If X and Y are two regression lines with equal mean and if 1- a Y = aX + b and X = aY + B. Prove that and also %3D %3D B 1-a find out the joint mean of two variables.
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?A regression line describes the relationship between the dependent and independent variables and can be used to estimate specific points for x or y, provided an individual is supplied with the values of all the other variables but one. True or falseHere, mean of X is 3 and the mean of Y is 7. The regression line that predicts Y from X necessarily goes through the point (3,7). True False
- Suppose that a least squares regression line equation is ˆy = 1.65 − 2.20x and the actual y value corresponding to x = 10 is −19, what is the residual value corresponding to y = −19?Suppose the equation for a regression line is y =4x + 6. If x = 5, what is the predicted corresponding value for y?If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that y and x are positively correlated. True or false?
- If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that y and x are positively correlated.If other factors are held constant, if the Pearson correlation between X and Y is r = 0.50, then the regression equation will produce more accurate predictions than would be obtained if r = 0.70. True or false?
- For a set of data: x = (0,1,2,3,4,5,6) and y=(36, 28, 25, 24, 23, 21, 19), is it wise to use a linear regression to extrapolate data for x = 50? Solution: Since the coefficient of determination is 0.8582, the linear model is a reasonably good fit for the data, so extrapolation for any x-value is acceptable. What is wrong with this solution?If the correlation of the X's is equal to 1 or -1, then the regression cannot process. Why?One set of 20 pairs of scores, X and Y values, produces a correlation of r = 0.70. If SSY = 150, calculate the standard error of the estimate for the regression line