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 thevalue 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 linearcorrelation between overhead widths of seals from photographs and the weights of the seals?Overhead WidthWeightClick here to view a table of critical values for the correlation coefficient.n300Aweight (kg) Q100+7width (cm)10Q7.11117.71729.6239Aweight (kg)300+100+The linear correlation coefficient is r=(Round to three decimal places as needed.)9.4204+width (cm)108.6196Q8.1183Aweight (kg)300-1007width (cm)10QAweight (kg)300-100+width (cm)10Q 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 value of the linear correlation coefficient isthan the positive critical value, theresufficient evidence to support theclaim that there is a linear correlation between overhead widths of seals from photographs and the weights of the seals for a significance level ofα=0.05.

Given is a question based on correlation b/w weight (in kg) and overhead width (in cm) of seals.

Answered: The linear correlation coefficient is…

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 Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation y = 40.86-0.45x. This line is shown in the scatter plot below. Mileage, x (in thousands) 29.7 27.8 26.8 24.2 21.1 23.0 24.3 15.4 37.7 23.6 34.4 27.8 23.5 20.9 259 Used selling price, y (in thousands of dollars) 27.4 29.4 31.2 30.1 31.7 31.7 27.2 34.2 23.4 28.2 26.3 26.5 33.7 30.6 267 Used selling price (in thousands of dollars) 40- 35+ 30- 25- 20 THIS 15 X 20 X X X 30 Mileagex (in thousands) X 35 40
A company wishes to estimate a regression line for the relationship between sales and advertising. а. To arrive at its decision regarding the best model to use, the company has calculated the following three correlation coefficients. i. ii. The sales in any month depend on that month's advertising with r = 0.28 The sales in any month depend on 50% of the previous month's advertising and 50% of that month's advertising with r= 0.68 The sales in any month depend on the previous month's advertising with r = 0.92 iii. Interpret each of the above correlation coefficients and state which of the suggested models you would choose as the basis for predicting sales. Justify your answer.
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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…
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The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation Ŷ=42.80-0.53x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)
please do this urgently
Given that the means of x and y are 64 and 66 , their standard deviations are 2.8 and 3.2 respectively and the coefficient of correlation between them is 0.5. 1) write down the regression lines.

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