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We cannot be sure of this relationship based on these

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be lurking third variables affecting the data. We cannot be sure of this relationship based on these two variables alone. Question 2 – Regression and the Market Model (Calculations from Summary Statistics) a) r 2 =(0.6217^2)*100=38.651089 38.7% of the variation in RIM stock is explained by the linear relationship with the market as a whole. b) -The slope of the regression line indicates the change in Y for a unit change in X. In this example, the slope indicates how much the monthly percent total return of RIM changes for a unit change in the monthly return on the NASDAQ index (the market). -The y-intercept of the regression line indicates the value of Y when X is 0. In this example, the y-intercept indicates what the return of RIM is when the return on the index is 0. c) Based on the statistics, I don’t think the monthly return on the NASDAQ index would be very effective in predicting the monthly percent total return on RIM stock, because as we calculated in part a), only 38.7% of the variation of Y is explained by X. d) An investor should prefer stocks with beta > 1 when the market is rising because the positive beta indicates the stocks’ return will rise when the market rises. They should also prefer stocks with beta < 1 when the marketing is falling because this means that when the market returns are lower, individual stocks’ return will be higher if it’s negatively correlated. Question 3 – Residual Plots - Halifax Real Estate Listings (yes, again) HURRAH! a) b) The residual plot shows that the regression model does not provide an adequate fit because the data points are more clustered on one side of the horizontal axis than the other, suggesting a skewness in the Y-variable.
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