The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 32 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lbto be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Giventhat there are 32 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts?Click the icon to view the Minitab display.Minitab outputThe linear correlation coefficient is(Round to three decimal places as needed.)The regression equation isHighway = 50.9- 0.00539 WeightPredictorСoefSE CoefPConstant50.8562.85617.480.000Weight- 0.00539160.0007634- 7.590.000S= 2.10918R-Sq = 64.2%R- Sq(adj) = 61.7%Predicted Values for New ObservationsNewObsFitSE Fit95% CI95% PI126.5940.521(25.582, 27.606)(21.997, 31.191)Values of Predictors for New ObservationsNewObsWeight14500

We use the given output to find the required solution.

Answered: The Minitab output shown below was…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 229 266 359 480 530 15.9 15.6 15.5 15.3 14.9 Construct a scatterplot. Choose the correct graph below. OA. В. Ос. D. Ay 17- Ay 17- Ay 17- Ay 17- 16- 16- 16- 16- 15- 15- 15- 15- 14+ 14+ 14- 14+ 200 400 600 200 400 600 200 400 600 200 400 600 The linear correlation coefficient r is (Round to three decimal places as needed.)
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 231 266 359 480 532 15.9 15.7 15.4 15.2 14.8 Ay 17- Ay 17- Ay 17+ Ay 17- 16- 16- 16- 16- 15- 15- 15- 15- X 14- 14+ 14- 14- 200 400 600 200 400 600 200 400 600 200 400 600 The linear correlation coefficient r is (Round to three decimal places as needed.) The P-value is (Round to three decimal places as needed.) Because the P-value is than the significance level 0.05, there sufficient evidence to support the claim that there is a linear correlation between lemon imports and crash fatality rates for a…
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 228 264 358 482 531 15.9 15.7 15.5 15.3 14.9
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Is there sufficient evidence to support a claim of linear correlation? Yes O No Minitab output The regression equation is Highway = 50.8 -0.00508 Weight Predictor Coef SE Coef T P Constant 50.772 2.793…
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 26 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 3000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 26 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Minitab output The regression equation is Highway = 50.3 -0.00539 Weight Predictor Coef SE Coef Constant 50.288 2.998 Weight -0.0053868 0.0007773 |S=2.11773 R-Sq=64.0% R-Sq(adj) = 60.9% Predicted Values…
Listed below are the overhead widths (in cm) of seals measured from photographs and the weights (in kg) of the seals. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the critical values of r using a = 0.05, Is there sufficient evidence to conclude that there is a linear correlation between overhead widths of seals from photographs and the weights of the seals? Overhead Width 7.1 7.5 178 Click here to view a table of critical values for the correlation coefficient. 9.6 9.4 8.8 8.2 Weight 112 205 238 204 188 OA. YB. OC. KD D. Aweight (kg) 300- A weight (kg) Aweight (kg) 300- Aweight (kg) 300- 300- .. 100 100+ 100 7. rect: 0 10 10 10 10 width (om) width (cm) width (on) widn (om) The linear correlation coefficient is r= (Round to three decimal places as needed.) Enter your answer in the answer box and then click Check Answer. O parts remaining Check Answer वी
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a= 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 266 15.7 228 358 484 531 15.8 15.5 15.2 14.8 What are the null and alternative hypotheses? O B. Ho: p=0 O A. Ho: p#0 H1:p=0 H1:p0 H,: p#0 Construct a scatterplot. Choose the correct graph below. OA. B. Oc. OD. Ay 17- Ay 17- AY 17- Ay 17- 16- Q 16- 16- 16- 15- 15- 15- 15- 14- 14+ 14- 14- 200 400 600 200 400 600 200 400 6ỏ0 200 400 600 The linear correlation coefficient is r= (Round to three decimal places as needed.)
Listed below are the overhead widths (in cm) of seals measured from photographs and the weights (in kg) of the seals. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the critical values of r using a = 0.01. Is there sufficient evidence to conclude that there is a linear correlation between overhead widths of seals from photographs and the weights of the seals? Overhead Width 7.2 7.5 9.8 9.3 8.9 8.1 Weight 119 172 248 201 205 181 Click here to view a table of critical values for the correlation coefficient. Construct a scatterplot. Choose the correct graph below. A. В. OC. D. Aweight (kg) 300- Aweight (kg) 300- Aweight (kg) 300- Aweight (kg) 300- 100+ 1004 100- 100+ 10 7 10 7 10 7 10 width (cm) width (cm) width (cm) width (cm) The linear correlation coefficient is r= (Round to three decimal places as needed.) The critical values are r= (Round to three decimal places as needed. Use a comma to separate answers as needed.) Because the absolute…
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear corelation coefficient r, and find the P-value using a=0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? non imports Crash Fatality Rate What are the null and alternative hypotheses? OA. Họ: p=0 H: p>0 OB. He: p=0 H:p<0 OC. He: p=0 H;: p#0 OD. H: pr0 H: p=0 Construct a scatterplot. Choose the correct graph below. OA OB. Oc. OD. 17 17 16 16 15 Click to select your answer(s).
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 228 266 358 484 531 15.8 15.7 15.5 15.2 14.8 O A. Ho: pz0 H4:p= 0 O B. Ho: p=0 H1:p 0 H1: p#0 Construct a scatterplot. Choose the correct graph below. OA. YB. Oc. OD. Ay 17- Ay 17- Ay 17- Ay 17- 16- Q 16- 16- 16- 15- 15- 15- 15- X 14- 14- 14- 14+ 200 400 600 200 400 600 200 400 600 200 400 600 The linear correlation coefficient is r= - 0.971 (Round to three decimal places as needed.) The test statistic is t= (Round to three decimal places as needed.)
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? 484 534 Lemon Imports Crash Fatality Rate 229 15.8 266 15.7 359 15.5 15.3 14.8 OC. Ho: p=0 H₁: p=0 Construct a scatterplot. Choose the correct graph below. O A. Ay 17+ 16- 15- 14- 0 200 400 600 Q Q The linear correlation coefficient is r= (Round to three decimal places as needed.) The test statistic is t- (Round to three decimal places as needed.) The P-value is (Round to three decimal places as needed.) Because the P-value is OB. Ax 17+ 16- 15 14+ 6 than the significance level 0.05, there % 0 200 400 400 600 Q Q G OD. H₂:p#0 H₁:…
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 231 265 358 483 530 15.8 15.7 15.5 15.2 14.8 What are the null and alternative hypotheses? OA. Ho: p 0 H₁ p=0 OC. Ho p=0 H₁: p>0 Construct a scatterplot. Choose the correct graph below. OA. Ay 17- 16- Q do ° 15- ° 14+ 0 200 400 600 The linear correlation coefficient is r= (Round to three decimal places as needed.) B. Ho: p=0 H₁p 0 OD. Ho: p=0 H₁: p<0 B. COD. Ay 17- Ay 17- Ay 17+ Q о 16- 16- 16- Q 0 ° ° 15- 15- ° G 15- G 14- 14- 14+ 0 200 400 600 0 200 400 600 0 200 400 600

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