Southern New Hampshire University
MAT240: Applied Statistics – Final Project
MAT240 is a competency-based course focused on applying
statistical techniques to solve real-world problems. The final
course grade is typically determined by two major projects,
not a traditional final exam. Both projects require you to
analyze a real estate dataset, perform statistical analyses,
and write professional reports.
EXAM OVERVIEW
MAT240 Applied Statistics teaches you to spot trends, dispel
misconceptions, and make smart decisions using data. You
will use real data sets from disciplines like business, biology,
and education, leveraging data analysis and visualization
software to answer questions.
Core Competencies Assessed in the Final Projects:
• Apply statistical techniques to address research
problems
, • Perform regression analysis to address an authentic
problem
• Perform hypothesis testing to address an authentic
problem
PART 1: PROJECT ONE – LINEAR REGRESSION MODEL
Scenario: You have been hired by the D. M. Pan National
Real Estate Company to develop a model to predict housing
prices for homes sold in 2019. The CEO wants to determine
if square footage is a good predictor of listing price.
Key Deliverables of Project One
1. Introduction
• Purpose: Describe the purpose of the report and the
question it answers
• When to use linear regression: Explain that linear
regression is appropriate when the relationship
between variables is linear, errors are normally
distributed with equal variance, and observations are
independent
, • Variables: Explain the difference between the predictor
(x) variable (square footage) and the response
(y) variable (listing price)
2. Data Collection
• Random Sample: Select a random sample of 50
houses from the dataset. Describe your sampling
method (e.g., using Excel's =RAND() function)
• Scatterplot: Create a scatterplot of your predictor and
response variables to ensure they are appropriate for
developing a linear model
3. Data Analysis
• Histograms: Create a histogram for each of the two
variables
• Summary Statistics: Table showing mean, median, and
standard deviation for both variables
• Interpretation: Interpret the center, spread, shape, and
any unusual characteristics (outliers, gaps) for your
sample
• Comparison to National Data: Compare your sample's
statistics to the national population statistics provided
in the course materials
MAT240: Applied Statistics – Final Project
MAT240 is a competency-based course focused on applying
statistical techniques to solve real-world problems. The final
course grade is typically determined by two major projects,
not a traditional final exam. Both projects require you to
analyze a real estate dataset, perform statistical analyses,
and write professional reports.
EXAM OVERVIEW
MAT240 Applied Statistics teaches you to spot trends, dispel
misconceptions, and make smart decisions using data. You
will use real data sets from disciplines like business, biology,
and education, leveraging data analysis and visualization
software to answer questions.
Core Competencies Assessed in the Final Projects:
• Apply statistical techniques to address research
problems
, • Perform regression analysis to address an authentic
problem
• Perform hypothesis testing to address an authentic
problem
PART 1: PROJECT ONE – LINEAR REGRESSION MODEL
Scenario: You have been hired by the D. M. Pan National
Real Estate Company to develop a model to predict housing
prices for homes sold in 2019. The CEO wants to determine
if square footage is a good predictor of listing price.
Key Deliverables of Project One
1. Introduction
• Purpose: Describe the purpose of the report and the
question it answers
• When to use linear regression: Explain that linear
regression is appropriate when the relationship
between variables is linear, errors are normally
distributed with equal variance, and observations are
independent
, • Variables: Explain the difference between the predictor
(x) variable (square footage) and the response
(y) variable (listing price)
2. Data Collection
• Random Sample: Select a random sample of 50
houses from the dataset. Describe your sampling
method (e.g., using Excel's =RAND() function)
• Scatterplot: Create a scatterplot of your predictor and
response variables to ensure they are appropriate for
developing a linear model
3. Data Analysis
• Histograms: Create a histogram for each of the two
variables
• Summary Statistics: Table showing mean, median, and
standard deviation for both variables
• Interpretation: Interpret the center, spread, shape, and
any unusual characteristics (outliers, gaps) for your
sample
• Comparison to National Data: Compare your sample's
statistics to the national population statistics provided
in the course materials
