Question One A Banking Analyst believes that living space, s, measured in square meters, is a good predictor of the price, p, of a property. The Analyst produces the figure below using sample of 200 properties collected in a big city. Price (p) 2,500,000- 2,000,000 1,500,000- 1,000,000- 50,000 2,000 4,000 6,000 Living space (s) i) Comment on the graph. The Banking Analyst fits a least squares regression line for the logarithmic price (y = In(p)) of the properties on the logarithm of the living space (x = In(s)), using the summary of x and y shown below. Σ Χ = 1,519.632 / Σ x2 = 11,583.92 040 Σ y = 2,616.206 Σ γ2 = 34,283.44μ Σ yx = 19,908.94 y=13.081x = 7.598 ii) Determine the Banking Analyst's least squares fitted regression line. iii) Calculate the coefficient of determination for the regression line determined in part (ii). iv) v) vi) vii) Calculate a two-sided 95% confidence interval for ẞ2, the slope of the true regression line. Test the hypothesis Ho: ẞ2=1vs H₁ ẞ21 at the 5% significance level. Determine the 95% confidence interval for the expected price of a property with 1,930 square meters of living space. Determine the 95% production interval for the price of a property with 1,930 square meters of living space. The Banking Analyst fitted another least square regression line for the price of the properties depending on the square meters of living space and also the year the property was built. The coefficient of determination for this regression line is R2 = 60%. viii) Comment on the result from this second regression line and your answer to part (iii).