STATS Part IV. Simple Regression

| August 30, 2017

Part IV. Simple Regression

Let’s say that we wanted to be able to predict the selling price of a home based on its area in sq. ft. Using this sample data, perform a simple-linear regression to determine the line-of-best fit. Use the Area as your x (independent) variable and Selling Price as your y (response) variable.Use 3 places after the decimal in your answer. Refer to page 13 in the Stat Disk User’s Manual.

Paste your results here:

Answer the following questions related to this simple regression

What is the equation of the line-of-best fit? Insert the values for boand b1from above into y = bo+ b1x.

What is the slope of the line? What does it tell you about the relationship between the Area and Selling Price data? Be sure to specify the proper units.

What is the y-intercept of the line? What does it tell you about the relationship between Area and Selling Price?

What would you predict the selling price of a house that is 3500 sq ft? Show your calculation and round to the nearest cent.

Let’s say you want to pay $254,000 for a house. Based on the linear regression relationship between area and selling price found above, what size house could you afford? Round to the nearest whole number.

Find the coefficient of determination (R2value) for this data. What does this tell you about this relationship?

[Hint: see definition on Page 311.]

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