Question

A real estate developer wishes to study the relationship between the size of home a client will purchase (in square feet) and other variables. Possible independent variables include the family income, family size, whether there is a senior adult parent living with the family (1 for yes, 0 for no), and the total years of education beyond high school for the husband and wife. The sample information is reported below.

Family | Square Feet | Income (000s) | Family Size | Senior Parent | Education | |||||

1 | 2,300 | 60.8 | 2 | 0 | 4 | |||||

2 | 2,300 | 68.4 | 3 | 1 | 6 | |||||

3 | 3,400 | 104.5 | 3 | 0 | 7 | |||||

4 | 3,360 | 89.3 | 4 | 1 | 0 | |||||

5 | 3,000 | 72.2 | 4 | 0 | 2 | |||||

6 | 2,900 | 114 | 3 | 1 | 10 | |||||

7 | 4,100 | 125.4 | 5 | 0 | 6 | |||||

8 | 2,500 | 83.6 | 3 | 0 | 8 | |||||

9 | 4,200 | 133 | 5 | 0 | 2 | |||||

10 | 2,800 | 95 | 3 | 0 | 6 |

a. Develop an appropriate multiple regression equation using
stepwise regression. **(Use Excel data analysis and enter
number of family members first, then their income and delete any
insignificant variables. Leave no cells blank - be certain to enter
"0" wherever required.** R and R2 adj are in percent values.
**Round your answers to 3 decimal places.)**

Step | 1. | 2. |

Constant | ||

Family Size | ||

T-Statistic | ||

P-value | ||

Income | ||

t-statistic | ||

p-value | ||

S | ||

R-Sq | ||

R-Sq (Adj) |

- Select all independent variables that should be in the final
model.
**(You may select more than one answer. Single-click the box with the question mark to produce a check mark for a correct answer and double-click the box with the question mark to empty the box for a wrong answer. Any boxes left with a question mark will be automatically graded as incorrect.)**

- Senior parent Yes or no
- Square feet Yes or no
- Family size Yes or no
- Income Yes or no
- Education Yes or no

Answer #1

a. Select all independent variables that should be in the final model is Income , Family size

A real estate builder wishes to determine how house size (House)
is influenced by family income (Income), family size (Size), and
education of the head of household (School). House size is measured
in hundreds of square feet, income is measured in thousands of
dollars, and education is in years. The builder randomly selected
50 families and ran the multiple regression. Microsoft Excel output
is provided below:
SUMMARY OUTPUT
Regression
Statistics
Multiple
R 0.865
R
Square 0.748
Adjusted R
Square 0.726
Standard
Error 5.195
Observations 50...

An executive in the home construction industry is interested in
how house size (House) is influenced by family income (Income),
family size (Size), and education of the head of household
(School). House size is measured in hundreds of square feet, income
is measured in thousands of dollars, and education is in years. The
executive randomly selected 50 families and ran the multiple
regression. Excel output is provided below:
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.865
R Square
0.748
Adjusted R...

A real estate builder wishes to determine how house size (House)
is influenced by family income (Income), family size (Size), and
education of the head of household (School). House size is measured
in hundreds of square feet, income is measured in thousands of
dollars, and education is in years. The builder randomly selected
50 families and ran the multiple regression. The business
literature involving human capital shows that education influences
an individual’s annual income. Combined, these may influence family
size....

A real estate agent in Athens used regression analysis to
investigate the relationship between apartment sales prices and the
various characteristics of apartments and buildings.
The variables collected from a random sample of 25 compartments are
as follows:
Sale price: The sale price of the apartment (in €)
Apartments: Number of apartments in the building
Age: Age of the building (in years)
Size: Apartment size (area in square meters)
Parking spaces: Number of car parking spaces in the building
Excellent...

Statistical Methods of Business II – Case Study –
Indiana Real Estate
Ann Perkins, a realtor in Brownsburg, Indiana, would like to use
estimates from a multiple regression model to help prospective
sellers determine a reasonable asking price for their homes. She
believes that the following four factors influence the asking price
(Price) of a house:
The square footage of the house (SQFT)
The number of bedrooms (Bed)
The number of bathrooms (Bath)
The lot size (LTSZ) in acres
She...

1.A real estate analyst has developed a multiple regression
line, y = 60 + 0.068 x1 – 2.5
x2, to predict y = the market
price of a home (in $1,000s), using two independent variables,
x1 = the total number of square feet of living
space, and x2 = the age of the house in years.
With this regression model, the predicted price of a 10-year old
home with 2,500 square feet of living area is __________.
$205.00
$255,000.00
$200,000.00...

A 10-year study conducted by the American Heart Association
provided data on how age, blood pressure, and smoking relate to the
risk of strokes. Data from a portion of this study are contained in
the Excel Online file below. Risk is interpreted as the probability
(times 100) that a person will have a stroke over the next 10-year
period. For the smoker variable, 1 indicates a smoker and 0
indicates a nonsmoker. Construct a spreadsheet to answer the
following questions....

2: A real estate agent is interested in what factors determine
the selling price of homes in Northwest Arkansas. She takes a
random sample of 20 homes, and conducts a multiple regression
analysis. The dependent variable is price of the home (in thousands
of dollars), the square footage of the home, and whether the home
is located in a new subdivision (0 = no; 1 = yes). The results of
the multiple regression analysis are shown below. Answer the
following...

An agent for a real estate company in a large city collected
data on the sizes (x in square feet) and monthly rents (y in
dollars) of a sample of eight apartments in a neighborhood. The
data is shown below, along with the linear regression equation.
Complete parts a through c below.
yi = 279.7 + 0.906 xi
Monthly Rent ($)
900
1500
800
1,600
1,950
950
1,750
1,350
Size (Square Feet)
800
1,300
1,050
1,250
2,000
700
1,300
1,050...

Overview of the Study: The data are based on a Comprehensive
School Reform (CSR) Initiative that focused on the improvement of
reading and writing for students in the primary grade. The school
received a grant from the state which was used to strengthen
classroom teachers’ instructional skills.
The regression outputs present information for students in the
school. Description of the variables: Please use the following
description/coding to help you in your analyses. Gender: female; 1
male=0 EnrollmentStatus: 0 - Not...

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