all I need is A,B and C answered. There is no other data provided, I am showing it all. Figure 4.1 is the plots right above the text.

3. The Sunday April 15, 2007 issue of the Houston Chronicle included a section devoted to real estate prices in Houston. In particular, data are presented on the 2006 median price per square foot for 1922 subdivisions. The data (HoustonRealEstate.txt) can be found on the book web site. Interest centers on developing a regression model to predict Y = 2006 median price per square foot from Xu: = %New Homes (i.e., of the houses that sold in 2006, the percentage that were built in 2005 or 2006) x3 = %Foreclosures (i.e., of the houses that sold in 2006, the percentage that were identified as foreclosures) Table 4.3 Data on salaries Years of experience as a full professor 0 2 4 6 8 12 17 22 28 34 Sample size, 17 33 19 25 18 60 58 31 34 19 Third quartile ($) 101,300 111,303 98,000 124,000 128,475 117,410 115,825 134,300 128,066 164,700 1.0- 400 400 0.8 300 300 0.6-16 YI YI 200 200 0.4 100- 0.2- 100- 0.0- 0.0 0.8 0.0 0.4 0.8 0.0 0.8 0.4 xti 0.4 xi x2i 12 lo 3.0-1 10 8 6 2.0- Standardized Residuals 808 Square Root(Standardized Residuals) 4 O 1.0 2 0 -2 0.0-1 0 20 60 0 20 60 Fitted Values Fitted Values Figure 4.1 Plots associated with model (4.6) for the i = 1, ... 1922 subdivisions. The first model considered was Y = B,+B,X1 +B,X, te (4.6) Model (4.6) was fit used weighted least squares with weights, W. = n; where n = the number of homes sold in subdivisioni in 2006. Output from model (4.6), in the form of plots, appears in Figure 4.1. (a) Explain it is necessary to use weighted least squares to fit model (4.6) and why w;= n, is the appropriate choice for the weights. (b) Explain why (4.6) is not a valid regression model. C) Describe what steps you would take to obtain a valid regression model (Figure 4.1). 3. The Sunday April 15, 2007 issue of the Houston Chronicle included a section devoted to real estate prices in Houston. In particular, data are presented on the 2006 median price per square foot for 1922 subdivisions. The data (HoustonRealEstate.txt) can be found on the book web site. Interest centers on developing a regression model to predict Y = 2006 median price per square foot from Xu: = %New Homes (i.e., of the houses that sold in 2006, the percentage that were built in 2005 or 2006) x3 = %Foreclosures (i.e., of the houses that sold in 2006, the percentage that were identified as foreclosures) Table 4.3 Data on salaries Years of experience as a full professor 0 2 4 6 8 12 17 22 28 34 Sample size, 17 33 19 25 18 60 58 31 34 19 Third quartile ($) 101,300 111,303 98,000 124,000 128,475 117,410 115,825 134,300 128,066 164,700 1.0- 400 400 0.8 300 300 0.6-16 YI YI 200 200 0.4 100- 0.2- 100- 0.0- 0.0 0.8 0.0 0.4 0.8 0.0 0.8 0.4 xti 0.4 xi x2i 12 lo 3.0-1 10 8 6 2.0- Standardized Residuals 808 Square Root(Standardized Residuals) 4 O 1.0 2 0 -2 0.0-1 0 20 60 0 20 60 Fitted Values Fitted Values Figure 4.1 Plots associated with model (4.6) for the i = 1, ... 1922 subdivisions. The first model considered was Y = B,+B,X1 +B,X, te (4.6) Model (4.6) was fit used weighted least squares with weights, W. = n; where n = the number of homes sold in subdivisioni in 2006. Output from model (4.6), in the form of plots, appears in Figure 4.1. (a) Explain it is necessary to use weighted least squares to fit model (4.6) and why w;= n, is the appropriate choice for the weights. (b) Explain why (4.6) is not a valid regression model. C) Describe what steps you would take to obtain a valid regression model (Figure 4.1)