By James G Smith

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What is your conclusion? (k) If none of the above tests is appropriate for testing the hypothesis that people of similar heights tend to marry each other, which test would you use? What is your conclusion based on this test? 11 Consider fitting a simple linear regression model through the origin, Y = P I X E , to a set of data using the least squares method. 49) is justified by theoretical or other physical and material considerations. 50). (c) Show that the residuals el, ep, . . , en will not necessarily add up to zero.

It only implies that they are not linearly related because the correlation coefficient measures only linear relationships. In other words, the Cor(Y, X ) can still be zero when Y and X are nonlinearly related. 2), yet Cor(Y, X ) = 0. Furthermore, like many other summary statistics, the Cor(Y,X) can be substantially influenced by one or few outliers in the data. , the same value of the correlation coefficient). 3. ’ An analysis based exclusively on an examination of summary statistics, such as the correlation coefficient, would have been unable to detect the differences in patterns.

X,. The equation may be used for several purposes. It may be used to evaluate the importance of individual SCOPE AND ORGANIZATION OF THE BOOK 17 predictors, to analyze the effects of policy that involves changing values of the predictor variables, or to forecast values of the response variable for a given set of predictors. Although the regression equation is the final product, there are many important by-products. We view regression analysis as a set of data analytic techniques that are used to help understand the interrelationships among variables in a certain environment.