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

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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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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
For a data set of chest sizes (distance around chest in inches) and weights (pounds) of twelve anesthetized bears that were measured, the linear correlation coefficient is r= 0.276. Use the table available below to find the critical values of r. Based on a comparison of the linear correlation coefficient r and the critical values, what do you conclude about a linear correlation? Click the icon to view the table of critical values of r. The critical values are Table of Critical Values of r IT Number of Pairs of Data n Critical Value of r 4 0.950 0.878 0.811 7 0.754 0.707 0.666 10 0.632 11 0.602 12 0.576
A company wishes to know whether advertising expenditure is related to sales volume. Using the past two years of data for these two variables, they determined the correlation coefficient is +0.93. This indicates 23 Multiple Choice 1 Dints awarded Scored a strong indirect relationship between advertising expenditure and sales volume. References a weak indirect relationship between advertising expenditure and sales volume. a strong direct relationship between advertising expenditure and sales volume. a weak direct relationship between advertising expenditure and sales volume.
For a data set of chest sizes (distance around chest in inches) and weights (pounds) of twelve anesthetized bears that were measured, the linear correlation coefficient is r= 0.732. Use the table available below to find the critical values of r. Based on a comparison of the linear correlation coefficient r and the critical values, what do you conclude about a linear correlation?
For a data set of brain volumes (cm3) and IQ scores of six males, the linear correlation coefficient is r=0.647. Use the table available below to find the critical values of r. Based on a comparison of the linear correlation coefficient r and the criticalvalues, what do you conclude about a linear correlation? LOADING... Click the icon to view the table of critical values of r. The critical values are nothing
The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technologgy was also given a foot length of 10.8 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 10.8 cm, what is the single value that is the best predicted height for that person? H Click the icon to view the technology output. The single value that is the best predicted height is cm. Technology Output (Round to the nearest whole number as needed.) The regression equation is Height - 67.5 +5.15 Foot Length Predictor Coef SE Coef Constant 67.52 11.07 6.10 0.000 Foot Length 5.1508 0.4817 10.69 0.000 S-5.50271 R-Sq = 73.18 R-Sq (adj) = 72.44 Predicted Values for New Observations New Obs Fit SE Fit 95 CI 954 PI 123.149 1.605 (119.732, 126.566) (111.996, 134.302) Values of Predictors for New…
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…
For a data set of chest sizes (distance around chest in inches) and weights (pounds) of seven anesthetized bears that were measured, the linear correlation coefficient is r=0.147. Use the table available below to find the critical values of r. Based on a comparison of the linear correlation coefficient r and the critical values, what do you conclude about a linear correlation? LOADING... Click the icon to view the table of critical values of r. The critical values are nothing. (Type integers or decimals. Do not round. Use a comma to separate answers as needed.) Since the correlation coefficient r is ▼ between the critical values in the right tail above the positive critical value in the left tail below the negative critical value , there ▼ is not is sufficient evidence to support the claim of a linear correlation.
A data set contains information on the grams of fat and number of calories in 25 different fast foods. The correlation coefficient between fat content and calories is determined to be 0.69. Calculate the p-value for the test of significance.
The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 17.8 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 17.8 cm, what is the single value that is the best predicted height for that person? Click the icon to view the technology output. The single value that is the best predicted height is (Round to the nearest whole number as needed.) cm. Technology Output The regression equation is Height = 53.3+4.25 Foot Length Predictor Constant Coef SE Coef 53.28 11.44 Foot Length 4.2537 0.4699 S = 5.50739 R-Sq=72.3% R-Sq (adj) = 71.6% T 4.66 9.05 Predicted Values for New Observations New Obs 1 Fit SE Fit 128.996 1.733 (124.309, 133.683) 95% CI Foot New Obs Length 1 17.8 0.000 0.000 Values of Predictors for New…

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