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Part Two (worth 30 points)

You have been given a dataset at work and must complete an analysis to determine whether the price of real estate in the United States can be predicted. The dataset contains what you will need in order to complete a regression analysis in Microsoft Excel and R-Studio. Included in this dataset are the US Home Pricing Index (USHPI), US Money Supply (M2), US Unemployment Rate (UNRATE), the Yield Curve (YC), and historical home prices. Complete below and upload to Blackboard with your supporting excel file and R-script.

List the Independent Variable:

List the Dependent Variable:

Is this Multiple Regression or Simple Regression?

MS Excel Instructions

Create a regression analysis at the 95% confidence level and identify the following:

· R-Squared

· Degrees of Freedom

· Calculated t-value

· Critical t-value

· Durbin Watson statistic

· Regression model equation

R-Studio Instructions

Create an R script that performs the following:

· Retrieves your dataset file

· Assign a variable name to your dataset

· Write a function that identifies the dimension of your dataset

· Write a function that identifies the headers in the columns of your dataset

· Assign variables to your dependent and independent variables

· Create a regression function

· Write a function that summarizes your regression

Interpret Results:

Indicate the following:

· Can you assume normal distribution? Why or why not?

· Do you have a valid regression model? Why or why not? Be sure to support your response with evidence from your models