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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4Olympic 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 surgery intern has conducted a study of the sleeping habits of her colleagues and has developed a following regression equation: y-hat = 6 + 0.1X, where X is the number of hours working on one shift, and Y is the number of hours sleeping at night after that shift. Yvette worked 10 hours and slept 8 hours. What is Yvette’s residual? 0.1 1 6 7
- The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.3 7 3.4 2.4 2.6 2.6 0.9 yy 14 11.1 10.1 6.7 6.1 6.4 6.5 4.9 xx = thousands of automatic weaponsyy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places):____________________ A) How many murders per 100,000 residents can be expected in a state with 10.8 thousand automatic weapons? Round to 3 decimal places. Answer = B) How many murders per 100,000 residents can be expected in a state with 4.4 thousand automatic weapons? Round to 3 decimal places. Answer =The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.1 6.7 3.3 2.7 2.3 2.2 0.3 yy 13.8 10.7 9.9 6.6 6.1 5.9 6.1 4.5 xx = thousands of automatic weaponsyy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 6.4 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 6.7 thousand automatic weapons? Answer = Round to 3 decimal places.The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.3 8.6 6.9 3.9 2.8 2.5 2.1 0.4 yy 13.8 11 9.7 7 6.7 6.5 6 4.1 xx = thousands of automatic weaponsyy = murders per 100,000 residentsDetermine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 1.1 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 6.1 thousand automatic weapons? Answer = Round to 3 decimal places.
- A fitted linear regression model is (y=10+2x ). If x = 0 and the corresponding observed value of y = 9, the residual at this observation is:The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.3 7 3.8 2.6 2.6 2.7 0.9 yy 13.7 11.4 10 7.2 6.3 6 6.2 5.1 xx = thousands of automatic weaponsyy = murders per 100,000 residentsDetermine the regression equation in y = a + bx form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 2.4 thousand automatic weapons? Answer = Round to 4 decimal places. B) How many murders per 100,000 residents can be expected in a state with 2.3 thousand automatic weapons? Answer = Round to 4 decimal places.The following estimated regression model was developed relating yearly income (Y in $1,000s) of 30 individuals with their age in years (X1) and their gender (X2) (0 if male and 1 if female). The yearly income of a 24-year-old female individual is