Perform a step-wise regression to reduce the number of independent variables and produce a final regression model. Write out the forecast equation for your final model. (45 pts) 6. Now that you have a final model, present a pitch as to why your model is better than Venus' model and whether Bayou City should use your model to invest in the Houston market. (20 pts

households). She also included home sales data like the median and average home sales price and number of single-family home sales. Lastly, she included the WTI crude price and unemployment rate bringing the total to seven (7) predictor variables. Table 1 shows the data set used by Venus to develop her multiple regression model. TABLE 1: Houston Rental Market Data Year Average Vacancy Rate Renter Total property Average Home Sales Home Median WT Crude Unemployment Sales Price 2017 $1,091 9.73% 94,818 Price $291,340 $220,000 508 5.8 2016 $1,084 7.28% 40.83% 91,530 $283.133 $221,000 43.29 4.8 2015 $1,069 6.46% 41.33% 88,764 $280,290 $212,000 48.66 4.6 2014 $1,020 7.13% 40.94% 91,439 5270,182 $199,000 93.17 5.5 2013 5964 3.39% 39.87% $248,591 $180,000 97.98 6.5 2012 5956 10.17% 38.65% 74,116 $225.330 $164,500 94.05 7.2 2011 5941 11.64% 38.44% 63,606 $213,723 $155,000 94.88 8.3 2010 5961 13.76% 37.16% 61,005 $211,765 $153,900 79.48 87 2009 5984 12.27% 37.74% 63,803 $203,625 $153,000 61.95 6.2 2008 5971 12.55% 36.63% 69,336 $208,266 $152,000 99.67 4.5 2007 5924 13.57% 2006 5913 10.91% 36.12% 35.52% 83,736 87,576 $206,393 $198,410 $152,000 72.34 4.6 $149,079 66.05 5.7 The Bayou City board was concerned about the predictive nature of the variables chosen by Venus and whether