13.3 (a) When X= 0, the estimated expected value of Yis 16. (b) For each increase in the value Xby 1 unit, you can expect a decrease in an estimated 0.5 units in the value of Y. (c) 13ˆY13.13 r2= 0.75. So, 75% of the variation in the dependent variable can be explained by the variation in the independent variable. 13.15 Since SST= SSR+ SSEand since SSEcannot be a negative number, SSTmust be at least as large as SSR. 13.17 (a) r2= 0.9015. So, 90.15% of the variation in audit newsstand sales can be explained by the variation in reported newsstand sales. (b) 42.1859YXS(c) Based on (a) and (b), the model should be very useful for predicting audited sales. 13.35 (a) b0= 169.455, b1= –1.8579 (b) Yˆ= 76.56 (c) “No need to worry about plotting residuals because you won’t be asked to do that in the exam.”(d) The Durbin-Watson test statistic of 1.18<1.27. There is evidence of positive autocorrelation among the residuals. (e) The plot of the residuals versus temperature indicates that positive residuals tend to occur for the lowest
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This note was uploaded on 05/31/2011 for the course MGT C06 taught by Professor A.stawinoga during the Fall '10 term at University of Toronto.