D600 Task 1: Linear Regression Analysis
Report
A. GitLab Repository
A subgroup and project were successfully created in GitLab for Task 1. The project was
correctly cloned into the IDE, and commits were made after the completion of each rubric-
aligned section. Each commit included a descriptive message and timestamp to ensure
proper version tracking and traceability. The GitLab repository URL was submitted in the
“Comments to Evaluator” section as instructed, and a full branch history was exported and
included in the final submission.
B. Purpose of Data Analysis
This section defines the core objective of the analysis. A research question is proposed to
explore how certain housing characteristics impact home prices. A clear, real-world goal is
also stated to guide the direction of the model development and analysis.
B1. Research Question:
What is the impact of SquareFootage, SchoolRating, DistanceToCityCenter, AgeOfHome,
Fireplace, Garage, and HouseColor on home prices?
B2. Goal:
To develop a predictive model for estimating home prices based on housing characteristics
to guide pricing decisions.
,C. Data Preparation
The variables selected for the regression model are explained and justified based on domain
knowledge. Descriptive statistics and visualizations provide an understanding of the data
distribution and potential relationships. These steps ensure data suitability and adherence
to regression assumptions.
C1. Selected Variables:
Dependent Variable (1 total): Price
Independent Variables (7 total):
Quantitative: SquareFootage, SchoolRating, DistanceToCityCenter, AgeOfHome
Categorical: Fireplace, Garage, HouseColor
Justification: SquareFootage, SchoolRating, and DistanceToCityCenter are known drivers of
home value. AgeOfHome captures depreciation effects. Fireplace and Garage represent
buyer-valued amenities. HouseColor introduces stylistic appeal and categorical diversity,
ensuring a balanced model with both numerical and categorical predictors.
C2. Descriptive Stats
Categorical Variable Frequencies
Fireplace:
No: 0.74
Yes: 0.26
Garage:
No: 0.64
Yes: 0.36
HouseColor:
White: 0.21
Yellow: 0.20
Blue: 0.20
Green: 0.20
Red: 0.19
Fireplace Visualization
, Garage Visualization
HouseColor Visualization
Report
A. GitLab Repository
A subgroup and project were successfully created in GitLab for Task 1. The project was
correctly cloned into the IDE, and commits were made after the completion of each rubric-
aligned section. Each commit included a descriptive message and timestamp to ensure
proper version tracking and traceability. The GitLab repository URL was submitted in the
“Comments to Evaluator” section as instructed, and a full branch history was exported and
included in the final submission.
B. Purpose of Data Analysis
This section defines the core objective of the analysis. A research question is proposed to
explore how certain housing characteristics impact home prices. A clear, real-world goal is
also stated to guide the direction of the model development and analysis.
B1. Research Question:
What is the impact of SquareFootage, SchoolRating, DistanceToCityCenter, AgeOfHome,
Fireplace, Garage, and HouseColor on home prices?
B2. Goal:
To develop a predictive model for estimating home prices based on housing characteristics
to guide pricing decisions.
,C. Data Preparation
The variables selected for the regression model are explained and justified based on domain
knowledge. Descriptive statistics and visualizations provide an understanding of the data
distribution and potential relationships. These steps ensure data suitability and adherence
to regression assumptions.
C1. Selected Variables:
Dependent Variable (1 total): Price
Independent Variables (7 total):
Quantitative: SquareFootage, SchoolRating, DistanceToCityCenter, AgeOfHome
Categorical: Fireplace, Garage, HouseColor
Justification: SquareFootage, SchoolRating, and DistanceToCityCenter are known drivers of
home value. AgeOfHome captures depreciation effects. Fireplace and Garage represent
buyer-valued amenities. HouseColor introduces stylistic appeal and categorical diversity,
ensuring a balanced model with both numerical and categorical predictors.
C2. Descriptive Stats
Categorical Variable Frequencies
Fireplace:
No: 0.74
Yes: 0.26
Garage:
No: 0.64
Yes: 0.36
HouseColor:
White: 0.21
Yellow: 0.20
Blue: 0.20
Green: 0.20
Red: 0.19
Fireplace Visualization
, Garage Visualization
HouseColor Visualization
