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 predictor (x) and response (y) variables in a linear regression

to justify the selection of variables.

Data Collection

Sampling the data:

Select a random sample of 50 houses. Describe how you obtained your sample data (provide

Excel formulas as appropriate).

Identify your predictor and response variables.

Scatterplot:

Create a scatterplot of your predictor and response variables to ensure they are appropriate for

developing a linear model./nData Collection

Sampling the data:

Select a random sample of 50 houses. Describe how you obtained your sample data (provide

Excel formulas as appropriate).

Identify your predictor and response variables.

Scatterplot:

Create a scatterplot of your predictor and response variables to ensure they are appropriate for

developing a linear model.

Data Analysis

Histogram:

Create a histogram for each of the two variables.

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 house sales and square footage.

Compare and contrast the center, shape, spread, and any unusual characteristic for your

sample of house sales with the national population (under Supporting Materials, see the/nCompare and contrast the center, shape, spread, and any unusual characteristic for your

sample of house sales with the national population (under Supporting Materials, see the

National Summary Statistics and Graphs House Listing Price by Region PDF).

Determine whether your sample is representative of national housing market sales.

Develop Your Regression Model

Scatterplot:

Provide a scatterplot of the variables with a line of best fit and regression equation.

Based on your scatterplot, explain if a regression model is appropriate.

Discuss associations:

Based on the scatterplot, discuss the association (direction, strength, form) in the context of/nIdentify 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.

Calculate r:

Calculate 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. For example, answer the questions: what does the

slope represent in this situation? What does the intercept represent? Revisit the Scenario

above.

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 assumed square footage of your home at

1500 square feet./nConclusions

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.