Project II: Regression AnalysisCreatea 4- to 5-slide PowerPointincluding:

One slide introducing your project (approximately 1 minute):

One to two slides of visuals (approximately 2-3 minutes).

oThese should be clear, neat, organized, and labeled. oVisuals must include the correlation coefficient, a scatter plot without regression

line, a scatter plot with the regression line, the regression equation, R-squared,

etc.

oShow the price prediction/forecast (based on 2,100 sf home).One slide for a conclusion (approximately 1 minute).

oDiscuss what you learned from this project.Include detailed speaker notes for each slide.

Considerthe following questions when creating your speaker notes:

What city did you choose, why?

Did you have any a prior feelings about how the variables would correlate with each

other (strong, weak, none)?

What is the explanatory variable, what is the response variable? Make sure

"explanatory" variable is the "x-axis".

What a linear model a good fit? Do you think some other non-linear model might fit

better?

Did your opinion of the relationship change from having the scatter plot alone to having

the scatter plot with regression line (and R-squared)?

What proportion of the variation in listing price is determined by variation in the square footage? What proportion of the variation in listing price is due to other factors?

Check the coefficients in your summary output. What is the regression equation relating square footage to listing price?

Using the regression equation that you designated above, what is the predicted sales price for a house of 2100 square feet?

Directions: Searchthe Internet for a realty site.

When all of the listings populate, make sure that the sort criteria reads "New Listings." This ensures that you are searching a random cross-section of listings rather than favoring one price range.