Please use San Joaquin County, in the Pacific area and California.
Please make sure you answer the questions complete.
Introduction
Describe the report: Give a brief description of the purpose of your report.
Define the question your report is trying to answer.
Explain when using linear regression is most
appropriate.
When using linear regression, what would you expect
the scatterplot to look like?
Explain the difference between response and predictor
variables in a linear regression to justify the selection of variables.
Data Collection
Sampling the data: Select
a random sample of 50 houses.
Identify your response and predictor variables.
Scatterplot: Create
a scatterplot of your response and predictor variables to ensure they are
appropriate for developing a linear model.
Data Analysis
Histogram: For
your two variables, create histograms.
Summary statistics: For
your two variables, create a table to show the mean, median, and standard
deviation.
Interpret the graphs and statistics:
Based on your graphs and sample statistics, interpret
the center, spread, shape, and any unusual characteristic (outliers,
gaps, etc.) for the two variables.
Compare and contrast the shape, center, spread, and
any unusual characteristic for your sample of house sales with the
national population. Is your sample representative of national housing
market sales?
Develop Your Regression Model
Scatterplot: Provide
a graph of the scatterplot of the data with a line of best fit.
Explain if a regression model is appropriate to
develop based on your scatterplot.
Discuss associations: Based
on the scatterplot, discuss the association (direction, strength, form) in
the context of your model.
Identify any possible outliers or influential points
and discuss their effect on the correlation.
Discuss keeping or removing outlier data points and
what impact your decision would have on your model.
Find r: Find
the correlation coefficient (r).
Explain
how the r value you calculated supports what you noticed
in your scatterplot.
Determine the Line of Best Fit. Clearly define your variables. Find and
interpret the regression equation. Assess the strength of the model.
Regression equation: Write
the regression equation (i.e., line of best fit) and clearly define your
variables.
Interpret regression equation: Interpret the slope and intercept in context.
Strength of the equation: Provide and interpret R-squared.
Determine the strength of the linear regression
equation you developed.
Use regression equation to make predictions: Use your regression equation to predict how much
you should list your home for based on the square footage of your home.
Conclusions
Summarize findings: In
one paragraph, summarize your findings in clear and concise plain language
for the CEO to understand. Summarize your results.
Did you see the results you expected, or was anything
different from your expectations or experiences?
What changes could support different results, or help
to solve a different problem
Provide at least one question that would be interesting for follow up research
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