851
44
1
3
1
2.5
218070
1
2194
44
0
4
0
2.5
25000
0.21
920
44
0
2
0
1
112000
0.27
1280
44
0
3
0
1
112500
0.72
1232
44
0
3
1
1
97000
0.36
1406
44
0
3
0
1.5
121000
0.52
1164
44
0
3
0
2
65720
0.19
1198
44
0
3
0
1
225000
0.38
2206
44
1
4
1
2.5
192000
0.82
1880
44
1
3
1
2.5
158500
0.69
1368
44
0
3
0
1.5
247000
0.51
1901
44
1
3
1
2.5
193777
0.72
1547
44
0
3
0
2
136500
0.62
896
44
0
3
0
2
75000
1
1008
44
0
3
0
1
45000
0.52
912
44
0
2
0
1
243000
0.82
2443
44
0
3
0
2.5
90000
0.29
900
44
0
4
0
1
158000
0.52
2098
44
1
3
0
2
174000
0.64
2115
44
0
4
1
2.5
150000
0.52
1344
44
0
2
1
2
159650
1
1106
44
0
3
0
1
139062
0.69
1440
44
0
3
2
2
84950
0.13
840
51
0
2
0
1
102000
0.22
1170
51
0
2
0
1
175000
0.46
1664
51
0
4
0
1
78500
1
3024
51
0
4
0
1
232478
1
2598
51
0
4
1
2.5
93805
0.13
840
51
0
2
0
1.5
119900
1.03
1701
41
0
5
0
1
146000
1.17
1456
41
0
3
1
1.5
125000
0.34
1224
71
0
3
0
1
135000
0.38
1411
71
0
3
1
1
157500
1.96
1608
71
0
3
1
1.5
135000
3
3020
41
0
4
0
2.5
142900
0.33
1898
64
0
3
1
1.5
259900
0.46
2544
64
1
4
1
2.5
85000
0.29
1038
64
0
2
0
1
180377
0.35
1820
64
0
3
1
2.5
163500
0.28
2202
64
1
3
1
2
106500
0.52
2387
64
0
3
0
1
115000
0.11
1368
64
1
2
1
1.5
115000
0.14
1480
64
1
2
1
2
135000
0.14
1348
64
1
2
1
1.5
198900
0.47
1889
64
1
4
1
2.5
139000
0.14
1343
64
1
2
1
1.5
206000
0.51
2011
64
1
3
1
2.5
120000
0.21
1180
64
1
2
1
1.5
205980
0.14
1983
64
1
3
1
2.5
275000
0.55
2253
64
1
3
1
2.5
139000
0.07
1604
64
1
2
0
1.5
89900
0.49
1196
45
0
3
0
1
139900
0.07
1602
64
1
2
0
2.5
172750
0.36
1747
64
0
3
0
2.5
136000
0.5
1092
64
0
3
0
1
270000
0.48
2410
64
1
4
1
2.5
174000
0.29
1933
64
1
3
1
2.5
379000
0.68
3080
64
1
4
1
2.5
382500
4.08
4534
64
1
6
2
2.5
144900
0.1
1677
64
1
3
1
2.5
349900
0.62
2900
64
1
4
1
2.5
133000
0.07
1392
64
1
2
1
1.5
115000
0.16
1058
64
0
2
1
1.5
211950
0.36
2342
64
1
4
1
2.5
227812
0.01
2206
64
1
4
1
2.5
164400
0.34
1516
64
1
3
1
2
340000
0.75
2836
64
1
4
1
2.5
139900
0.29
1256
64
1
2
0
1.5
279900
0.47
2340
64
1
4
1
3.5
225000
0.32
2129
64
1
4
1
2.5
144500
0.12
1343
64
1
2
1
2.5
362710
1
2963
64
1
4
1
2.5
247000
0.46
2114
64
1
4
1
3.5
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120000
0.18
1144
64
1
2
1
1
136000
0.55
988
64
0
3
1
1
228500
0.46
1475
64
0
3
0
2
162800
0.15
1247
64
0
3
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4
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195000
0.55
2576
64
0
4
1
2.5
168500
0.44
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64
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0.06
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0.33
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0.69
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290000
0.51
2362
64
0
4
1
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184400
0.28
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221000
0.52
2096
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119900
0.18
840
36
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0.11
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0.28
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95000
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371165
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249230
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165000
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200000
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150000
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191000
0.44
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63
0
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123500
0.15
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123500
0.15
1385
54
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63
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23
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1988
63
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57
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2006
63
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255000
1
1994
60
0
3
1
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185000
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234000
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146500
0.12
1138
63
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0.49
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1
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0.4
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0
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0.13
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177555
0.56
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0
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1
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0.2
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1
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0.77
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0.09
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0.22
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0
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238000
0.71
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0
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211000
1
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0.23
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1
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0.46
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212000
0.71
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230000
0.47
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189000
0.42
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313635
1.93
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1
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185000
0.48
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0.42
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0.48
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120000
1.48
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0.33
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311000
0.46
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1
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1
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198000
0.32
1558
63
0
3
1
1
158500
0.92
1352
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0
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0
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227000
0.46
1920
63
0
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1
2.5
265900
0.34
1940
63
0
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1
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163000
0.23
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0
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0
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165000
0.28
1576
63
0
3
0
2.5
151000
0.07
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1
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1.5
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0.29
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1
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0.77
2748
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0.33
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0.66
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1
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1
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1
3.5
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0.39
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0
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0.31
2014
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0
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236000
0.42
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0
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124000
0.46
1745
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0
4
0
1.5
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0.14
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1
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0.5
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0
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63
0
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1
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0.32
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63
1
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1.5
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0.48
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0.77
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63
1
3
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2
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0.95
1500
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0
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0.53
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2.5
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0.06
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0.18
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1
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0.47
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2
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1.82
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120000
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1.5
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0.42
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0.08
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0
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271000
0.47
2003
63
1
3
1
2.5
129900
0.07
1480
54
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1
2.5
133000
0.06
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0.25
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239000
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33
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1.28
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21
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1.64
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129000
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0
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1.19
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0
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0.9
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0
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0.91
1927
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0
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0
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1
960
62
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3
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1804
62
0
3
1
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318310
0.23
2616
62
0
4
1
2.5
192500
1.2
1116
62
0
2
0
1.5
195000
1
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0
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138000
0.87
876
62
0
2
2
1.5
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0.79
2102
62
0
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1
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1
1776
62
0
3
1
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4.84
1996
62
1
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0.16
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35
0
3
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1
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1
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0
3
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0.29
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0
4
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135000
0.31
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27
0
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0.39
1620
35
0
3
0
2
344100
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2608
52
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4
2
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112000
1
1080
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0
3
0
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380000
0.3
1140
50
1
3
0
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125000
0.6
1152
36
0
3
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1
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5.52
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0
2
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0.48
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62
0
3
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0.15
936
35
0
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0.04
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33
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0.01
960
33
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2
0
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0.25
1058
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0
2
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1
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35
0
2
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1
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2
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52
0
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1
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176500
0.29
1198
33
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2
1
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212000
1
2430
50
1
4
0
1
135000
0.41
918
50
0
3
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1
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0.89
2722
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1
1.5
262000
0.46
2364
33
0
4
1
2.5
155000
1
1440
50
0
3
0
1
207000
0.27
1744
50
0
3
0
1
185000
0.14
2280
33
0
4
0
1.5
92200
0.42
1736
33
0
3
0
2
111000
0.74
883
50
0
1
0
1
97000
0.1
1001
50
0
2
0
1
200000
1.73
1296
50
0
3
0
1
230000
0.47
2304
40
0
3
1
2.5
299900
0.62
2076
52
0
3
1
2.5
183855
0.34
1468
52
0
4
0
1
174900
0.34
1272
52
0
3
1
1.5
335000
3.28
1900
43
1
3
1
2.5
208000
0.47
1548
52
0
3
0
1.5
380000
2
2212
52
0
4
1
1.5
289900
0.3
2376
52
0
4
1
2.5
188000
0.41
1712
52
1
3
1
1.5
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600000
0.99
2660
62
1
4
1
3.5
262000
0.29
1487
36
1
3
0
3
220000
2.03
1532
36
0
3
1
1.5
275000
0.61
2486
62
1
4
1
2.5
215000
0.46
1918
62
0
3
0
2
100000
0.51
1270
21
0
3
0
1
158500
0.17
1406
40
0
2
0
1.5
148900
0.15
1385
40
0
2
0
1.5
218000
0.45
1300
40
0
3
0
1.5
355840
0.87
3259
40
0
4
1
2.5
168000
0.08
1536
36
1
3
0
1.5
171000
0.58
1636
35
0
4
1
1.5
195000
0.41
1944
35
0
3
1
1.5
201880
0.46
1676
35
0
3
1
2
145000
0.47
1040
36
1
3
0
1
206000
0.01
1800
36
1
3
1
2.5
78000
0.14
1394
21
0
3
1
1.5
320900
0.47
1885
21
0
2
1
2
159000
0.61
1056
21
0
3
0
2
180000
0.16
1593
62
0
3
0
1.5
129900
0.05
1436
62
0
4
0
1
150000
0.38
1668
62
0
4
1
1.5
171134
0.25
1302
62
0
3
0
2
151000
0.37
1239
62
1
3
1
1
100000
0.31
840
62
0
2
0
1
655000
0.19
780
62
0
2
0
1.5
243500
0.29
1728
62
1
3
0
2.5
265000
0.18
1666
62
0
3
0
1
400000
0.24
2022
62
1
3
1
2.5
272800
0.08
2225
62
0
4
1
2
255000
0.17
1023
62
0
2
0
1
220000
0.13
1290
62
1
2
0
1.5
775000
0.48
3968
62
1
5
4
3.5
233000
0.14
1699
62
0
4
0
1
216977
0.48
2137
62
0
4
0
1
245000
0.03
1428
62
1
2
1
2.5
159000
0.24
936
62
1
3
0
1
725000
0.21
2586
62
1
4
1
3
268300
0.16
1373
62
0
3
1
2
159000
0.28
1272
62
0
3
0
1
155000
0.3
1368
62
1
3
1
2
345000
0.29
2120
62
1
4
1
2.5
165200
0.3
1279
62
0
2
0
1
185000
0.53
1104
62
1
2
0
1
225500
0.28
1092
62
0
3
1
1
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420000
0.11
2247
62
0
4
0
2
220000
0.18
1146
62
0
4
0
1.5
620000
0.53
3250
62
0
3
2
2.5
260000
0.35
1920
62
1
4
1
2.5
359900
0.5
1932
62
0
3
1
2
375000
0.14
1620
62
1
3
0
2.5
302900
0.22
1640
62
0
3
0
1
175000
0.17
1040
62
0
5
0
1.5
260000
5.35
1676
62
1
4
0
2
270000
0.33
1991
62
1
3
1
2.5
485000
0.24
1908
62
1
4
1
3.5
200000
0.57
966
62
0
3
0
1
367000
0.03
1815
62
1
2
1
2.5
175000
0.3
1664
62
0
3
0
1
203000
0.04
1216
62
1
2
1
2
170000
0.16
1564
62
1
3
0
2
375000
0.31
2752
62
1
4
1
2.5
159900
0.25
936
62
0
3
0
1
230000
0.14
1617
62
0
4
0
1.5
375000
0.12
1452
62
0
3
1
2
421000
0.31
1911
62
1
3
1
2.5
218000
0.28
1580
62
1
3
0
1.5
167850
0.27
1920
62
1
3
1
1
252500
0.21
1155
62
0
4
0
1.5
650000
0.34
3770
62
1
4
1
2.5
380000
0.04
1675
62
1
2
1
2.5
214500
0.21
912
62
0
3
0
1
218500
0.17
1510
62
0
3
0
1
174000
0.16
864
62
1
2
0
1
225000
0.46
1600
62
1
4
0
2
309750
0.07
1744
62
1
3
1
2.5
658300
0.69
3208
62
1
4
1
2.5
203000
0.06
1290
62
1
2
0
1.5
375000
0.03
1900
62
1
2
1
2.5
62500
1
2808
35
0
3
0
1
205000
0.19
1576
35
0
3
0
1.5
84500
0.2
1118
35
0
3
0
1
280500
1.09
1752
35
1
3
2
2
182000
0.02
1753
35
0
3
0
1.5
571000
1
2186
62
1
3
0
2.5
168000
0.25
1800
62
0
2
0
1
354000
0.46
2462
62
1
5
1
2.5
238000
0.2
1286
62
0
5
0
2
182000
0.16
1337
62
1
3
0
1
218500
0.17
1170
62
0
2
1
2.5
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227500
0.55
1185
62
0
3
0
1
375000
0.5
2424
62
1
4
1
2.5
399900
0.09
1828
62
0
3
0
1.5
360000
0.34
2176
62
1
4
1
2.5
412500
0.39
2000
62
1
3
1
2.5
268500
0.18
1340
62
1
3
1
2.5
300300
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325000
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173000
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355063
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0.73
2078
64
0
4
1
1.5
200500
0.69
1666
64
1
3
1
1.5
310000
0.46
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169900
0.06
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1
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210500
0.28
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305000
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2290
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0
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508000
0.17
3511
64
0
4
1
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220000
0.41
1922
64
0
4
0
1.5
256000
0.5
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64
0
4
1
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196900
0.41
1672
64
1
4
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233000
0.26
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162900
0.12
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178000
0.17
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64
1
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1.5
265000
0.67
2310
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0
5
1
2.5
454900
0.66
2880
64
0
4
1
2.5
215000
5.65
1948
64
1
2
1
1.5
248000
0.67
1613
64
1
3
1
1.5
395000
0.7
2803
64
1
4
1
3
171500
0.07
1479
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1
3
1
1.5
268000
0.48
2147
64
1
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2.5
166500
0.06
1314
64
1
2
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1.5
355000
0.43
2330
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165000
0.93
1352
64
0
3
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1
300000
0.08
2276
64
0
5
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350000
0.5
2162
64
1
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279900
0.49
2091
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265000
0.65
2564
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0
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267000
0.31
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235000
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1768
64
0
3
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2
299900
0.46
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167000
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64
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168000
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3
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150000
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455000
4.2
2828
64
1
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2.5
351100
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315000
0.46
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2.5
220000
0.4
1500
64
0
3
0
1.5
159000
0.14
1480
64
0
3
0
1.5
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341000
0.57
2250
64
1
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1
2.5
276000
0.47
1831
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309000
0.49
2214
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1
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2.5
217021
0.46
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64
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3
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1.5
188500
0.05
1302
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1.5
320000
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236000
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3
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2
233900
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1918
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4
1
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162740
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0
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1
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225000
0.45
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0
4
1
1.5
360000
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1
4
1
3
281520
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1
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1
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365000
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315000
0.6
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1
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270000
0.42
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64
1
4
0
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260000
0.42
2310
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0
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1
2.5
72000
0.33
1152
52
0
3
0
1
229500
0.34
1577
64
0
3
0
2
275000
0.54
2175
64
0
4
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155000
0.29
1375
64
0
2
0
1.5
225570
0.37
1616
64
0
3
1
3
224000
0.54
1954
64
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4
1
2
290000
0.61
2728
64
0
4
1
2.5
262000
0.64
2553
64
0
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1
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319900
0.34
1963
64
0
4
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2.5
265000
0.45
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64
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2.5
255000
0.33
1792
64
1
3
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1.5
217300
0.35
1663
64
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3
1
1
291000
0.49
2070
64
1
4
1
2.5
319000
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2.5
271500
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1
2.5
224900
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1732
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4
0
2
260000
0.63
2310
64
0
5
1
2.5
199900
0.63
1742
52
1
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1
1
252659
0.41
2802
64
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4
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2.5
312500
0.46
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2030
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155000
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130000
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1480
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1
2.5
264000
0.21
2427
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0
3
0
2.5
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193980
0.31
1348
40
0
2
0
2
219900
0.25
1391
40
0
3
0
2
469900
1.4
3422
62
1
4
1
4
314230
0.51
2686
62
1
4
1
3
138900
0.15
1034
40
1
2
0
1.5
160000
0.29
1072
40
1
2
0
1
228000
0.25
1512
40
0
3
0
2
224400
5
1008
40
0
2
0
1
132870
0.06
1480
40
0
3
1
1.5
135600
0.51
912
40
0
3
0
1
150600
0.07
1480
40
1
3
1
1.5
335000
1.25
1558
62
0
3
1
2
316250
0.04
2648
62
0
4
1
2.5
181280
0.39
2248
40
0
4
0
1
158000
0.73
1461
40
0
3
0
1
221000
0.23
1470
40
0
3
1
2.5
230000
0.5
1900
40
0
3
0
1.5
299000
0.1
2054
62
1
4
0
2.5
148000
0.07
1480
40
0
2
1
2.5
267500
0.24
1822
40
0
3
0
2
259000
0.01
1620
62
0
3
1
2.5
321000
0.1
2120
62
1
4
0
2.5
409790
0.27
3009
62
0
5
1
3.5
184000
0.45
1791
40
0
4
0
1
145000
0.07
1480
40
1
2
1
2.5
310000
1.75
1300
40
0
3
0
2
170000
0.07
1480
40
0
3
1
1.5
210500
0.02
1876
40
0
3
0
2
199900
0.24
1564
40
0
3
0
2
150000
0.28
1248
40
0
3
0
1.5
153000
0.11
1480
40
0
2
0
2.5
334000
0.55
2281
62
0
4
1
2.5
216900
0.4
2395
40
1
3
1
1.5
259000
0.05
1915
40
0
4
1
2.5
240000
0.3
1400
40
0
3
2
1.5
186030
0.37
1104
40
1
4
0
1
203000
0.43
1796
40
1
3
1
2.5
255000
0.13
1512
40
0
3
0
1.5
119000
0.09
1646
40
0
3
0
1
260800
0.5
1868
40
0
3
1
2.5
169000
0.19
1301
40
0
3
0
1.5
225000
0.95
1652
62
0
3
0
2
124000
0.35
1056
62
0
3
0
1
245000
0.51
2312
62
0
3
0
1.5
250000
0.48
2072
62
1
3
0
2.5
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172000
0.46
1200
62
0
2
0
1
153500
0.48
1764
62
0
3
1
1.5
165000
0.02
960
62
0
3
0
1
159000
0.17
988
51
0
2
0
2
171500
0.17
1514
51
0
3
0
1.5
240000
0.51
1320
61
1
3
0
2
409550
0.69
2719
61
1
3
1
2
302000
0.61
1796
61
0
3
1
2.5
285000
0.38
1709
61
0
3
1
2.5
385900
0.4
3008
61
1
4
1
2.5
140595
0.42
1350
61
0
3
0
1
213000
0.47
1200
61
1
3
0
1.5
335000
0.47
1857
61
1
3
1
2.5
290000
0.66
2305
51
1
4
1
2.5
265000
0.49
2184
51
1
3
1
2.5
334000
0.32
1668
51
0
3
0
2
382406
0.44
2712
51
1
4
1
2.5
149300
0.16
912
51
0
2
0
1.5
212000
0.34
1508
51
0
3
0
1.5
195000
0.46
1232
61
0
3
1
1
245000
0.36
1836
51
0
4
1
1.5
422680
0.16
4486
51
0
6
1
4
240000
4.21
2204
51
0
3
2
2
156000
0.16
1184
51
0
3
0
2
265000
0.49
2184
51
1
3
1
2.5
334000
0.32
1668
51
0
3
0
2
382406
0.44
2712
51
1
4
1
2.5
149300
0.16
912
51
0
2
0
1.5
212000
0.34
1508
51
0
3
0
1.5
195000
0.46
1232
61
0
3
1
1
171600
0.32
960
61
0
3
0
1
245000
0.36
1836
51
0
4
1
1.5
282000
0.87
1836
61
1
4
1
1.5
260000
0.34
1803
61
0
4
0
1.5
254500
0.17
1463
61
1
3
1
2
179900
0.07
1290
61
1
2
1
1.5
255000
0.53
2310
61
0
5
1
2.5
265000
0.43
1664
61
1
3
1
2
174000
0.06
1479
61
0
3
0
1.5
322000
0.26
2483
61
1
4
1
2.5
337000
0.46
2480
61
1
4
1
2.5
221450
0.36
2699
61
0
6
1
1
247000
0.3
1676
61
1
3
1
2.5
180000
0.54
2816
61
0
5
1
2.5
229900
0.37
1966
61
0
3
0
1.5
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230000
0.54
2112
61
0
4
1
1.5
388000
0.77
2762
61
0
4
1
2.5
350000
0.66
2184
61
1
3
1
2.5
169700
0.07
1480
61
0
3
0
1.5
190000
0.53
1624
61
0
3
0
1
317500
0.54
1650
61
1
3
1
2
70000
1.34
1313
61
0
2
0
1.5
300000
0.65
2600
61
0
4
1
2.5
145400
0.13
945
61
0
2
0
1
183500
0.28
1121
61
0
3
0
1.5
275900
0.81
2288
61
0
3
1
2.5
436023
0.41
2718
61
1
4
1
2.5
319900
0.58
2464
61
0
4
1
2.5
249000
0.43
2426
61
0
4
2
2
289000
0.38
2310
61
0
5
1
2.5
285000
0.94
2564
61
0
4
1
2.5
194900
0.39
1099
51
0
2
0
1
125000
0.24
1225
51
0
3
1
1
111300
0.59
1959
51
0
3
0
1
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SUMMARY OUTPUT
Regression Statistics
Multiple R
0.71239023
R Square
0.50749984
Adjusted R Square
0.5072145
Standard Error
69104.5592
Observations
1728
ANOVA
df
SS
MS
F
ignificance F
Regression
1
8.49E+12
8.49E+12
1778.567 9.49E-268
Residual
1726
8.24E+12
4.78E+09
Total
1727
1.67E+13
Coefficients
tandard Erro
t Stat
P-value
Lower 95%Upper 95%Lower 95.0%
Upper 95.0%
Intercept
13439.394 4992.353 2.691996
0.007171 3647.696 23231.09
3647.696 23231.09
X Variable 1
113.122542
2.682341 42.17307 9.49E-268
107.8616 118.3835
107.8616 118.3835
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