ࡱ> sur#` bjbj 7&? 6>>>R8R$v$R"s}s}s}$hd>s}Q}"s}s}s}sfʅ{{{s}j>{s}{{:o,|> ]}="}^ 0R;~> s}s}{s}s}s}s}s}js}s}s}s}s}s}s}RRRD RRRRRR  COMPUTER PROJECT ECON 306 Ashley Keller November 8, 2007 Part One: Model and Data Description The model I will be estimating will be data based on Real Estate. This data will look at the square footage of the home and give the price for the house. This model will be interesting because we will see a direct relationship between the two variables, with size and the independent variable and value as the dependent variable. There are no other factors in determining the price presented so the price of the house is solely based on square footage. Tables, See Appendix A. The standard deviation for this data is relatively high as is the standard error. Scatter-plot:  SHAPE \* MERGEFORMAT  Part Two: Regression Model being estimated: y = b0 + b1x1 My a priori expectation regarding the coefficients in the estimated equation are that the factors determined the value of the house are the details on the property that include; acres, number of bedrooms, number of bathrooms, neighborhood, age, upgrades, etc; which we dont see. However, for every square foot the house has it is increased by b1. Regression Results, See Appendix B. Sample Regression Equation: value = b0 + b1size or value = -50035 + 72.82size. Analysis of regression results: Linear relationships, See Appendix C. 95% confidence interval around the parameters estimated: For the intercept of b0 = -50035, it would be -64765 and -35305. For the x variable of b1 = 72.82, it would be 62.45 and 83.19. R2 statistic, Fit of the model: The R2 statistic for this data is 66.5%, this shows us that that percentage of the data fits under out equation that we have come up with, the other 33.5% does not follow the equation to come us with the value of the house, so there are other things about the house that determine the value but we cannot see what they are. Estimated Parameters jointly significant at 5% significant level, See Appendix D. Part Three: Conclusion While this information on real estate is very interesting and I have analyzed it best I could, I would say this is an ok model to use for this kind of analysis. The outcome of the value compared to size, while they do formulate into some kind of pattern and they do have a linear relationship, does not fit the normal curve which results in 33.5% of the 100 homes do not follow the equation I have calculated. Therefore, with 27,270 as the standard error, it would be hard to say the results are as expected and unless we can see the other factors which determine the value of a house it would be hard to estimate the value solely based on square footage.     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