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Running head: MULTIPLE LINEAR REGRESSION 1
Application of Multiple Linear Regression
Student Name
Institution
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MULTIPLE LINEAR REGRESSION 2
Research Question
What factors affect the sale price of a house in the United States of America?
Data Description
The data of interest was retrieved from the United States of America Department of
housing (https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis). The data had 186 records
and seven attributes. Therefore, the sample size of the secondary data is 186 (N=186). The data
consists of seven columns namely: house sale price, size of the land a house is built on, the
condition of the house, age of the house, the number of bedrooms, detached state of the house,
and the house area in square feet (EQM).
The attributes “house sale price,” “the size of the land a house is built on,” “age of the
house,” “the number of bedrooms,” and “the house area” are numerical. In contrast, the attributes
“the condition of the house” and “detached state of the house” are categorical. Moreover, the
attributes “house sale price,” “the size of the land a house is built on,” “age of the house,” and
“the house area” are continuous while the attribute “number of bedrooms” is discrete. The
categorical variable “detached state” has two levels “yes” and “No.” The condition of a house is
measured on a Likert scale ranging from zero to nine where “9” stands for the excellent state of a
house and “0” stands for the poor state of a house.
Data Analysis
I would like to determine whether the size of the land a house is built on, the condition of
the house, age of the house, the number of bedrooms, detached state of the house, and the house
area in square feet (EQM) significantly affects the sale price of a house. Therefore, a multiple
linear regression model is the best statistical tool of study for the research question of interest. In
the study, the dependent variable in the study will be “house sale price” while the dependent
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MULTIPLE LINEAR REGRESSION 3
variables will include size of the land a house is built on, the condition of the house, age of the
house, the number of bedrooms, detached state of the house, and the house area in square feet
(EQM).
Reference
Agency, F., & Desk, H. (2019). FHFA House Price Indexes (HPIs) - Data.gov. Retrieved from
https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis

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Running head: MULTIPLE LINEAR REGRESSION Application of Multiple Linear Regression Student Name Institution 1 MULTIPLE LINEAR REGRESSION 2 Research Question What factors affect the sale price of a house in the United States of America? Data Description The data of interest was retrieved from the United States of America Department of housing (https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis). The data had 186 records and seven attributes. Therefore, the sample size of the secondary data is 186 (N=186). The data consists of seven columns namely: house sale price, size of the land a house is built on, the condition of the house, age of the house, the number of bedrooms, detached state of the house, and the house area in square feet (EQM). The attributes “house sale price,” “the size of the land a house is built on,” “age of the house,” “the number of bedrooms,” and “the house area” are numerical. In contrast, the attributes “the condition of the house” and “detached state of the house” are categorical. Moreover, the attributes “house sale price,” “the size of the land a house is built on,” “age of the house,” and “the house area” are continuous while the attribute “number of bedrooms” is discrete. The categorical variable “detached state” has two levels “yes” and “No.” The condition of a house is measured on a Likert scale ranging from zero to nine where “9” stands for the excellent state of a house and “0” stands for the poor state of a house. Data Analysis I would like to determine whether the size of the land a house is built on, the condition of the house, age of the house, the number of bedrooms, detached state of the house, and the house area in square feet (EQM) significantly affects the sale price of a house. Therefore, a multiple linear regression model is the best statistical tool of study for the research question of interest. In the study, the dependent variable in the study will be “house sale price” while the dependent MULTIPLE LINEAR REGRESSION 3 variables will include size of the land a house is built on, the condition of the house, age of the house, the number of bedrooms, detached state of the house, and the house area in square feet (EQM). Reference Agency, F., & Desk, H. (2019). FHFA House Price Indexes (HPIs) - Data.gov. Retrieved from https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Name: Description: ...
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