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Regression analysis in Excel, Histograms & Scatterplot, etc.
Case Study #4 wil
Regression analysis in Excel, Histograms & Scatterplot, etc.
Case Study #4 will assess your ability to apply the
concepts of chapter 14 to conduct
simple and
multiple
regression analyses to create a prediction model for home prices based on
up to four
independent variables. You will calculate
various descriptive statistics, create summary tables,
create
various charts and develop five regression
prediction models. Finally, you will create a
written
report summarizing your findings. You will need to use the Data
Analysis ToolPak Add-in
as you did for the
previous two case studies.
The data
file contains data for a random sample of 1,000 houses
located in the greater
Wilmington,
DE area. The data fields included are as
follows:
· Home Price
· Living area (square feet)
· Number of bedrooms
· Number of bathrooms
· Age (years)
The data fields included are as follows:
Age (years)
In developing both your model and the report, address the items below.
1. There are numerous
variables that are believed to be predictors of housing prices, including the
ones in the data set for this project. Using the web, find the key variables that
determine home price including any not include in this data set.
2. Using
Data>Data Analysis>Descriptive Statistics in Excel, calculate the mean, median,
range and standard deviation of each variable and summarize the results in
table.
3. Using
Excel, create histograms for price of the home, living area (square feet) and
age of the home. Be sure to give each chart a title and label the axes clearly.
4. Using
Excel, create scatterplots of each variable with each other variable. Be sure
to give each chart a title and label the axes clearly.
5. Using
Data>Data Analysis>Correlation in Excel, calculate the correlation
coefficient each variable with each other variable.
6. Using
Data>Data Analysis>Regression in Excel, run 4 separate simple regression
models to predict the dependent variable (price of the home) with each of the
independent variables. Use an alpha level of 0.05 to determine significance.
7. Using
Data>Data Analysis>Regression in Excel, run a multiple regression model
to predict the dependent variable with all 4 independent variables. Use an
alpha level of 0.05 to determine significance.
8. In
Word, write a summary report of the findings that includes the tables, charts
and regression analyses from steps 1-7 and includes the following:
a. An
introductory paragraph summarizes the purpose of the analysis. Also include information
that found in your web search about the key variables that determine home price.
b. A
section (1 or more paragraphs) describing what the tabular data from step 2 indicate
about the central tendency, variability, and distribution of each variable. For
example, do the variables appear to be distributed in a symmetric or skewed pattern?
c. A
section (1 or more paragraphs) describing how the frequency histograms from step
3 support and clarify the findings of the tabular data. Include in this section
any evidence suggesting outliers in the data.
d. A
section (1 or more paragraphs) describing what the scatterplots from step 4 and
correlations from step 5 indicate about the relationship between the various
pairs of variables (e.g., are the variables related?, does the relationship
appear to be linear or nonlinear?, is the direction of the relationship
positive or negative?).
e. A
section (1 or more paragraphs) summarizing the findings of the 4 simple
regression
models from step 6. Which models (if any) show that the independent variable in
the model is a significant predictor of price of the home? Which models (if
any) show that the independent variable in the model is not a significant
predictor of price of the home? Which model is the best fitting? Which model is
the poorest fitting?
f. A
section (1 or more paragraphs) summarizing the findings of the multiple
regression model from step 7. Which variables in the model (if any) show that
are a significant predictor of price of the home? Which variables in the model
(if any) show that are not a significant predictor of price of the home? Does
the multiple regression model provide a better fit than the best fitting simple
regression model?
g. A
concluding paragraph summarizing the key findings of the analysis and making about
which model is the best fitting. Based on your web research, indicate any other
variables that are not included in the current best fitting model that might improve
the fit if they were included.
Submit a
single Excel workbook showing all work for Steps 2-7 and a Word document of
your summary report that addresses all parts of Step 8 and that also
includes/interweaves all supporting tables and charts from Steps 2-7 (to tell a
story with the data and through
visualization
means).
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