# just need some help

Anonymous
account_balance_wallet \$9.99

### Question Description

i have a couple of questions that need answered pretty soon. there's some regression stuff and some confidence interval things as well.

### Unformatted Attachment Preview

Regression Analysis and Simulation Question 1: What Predicts House Prices? A real estate company wants to know the effect of different house features on house prices. They collect data on 150 houses (House Prices.xlsx). The variables in the dataset are described in the first sheet of the excel file. Run a multiple regression predicting “House Price” with all the other variables in the dataset. Next, analyze your regression results to answer the following questions: a) b) c) d) Write down the regression model/equation. Show your table of regression results. By how much is house price expected to change if the house has a lake view? Is the number of bathrooms a statistically significant predictor of house price? Why or why not? If yes, what is the difference in predicted house price for a house with 3 bathrooms versus 2 bathrooms? e) Is the number of bedrooms a statistically significant predictor of house price? Why or why not? If yes, what is the effect of an additional bedroom on house price? f) What is the expected house price for a house with the following features? “house size”=1,000 sqft, “lot size”=0.5 acres, 4 bedrooms, 3 bathrooms, no lake view, and garage parking. g) How much would house price be expected to increase if the house has a parking garage compared to a house with street parking? h) According to the regression model, how much of the variation (%) in house prices is explained by the variation in all the independent variables? Question 2: TV Hours Use the following information for the next four questions: the station manager of a local television station is interested in predicting the amount of television (in hours) that people will watch in the viewing area. The independent variables are: X1 age (in years), X2 education (highest level obtained, in years) and X3 family size (number of family members in household). The multiple regression output is shown below: Summary measures Multiple R R-Square Adj R-Square StErr of Estimate 0.8440 0.7123 0.6644 0.5598 Copyright © 2015 by Anita Mukherjee and Hessam Bavafa Page 1 of 2 a) Use the information above to write the linear regression model b) What is the relationship between “age” and the amount of television watched? c) According to the regression model, how much would hours of television watched be expected to increase if the number of years of education increases by 2? d) According to the regression model, how much would hours of television watched be expected to increase if the age of a viewer decreases by 5? Question 3: McDonald’s Simulation McDonald’s has built a simulation model of one of its restaurants to measure average customer wait time. a) McDonald’s knows that the standard deviation of wait time is around 10min. It wants to build the 99% confidence interval for the average wait time with ±1 min of error. How many replications should it run? b) McDonald’s runs 400 replications. The mean wait time is 5min with a standard deviation of 9min. What is the 95% confidence interval for customer wait time? Page 2 of 2 Variable Comment House Price House Size Lot Size Bedrooms Bathrooms in dollars in square feet in acres number of bedrooms number of bathrooms Yes/No: whether or not house has a lakeview Lakeview Parking Type "Street Parking", "Driveway Parking", "Garage Parking" Hint You need only one dummy: a variable that is 1 if the house has lakeview and 0 otherise. You need only two dummy variables. While you can include any two of the three categories, we suggest you create dummies for "Driveway Parking" and "Street Parking". Home House Price House Size Lot Size Bedrooms Bathrooms Lakeview 1 \$102.000 600 0,50 3 1,0 No 2 \$146.300 1050 0,43 5 1,5 No 3 \$182.000 1800 0,68 7 1,5 Yes 4 \$110.500 922 0,30 5 1,0 No 5 \$171.900 1950 0,75 8 2,5 No 6 \$154.000 1783 0,22 8 1,5 No 7 \$147.000 1008 0,50 6 1,0 Yes 8 \$195.900 1840 1,16 8 2,0 Yes 9 \$183.500 3700 1,10 10 3,0 No 10 \$156.500 1092 0,26 6 1,0 Yes 11 \$152.000 1950 0,50 7 1,5 No 12 \$170.000 1403 0,50 6 2,0 Yes 13 \$253.000 1680 14,37 8 2,0 No 14 \$129.500 1000 0,49 4 1,0 No 15 \$241.900 2310 0,46 8 2,5 Yes 16 \$151.900 1300 0,78 6 1,0 Yes 17 \$199.000 1930 3,00 9 3,0 No 18 \$186.000 3000 0,50 11 2,5 No 19 \$153.500 1362 0,40 7 2,0 No 20 \$166.000 1750 0,50 7 2,0 No 21 \$224.900 2080 1,00 8 2,5 Yes 22 \$158.500 1344 0,94 6 2,0 No 23 \$332.000 2130 11,91 8 1,5 Yes 24 \$172.000 1500 0,41 7 1,0 Yes 25 \$176.000 2400 0,40 7 2,5 No 26 \$210.000 2272 0,41 9 2,5 Yes 27 \$156.500 1050 1,00 5 1,0 Yes 28 \$169.500 1610 0,45 8 1,5 Yes 29 \$154.900 1248 0,22 7 1,0 Yes 30 \$163.000 2000 0,50 8 2,0 No 31 \$140.000 1450 0,30 6 2,0 No 32 \$148.500 1248 0,25 7 1,0 Yes 33 \$224.500 2544 0,28 9 2,5 Yes 34 \$299.900 2500 0,92 8 3,0 Yes 35 \$199.900 2858 0,79 9 3,0 No 36 \$220.000 1745 0,58 7 2,5 Yes 37 \$233.000 2653 1,80 9 3,0 Yes 38 \$174.900 1450 0,30 7 1,0 Yes 39 \$124.000 850 0,11 4 1,0 No 40 \$169.900 1839 2,60 7 1,5 No 41 \$213.000 2016 0,78 8 2,5 Yes 42 \$165.000 1625 0,36 7 1,5 No 43 \$162.000 2000 0,11 8 2,0 No Parking Type Driveway Parking Street Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Garage Parking Driveway Parking Street Parking Driveway Parking Street Parking Street Parking Street Parking Driveway Parking Garage Parking Street Parking Driveway Parking Street Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Garage Parking Driveway Parking Street Parking Driveway Parking Driveway Parking Street Parking Driveway Parking Driveway Parking Street Parking Street Parking Driveway Parking Garage Parking Street Parking Garage Parking Street Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 \$211.500 \$166.000 \$194.000 \$192.000 \$171.000 \$226.800 \$155.000 \$157.500 \$297.000 \$315.000 \$161.000 \$193.500 \$163.000 \$180.000 \$171.000 \$163.000 \$220.000 \$155.900 \$219.900 \$185.000 \$172.500 \$167.900 \$160.000 \$147.000 \$210.500 \$192.500 \$138.000 \$200.000 \$186.000 \$217.000 \$180.000 \$195.000 \$149.000 \$165.500 \$175.900 \$156.000 \$235.406 \$215.500 \$225.000 \$155.000 \$190.000 \$126.000 \$172.000 \$175.000 2250 1300 1956 2496 1575 1960 1200 1296 1950 2516 1066 2276 1908 1122 3500 1100 2300 1118 2464 2100 1552 1856 1800 1248 2000 1848 1036 2277 2300 2080 1600 2680 1200 1526 1680 1232 2465 2800 2265 1300 1900 864 2000 1800 0,33 0,30 0,50 0,75 0,25 1,33 0,33 0,50 18,70 8,10 0,33 1,00 0,46 3,09 1,00 0,33 5,63 0,56 0,43 0,58 0,46 0,33 0,30 0,30 0,60 0,50 0,95 0,80 0,65 1,23 1,84 0,50 0,25 0,30 0,50 0,31 1,55 1,68 0,85 0,65 1,00 0,32 0,75 0,66 9 7 8 9 7 8 5 9 7 7 5 8 7 5 10 6 7 7 8 8 6 7 7 6 9 7 6 8 7 8 7 9 7 7 6 6 8 9 8 5 8 4 9 8 2,5 1,0 2,5 2,5 1,5 2,5 1,0 1,0 2,5 2,5 1,0 2,5 2,0 2,0 2,5 1,0 2,5 1,5 2,5 1,5 1,5 1,5 1,5 1,0 2,5 2,5 1,0 3,0 3,0 2,5 2,0 3,0 1,0 1,5 1,5 2,0 2,5 1,5 2,5 1,0 2,5 1,0 1,5 2,5 Yes Yes Yes No Yes Yes Yes Yes No Yes Yes No No Yes No Yes No Yes Yes Yes Yes No No Yes Yes Yes No No No Yes No No Yes Yes Yes No Yes Yes Yes Yes No No No No Garage Parking Driveway Parking Street Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Driveway Parking Driveway Parking Street Parking Driveway Parking Driveway Parking Street Parking Driveway Parking Street Parking Driveway Parking Garage Parking Driveway Parking Street Parking Driveway Parking Street Parking Garage Parking Driveway Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Street Parking Driveway Parking Garage Parking Driveway Parking Street Parking Driveway Parking Street Parking Garage Parking Garage Parking Garage Parking Driveway Parking 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 \$181.500 \$180.000 \$295.000 \$146.000 \$165.000 \$159.000 \$138.500 \$194.900 \$140.000 \$184.000 \$164.000 \$190.000 \$250.000 \$156.500 \$156.500 \$188.000 \$202.000 \$245.000 \$171.900 \$119.900 \$159.900 \$165.000 \$165.000 \$152.500 \$265.000 \$164.500 \$156.500 \$210.000 \$157.500 \$195.000 \$127.000 \$130.000 \$238.000 \$212.000 \$205.000 \$174.900 \$207.000 \$261.750 \$195.000 \$108.000 \$209.000 \$115.000 \$190.000 \$171.000 1900 1564 2400 1100 1800 1200 1540 1980 1289 1800 1502 2025 3000 1500 1600 1500 2100 2100 1632 1660 1070 1400 1800 1100 3150 2000 1700 1800 1850 2320 1300 1338 2288 2400 2400 1900 2010 2981 1725 821 3060 875 1760 2000 0,75 0,33 2,00 1,10 1,00 0,33 0,18 0,70 0,25 0,68 0,35 1,10 1,15 0,50 0,26 0,54 1,00 0,50 3,00 0,21 1,69 0,35 0,50 0,37 0,30 0,70 0,30 1,52 0,26 0,40 0,37 0,12 1,20 0,50 0,70 0,44 0,68 1,30 1,53 2,30 0,75 0,26 0,05 0,65 7 6 7 6 8 6 7 8 6 7 7 7 10 7 8 5 8 8 6 7 5 6 7 7 11 8 8 8 9 8 5 6 8 8 8 6 8 10 8 4 8 5 7 7 2,0 2,0 2,0 1,0 2,5 1,0 2,0 2,5 1,0 2,0 1,5 2,0 3,5 1,5 1,5 2,5 2,5 2,5 3,0 1,0 1,0 2,0 2,0 1,0 4,0 1,0 2,0 2,5 2,0 2,5 1,0 1,0 2,5 2,5 3,0 2,0 1,5 3,5 2,5 1,0 2,0 1,0 2,0 1,0 No Yes Yes No No Yes No No No Yes Yes No Yes No No Yes Yes Yes No No Yes No No Yes Yes No No Yes No No No No Yes Yes No No Yes Yes Yes No No No Yes No Garage Parking Driveway Parking Garage Parking Garage Parking Driveway Parking Driveway Parking Street Parking Garage Parking Garage Parking Street Parking Street Parking Garage Parking Street Parking Garage Parking Garage Parking Street Parking Street Parking Garage Parking Garage Parking Street Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Garage Parking Garage Parking Driveway Parking Garage Parking Garage Parking Driveway Parking Street Parking Street Parking Driveway Parking Driveway Parking Driveway Parking Garage Parking 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 \$215.000 \$143.500 \$220.000 \$137.000 \$247.000 \$224.500 \$182.000 \$240.000 \$170.000 \$150.500 \$209.900 \$182.500 \$189.000 \$198.500 \$128.000 \$147.500 \$145.000 \$305.000 \$220.000 2600 1624 2473 1100 3100 2300 1450 2100 1650 1600 2790 1786 1728 1900 1165 1300 1080 2820 2100 0,75 1,80 1,25 0,17 0,54 0,91 0,30 0,50 0,50 0,40 0,75 0,30 0,50 1,06 0,12 0,29 0,31 1,00 1,30 8 7 9 5 10 8 6 8 8 6 13 8 8 7 6 6 5 9 8 2,0 1,5 2,5 1,0 3,5 2,5 1,5 2,5 2,5 2,0 2,5 2,0 1,5 2,5 1,0 1,0 1,0 2,5 1,5 Yes No Yes No Yes Yes Yes Yes No No No Yes Yes Yes No Yes Yes Yes Yes Driveway Parking Street Parking Driveway Parking Garage Parking Street Parking Driveway Parking Driveway Parking Garage Parking Driveway Parking Driveway Parking Garage Parking Garage Parking Driveway Parking Street Parking Driveway Parking Garage Parking Street Parking Garage Parking Garage Parking ...
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HKPJ
School: Rice University

Attached.

Regression Analysis and Simulation
Question 1: What Predicts House Prices?
A real estate company wants to know the effect of different house features on house prices. They
collect data on 150 houses (House Prices.xlsx). The variables in the dataset are described in the
first sheet of the excel file.
Run a multiple regression predicting “House Price” with all the other variables in the dataset. Next,
a)
b)
c)
d)

Write down the regression model/equation.
Show your table of regression results.
By how much is house price expected to change if the house has a lake view?
Is the number of bathrooms a statistically significant predictor of house price? Why or why
not? If yes, what is the difference in predicted house price for a house with 3 bathrooms
versus 2 bathrooms?
e) Is the number of bedrooms a statistically significant predictor of house price? Why or why
not? If yes, what is the effect of an additional bedroom on house price?
f) What is the expected house price for a house with the following features? “house
size”=1,000 sqft, “lot size”=0.5 acres, 4 bedrooms, 3 bathrooms, no lake view, and garage
parking.
g) How much would house price be expected to increase if the house has a parking garage
compared to a house with street parking?
h) According to the regression model, how much of the variation (%) in house prices is
explained by the variation in all the independent variables?

Question 2: TV Hours
Use the following information for the next four questions: the station manager of a local television
station is interested in predicting the amount of television (in hours) that people will watch in the
viewing area. The independent variables are: X1 age (in years), X2 education (highest level
obtained, in years) and X3 family size (number of family members in household). The multiple
regression output is shown below:

Summary measures
Multiple R
R-Square
StErr of Estimate

0.8440
0.7123
0.6644
0.5598

Page 1 of 2

a) Use the information above to write the linear regression model
b) What is the relationship between “age” and the amount of television watched?
c) According to the regression model, how much would hours of television watched be
expected to increase if the number of years of education increases by 2?
d) According to the regression model, how much would hours of television watched be
expected to increase if the age of a viewer decreases by 5?

Question 3: McDonald’s Simulation
McDonald’s has built a simulation model of one of its restaurants to measure average customer
wait time.
a) McDonald’s knows that the standard deviation of wait time is around 10min. It wants to
build the 99% confidence interval for the average wait time with ±1 min of error. How
many replications should it run?
b) McDonald’s runs 400 replications. The mean wait time is 5min with a standard deviation
of 9min. What is the 95% confidence interval for customer wait time?

Page 2 of 2

Regression Analysis and Simulation
Question 1: What Predicts House Prices?
A real estate company wants to know the effect of different house features on house prices. They
collect data on 150 houses (House Prices.xlsx). The variables in the dataset are described in the
first sheet of the excel file.
Run a multiple regression predicting “House Price” with all the other variables in the dataset. Next,
a) Write down the regression model/equation.
b) Show your table of regression results.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.931741113
R Square
0.868141502
0.862608978
Standard Error
15423.6188
Observations
150
c) By how much is house price expected to change if the house has a lake view?
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.404745579
R Square
0.163818984
0.158169112
Standard Error
38178.5591
Observations
150
Out of 150 total observations, house prices are expected to change if the house has a lake
view by 16% for every \$38,178.55 incremental house price increase.
d) Is the number of bathrooms a statistically significant predictor of house price? Why or why
not? If yes, what is the difference in predicted house price for a house with 3 bathrooms
versus 2 bathrooms?
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.660459721
R Square
0.436207043
0.432397631
Standard Error
31349.39797
Observations
150
Yes, the number of bathrooms is a statistically significant predictor of house price because the
value of lit size increase which increase the overall price of the house’s value. The increase in
needed space increases price values.

e) Is the number of bedrooms a statistically significant predictor of house price? Why or why
not? If yes, what is the effect of an additional bedroom on house price?
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.576506309
R Square
0.332359524
0.32784844
Standard Error
34114.63358
Observations
150
Yes, as concept applies as above in which the number of bedrooms a statistically significant
predictor of house price because the value of lit size increase which increase the overall price of
the house’s value. The increase in needed space increases price values.
f)

What is the expected house price for a house with the following features? “house
size”=1,000 sqft, “lot size”=0.5 acres, 4 bedrooms, 3 bathrooms, no lake view, and garage
parking.
SUMMARY OUTPUT
Regression Statistics

Multiple R
0.576506309
R Square
0.332359524
0.32784844
Standard Error
34114.63358
Observations
150
The expected house price for a house with the following features. “House size”=1,000 sqft, “lot
size”=0.5 acres, 4 bedrooms, 3 bathrooms, no lake view, and garage parking is \$129,500. See excel

workbook, worksheet title “Data.”
g) How much would house price be expected to increase if the house has a parking garage
compared to a house with street parking?
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Standard Error
Observations

0.165624288
0.027431405
0.020859995
41174.61081
150

House prices are expected to increase if the house has a parking garage compared to a house
with street parking by 16% for every \$41,174.61 house pricing increase.
h) According to the regression model, how much of the variation (%) in house prices is
explained by the variation in all the independent variables?
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.931741113

R Square
0.868141502
0.862608978
Standard Error
15423.6188
Observations
150
House pricing will vary depending on specific property needs which dictates the total amount of
footage required, and added amenities. The price value for all variations ranges from 86% to 93%
increase in pricing. Low value prices represent minimal amenities and footage, while higher percentile
ranges represent increased amenities and amount of needed footage.

Question 2: TV Hours
Use the following information for the next four questions: the station manager of a local television
station is interested in predicting the amount of television (in hours) that people will watch in the
viewing area. The independent variables are: X1 age (in years), X2 education (highest level
obtained, in years) and X3 family size (number of family members in household). The multiple
regression output is shown below:

Summary measures
Multiple R
R-Square
StErr of Estimate

0.8440
0.7123
0.6644
0.5598

a) Use the information above to write the linear regression model

Page 1 of 2

0.35
0.3
0.25

0.2135

0.2
0.15
0.1
0.0222

0.05
0
-0.05

0.06116
0.0405

0

0.0005

-0.0498
1

0.5

1.5

2

2.5

3

3.5

4

-0.1
-0.15
Independent Variables

Dependent Variables

b) What is the relationship between “age” and the amount of television watched?
The relationship between “age” and the amount of television watched expands between ages
one and three with two years of age watching television majority of the time.
c) According to the regression model, how much would hours of television watched be expected
to increase if the number of years of education increases by two?
0.7
0.6
0.427

0.5
0.4
0.3
0.2
0.1

0.001

0

0.0222
-0.0498

-0.1 0

1

2

0.06116
0.0405
3

4

5

6

-0.2
-0.3
Independent Variables

Dependent Variables

Linear (Independent Variables )

Linear (Dependent Variables )

The amount of hours of television watched expected to increase if the number of years of
education increases by two would increase television-viewing times.

d) According to the regression model, how much would hours of television watched be expected
to increase if the age of a viewer decreases by 5?
0.2
0.15
0.1
0.0427

0.05

0.06116
0.0405

0.0222
0.0001

0
0

1
-0.0498

2

3

4

5

6

-0.05
-0.1
Independent Variables

Dependent Variables

Linear (Independent Variables )

Linear (Dependent Variables )

The amount of hours of television watched expected to increase if the number of years of
education decreases by five would decrease the amount of television viewing times.
Question 3: McDonald’s Simulation
McDonald’s has built a simulation model of one of its restaurants to measure average customer
wait time.
a) McDonald’s knows that the standard deviation of wait time is around 10min. It wants to build
the 99% confidence interval for the average wait time with ±1 min of error. How many
replications should it run?
1 − 𝜕 = 0.99 = 𝜕 = 0.001
𝑠
𝐸 = 𝑡𝜕 ∗ (𝑛 − 1) ∗ (
=
√𝑛)
2
1
𝐸 = 𝑡1 ∗ (10 − 1) ∗ (
= 0.45
2
√10)
b) McDonald’s will need to run one replication ever .45 seconds for 10 minutes equaling 27
replications.
McDonald’s runs 400 replications. The mean wait time is 5min with a standard deviation of
9min. What is the 95% confidence interval for customer wait time?

Variable

Comment

House Price
House Size
Lot Size
Bedrooms
Bathrooms

in dollars
in square feet
in acres
number of bedrooms
number of bathrooms
Yes/No: whether or not house has a
lakeview

Lakeview

Parking Type

"Street Parking", "Driveway Parking",
"Garage Parking"

Hint

You need only one dummy: a variable that is 1 if
the house has lakeview and 0 otherise.
You need only two dummy variables. While you
can include any two of the three categories, we
suggest you create dummies for "Driveway
Parking" and "Street Parking".

Home House Price House Size Lot Size Bedrooms Bathrooms Lakeview Parking Type
1
\$102.000
600
0,50
3
1,0
0
2
\$146.300
1050
0,43
5
1,5
0
3
\$182.000
1800
0,68
7
1,5
1
4
\$110.500
922
0,30
5
1,0
0
5
\$171.900
1950
0,75
8
2,5
0
6
\$154.000
1783
0,22
8
1,5
0
7
\$147.000
1008
0,50
6
1,0
1
8
\$195.900
1840
1,16
8
2,0
1
9
\$183.500
3700
1,10
10
3,0
0
10
\$156.500
1092
0,26
6
1,0
1
11
\$152.000
1950
0,50
7
1,5
0
12
\$170.000
1403
0,50
6
2,0
1
13
\$253.000
1680 14,37
8
2,0
0
14
\$129.500
1000
0,49
4
1,0
0
15
\$241.900
2310
0,46
8
2,5
1
16
\$151.900
1300
0,78
6
1,0
1
17
\$199.000
1930
3,00
9
3,0
0
18
\$186.000
3000
0,50
11
2,5
0
19
\$153.500
1362
0,40
7
2,0
0
20
\$166.000
1750
0,50
7
2,0
0
21
\$224.900
2080
1,00
8
2,5
1
22
\$158.500
1344
0,94
6
2,0
0
23
\$332.000
2130 11,91
8
1,5
1
24
\$172.000
1500
0,41
7
1,0
1
25
\$176.000
2400
0,40
7
2,5
0
26
\$210.000
2272
0,41
9
2,5
1
27
\$156.500
1050
1,00
5
1,0
1
28
\$169.500
1610
0,45
8
1,5
1
29
\$154.900
1248
0,22
7
1,0
1
30
\$163.000
2000
0,50
8
2,0
0
31
\$140.000
1450
0,30
6
2,0
0
32
\$148.500
1248
0,25
7
1,0
1
33
\$224.500
2544
0,28
9
2,5
1
34
\$299.900
2500
0,92
8
3,0
1
35
\$199.900
2858
0,79
9
3,0
0
36
\$220.000
1745
0,58
7
2,5
1
37
\$233.000
2653
1,80
9
3,0
1
38
\$174.900
1450
0,30
7
1,0
1
39
\$124.000
850
0,11
4
1,0
0
40
\$169.900
1839
2,60
7
1,5
0
41
\$213.000
2016
0,78
8
2,5
1
42
\$165.000
1625
0,36
7
1,5
0
43
\$162.000
2000
0,11
8
2,0
0

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80
81
82
83
84
85
86
87

\$211.500
\$166.000
\$194.000
\$192.000
\$171.000
\$226.800
\$155.000
\$157.500
\$297.000
\$315.000
\$161.000
\$193.500
\$163.000
\$180.000
\$171.000
\$163.000
\$220.000
\$155.900
\$219.900
\$185.000
\$172.500
\$167.900
\$160.000
\$147.000
\$210.500
\$192.500
\$138.000
\$200.000
\$186.000
\$217.000
\$180.000
\$195.000
\$149.000
\$165.500
\$175.900
\$156.000
\$235.406
\$215.500
\$225.000
\$155.000
\$190.000
\$126.000
\$172.000
\$175.000

2250
1300
1956
2496
1575
1960
1200
1296
1950
2516
1066
2276
1908
1122
3500
1100
2300
1118
2464
2100
1552
1856
1800
1248
2000
1848
1036
2277
2300
2080
1600
2680
1200
1526
1680
1232
2465
2800
2265
1300
1900
864
2000
1800

0,33
0,30
0,50
0,75
0,25
1,33
0,33
0,50
18,70
8,10
0,33
1,00
0,46
3,09
1,00
0,33
5,63
0,56
0,43
0,58
0,46
0,33
0,30
0,30
0,60
0,50
0,95
0,80
0,65
1,23
1,84
0,50
0,25
0,30
0,50
0,31
1,55
1,68
0,85
0,65
1,00
0,32
0,75
0,66

9
7
8
9
7
8
5
9
7
7
5
8
7
5
10
6
7
7
8
8
6
7
7
6
9
7
6
8
7
8
7
9
7
7
6
6
8
9
8
5
8
4
9
8

2,5
1,0
2,5
2,5
1,5
2,5
1,0
1,0
2,5
2,5
1,0
2,5
2,0
2,0
2,5
1,0
2,5
1,5
2,5
1,5
1,5
1,5
1,5
1,0
2,5
2,5
1,0
3,0
3,0
2,5
2,0
3,0
1,0
1,5
1,5
2,0
2,5
1,5
2,5
1,0
2,5
1,0
1,5
2,5

1
1
1
0
1
1
1
1
0
1
1
0
0
1
0
1
0
1
1
1
1
0
0
1
1
1
0
0
0
1
0
0
1
1
1
0
1
1
1
1
0
0
0
0

1
3
2
3
3
1
3
3
3
1
3
3
3
3
2
3
3
2
3
2
3
1
3
2
3
2
1
3
3
3
1
3
3
2
3
1
3
2
3
2
1
1
1
3

88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

\$181.500
\$180.000
\$295.000
\$146.000
\$165.000
\$159.000
\$138.500
\$194.900
\$140.000
\$184.000
\$164.000
\$190.000
\$250.000
\$156.500
\$156.500
\$188.000
\$202.000
\$245.000
\$171.900
\$119.900
\$159.900
\$165.000
\$165.000
\$152.500
\$265.000
\$164.500
\$156.500
\$210.000
\$157.500
\$195.000
\$127.000
\$130.000
\$238.000
\$212.000
\$205.000
\$174.900
\$207.000
\$261.750
\$195.000
\$108.000
\$209.000
\$115.000
\$190.000
\$171.000

1900
1564
2400
1100
1800
1200
1540
1980
1289
1800
1502
2025
3000
1500
1600
1500
2100
2100
1632
1660
1070
1400
1800
1100
3150
2000
1700
1800
1850
2320
1300
1338
2288
2400
2400
1900
2010
2981
1725
821
3060
875
1760
2000

0,75
0,33
2,00
1,10
1,00
0,33
0,18
0,70
0,25
0,68
0,35
1,10
1,15
0,50
0,26
0,54
1,00
0,50
3,00
0,21
1,69
0,35
0,50
0,37
0,30
0,70
0,30
1,52
0,26
0,40
0,37
0,12
1,20
0,50
0,70
0,44
0,68
1,30
1,53
2,30
0,75
0,26
0,05
0,65

7
6
7
6
8
6
7
8
6
7
7
7
10
7
8
5
8
8
6
7
5
6
7
7
11
8
8
8
9
8
5
6
8
8
8
6
8
10
8
4
8
5
7
7

2,0
2,0
2,0
1,0
2,5
1,0
2,0
2,5
1,0
2,0
1,5
2,0
3,5
1,5
1,5
2,5
2,5
2,5
3,0
1,0
1,0
2,0
2,0
1,0
4,0
1,0
2,0
2,5
2,0
2,5
1,0
1,0
2,5
2,5
3,0
2,0
1,5
3,5
2,5
1,0
2,0
1,0
2,0
1,0

0
1
1
0
0
1
0
0
0
1
1
0
1
0
0
1
1
1
0
0
1
0
0
1
1
0
0
1
0
0
0
0
1
1
0
0
1
1
1
0
0
0
1
0

1
3
1
1
3
3
2
1
1
2
2
1
2
1
1
2
2
1
1
2
3
1
3
3
3
1
3
3
3
1
3
3
1
1
3
1
1
3
2
2
3
3
3
1

132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
Min
Mean
Max
Range
STD.

\$215.000
\$143.500
\$220.000
\$137.000
\$247.000
\$224.500
\$182.000
\$240.000
\$170.000
\$150.500
\$209.900
\$182.500
\$189.000
\$198.500
\$128.000
\$147.500
\$145.000
\$305.000
\$220.000
\$102.000,00
\$183.775,04
\$332.000,00
\$230.000,00
\$41.471,96

2600
1624
2473
1100
3100
2300
1450
2100
1650
1600
2790
1786
1728
1900
1165
1300
1080
2820
2100

0,75
1,80
1,25
0,17
0,54
0,91
0,30
0,50
0,50
0,40
0,75
0,30
0,50
1,06
0,12
0,29
0,31
1,00
1,30

8
7
9
5
10
8
6
8
8
6
13
8
8
7
6
6
5
9
8

2,0
1,5
2,5
1,0
3,5
2,5
1,5
2,5
2,5
2,0
2,5
2,0
1,5
2,5
1,0
1,0
1,0
2,5
1,5

1
0
1
0
1
1
1
1
0
0
0
1
1
1
0
1
1
1
1
1=Yes
0=No

3
2
3
1
2
3
3
1
3
3
1
1
3
2
3
1
2
1
1
Garage Parking
Street Parking
Driveway Parking

1
2
3

SUMMARY OUTPUT
Regression Statistics
Multiple R
0,931741113
R Square
0,868141502
0,862608978
Standard Error
15423,6188
Observations
150
ANOVA
df
Regression
Residual
Total

Intercept
House Size
Lot Size
Bedrooms
Bathrooms
Lakeview
Parking Type

6
143
149

SS
2,23971E+11
34017986436
2,57989E+11

MS
37328431975
237888017

Coefficients
91496,34855
30,61732842
7874,740407
-896,5660339
16593,18625
34835,71603
-7165,357871

Standard Error
7392,851826
4,588817929
574,3768045
1518,665589
2818,600879
2548,380495
1481,154858

t Stat
12,37632658
6,672160216
13,71005992
-0,590364357
5,887029403
13,66974677
-4,83768313

RESIDUAL OUTPUT
Observation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

Predicted House Price
106211,5303
133106,9152
183915,8227
112702,2299
184251,2738
144040,6655
165180,134
196320,4184
239925,8608
151531,3361
159420,6106
186701,8071
267776,6084
117483,1483
227825,5533
169159,9633
194426,4045

Residuals
Standard Residuals
-4211,530336
-0,278726824
13193,08477
0,873142616
-1915,822731
-0,126792672
-2202,229939
-0,145747628
-12351,27376
-0,817430014
9959,334499
0,659127075
-18180,134
-1,203194706
-420,4183545
-0,02782406
-56425,86082
-3,73436725
4968,663854
0,328835312
-7420,610567
-0,491109656
-16701,8071
-1,105356313
-14776,60843
-0,977943124
12016,85173
0,79529735
14074,4467
0,931472769
-17259,96334
-1,142296119
4573,595548
0,302688965

18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64

204575,7275
141761,3827
154428,3801
225035,9276
146359,1966
295887,0256
164307,8512
189004,1206
211041,076
156969,2823
182556,1319
155096,0837
161186,1462
151730,1574
162497,6838
218345,2731
245561,8194
212601,4668
212368,2976
249114,1183
161910,7633
124228,8634
165393,6841
207013,2599
155532,873
158114,9974
224068,2313
157318,164
210136,6508
185741,0533
173640,7855
223960,5125
156285,8055
156977,5107
312168,7144
295192,3057
152183,0835
181870,4922
158950,9284
192225,1236
224718,328
152327,5066
219961,9222
169255,1938
217973,6639
198582,3391
175486,8484

-18575,72752
11738,61733
11571,61986
-135,9275779
12140,80339
36112,97441
7692,14883
-13004,12057
-1041,...

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