Consider the data,X, 14Y, 48 6 10 12The estimated regression equation for these data is ý- 2.60 + 1.80x.(a) Compute SSE, SST, and SSR using equations SSE - I(r, - ), sST - E(y, - v), and SSR = E(, - .SSE 7.6SST =40SSR = 324(b) Compute the coefficient of determination ?.Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.)O The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line.O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line.O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares lineThe least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares ine.(c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)

For the given data Find ( b ) r^2 =? Comment on the goodness fit ( c ) Correlation = ?

Answered: Consider the data. x, 1 3. 4 Y:4 S 6 10…

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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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?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.4 6.6 3.9 2.4 2.4 2.2 0.6 yy 14 10.9 9.4 7.5 6 5.8 5.8 4.4 xx = thousands of automatic weaponsyy = murders per 100,000 residents Use excel to determine the equation of the regression line. (Round to 2 decimal places)Determine the regression equation in y = b0 + bx(x) form and write it below. A) How many murders per 100,000 residents can be expected in a state with 10.7 thousand automatic weapons? B) How many murders per 100,000 residents can be expected in a state with 2.1 thousand automatic weapons?
We wish to predict the salary for baseball players (y) using the variables RBI (x1) and HR (x2), then we use a regression equation of the form yˆ=b0+b1x1+b2x2 HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars.
We wish to predict the salary for baseball players (yy) using the variables RBI (x1x1) and HR (x2x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2y^=b0+b1x1+b2x2. HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars. RBI's HR's Salary (in millions) 108 38 28.050 86 31 27.500 59 25 25.000 119 31 25.000 103 39 24.050 44 15 23.125 49 11 23.000 111 30 22.750 87 31 22.125 90 18 21.857 49 7 21.667 70 21 21.571 108 35 21.500 56 9 21.143 84 38 21.119 80 14 20.802 17 7 20.000 79 24 20.000 91 31 20.000 97 29 20.000 57 13 18.500 44 8 18.000 104 32 18.000 86 27 18.000 100 25 17.454 62 20 17.000 58 20 17.000 100 29 16.083 127 38 16.000 83 29 16.000 59 30 16.000 54…
We wish to predict the salary for baseball players (yy) using the variables RBI (x1x1) and HR (x2x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2y^=b0+b1x1+b2x2. HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars. The following is a chart of baseball players' salaries and statistics from 2016. Player Name RBI's HR's Salary (in millions) Miquel Cabrera 108 38 28.050 Yoenis Cespedes 86 31 27.500 Ryan Howard 59 25 25.000 Albert Pujols 119 31 25.000 Robinson Cano 103 39 24.050 Mark Teixeira 44 15 23.125 Joe Mauer 49 11 23.000 Hanley Ramirez 111 30 22.750 Justin Upton 87 31 22.125 Adrian Gonzalez 90 18 21.857 Jason Heyward 49 7 21.667 Jayson Werth 70 21 21.571 Matt Kemp 108 35 21.500…
We wish to predict the salary for baseball players (y) using the variables RBI (x1) and HR (x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2y^=b0+b1x1+b2x2. HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars. The following is a chart of baseball players' salaries and statistics from 2016. Player Name RBI's HR's Salary (in millions) Adrian Beltre 104 32 18.000 Justin Smoak 34 14 3.900 Jean Segura 64 20 2.600 Justin Upton 87 31 22.125 Brandon Crawford 84 12 6.000 Curtis Granderson 59 30 16.000 Aaron Hill 38 10 12.000 Miquel Cabrera 108 38 28.050 Adrian Gonzalez 90 18 21.857 Jacoby Ellsbury 56 9 21.143 Mark Teixeira 44 15 23.125 Albert Pujols 119 31 25.000 Matt Wieters 66 17 15.800 Logan…
The number of murders and robberies per 100,000 population for a random selection of states are shown. Find the equation of the regression line y'=ax+b, and predict the number of robberies when x=4.5 murders murders,x: 2.4,2.7,5.6,2.6,2.1,3.3,6.6,5.7 robberies,y : 25.3,14.3,151.6,91.1,80,49,17.3,45.8
For x={1 2 3 4 5} and y={2 1 4 3 6} use normal equation (c =(ATA)-1ATy) to find with: a-) linear regression coefficients, b-) the linear regression equation, c-) residel sum of squares(RSS)
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.3 8.3 6.8 3.4 2.7 2.7 2.5 0.4 yy 13.5 11 9.6 6.9 6 6.4 5.9 4.1 xx = thousands of automatic weaponsyy = murders per 100,000 residents Use your calculator to determine the equation of the regression line. (Round to 2 decimal places)Determine the regression equation in y = ax + b form and write it below. A) How many murders per 100,000 residents can be expected in a state with 2.4 thousand automatic weapons?Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 7.3 thousand automatic weapons?Answer = Round to 3 decimal places.
Given are five observations collected in a regression study on two variables. xi 2 6 9 13 20 yi 9 19 7 26 21 Develop the estimated regression equation for these data. ŷ = (c) Use the estimated regression equation to predict the value of y when x = 6.
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 =

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