(1.)

Identify the correlation coefficient, r. rE Round to three decimal places as needed.)Identify the critical value(s). (Round to three decimal places as needed.) A. There are two critical values at r= + O B. There is one critical value at r =Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? Choose the correct answer below and, if necessary, fill in the answer box within your choice. (Round to three decimal places as needed.) O A. Yes, because the absolute value of the test statistic exceeds the critical value. O B. No, because the absolute value of the test statistic exceeds the critical value. O C. Yes, because the test statistic falls between the critical values. O D. No, because the test statistic falls between the critical values. O E. The answer cannot be determined from the given information.1r.I"|.|'hen measuring an anesthetized bear. is it easier to measure chest size than weight? If so, does it appear that a measured chest size can be used to predict the weight? [:1 A. [:1 E. r\": C. [:1 D. Yes, it is easier to measure a chest size than a weight because measuring weight would require lifting the bear onto the scale. The chest size could not be used to predict weight because there is too much 1.rariancae in the weight of the bears. Yes, it is easier to measure a chest size than a weight because measuring weight would require lifting the bear onto the scale. The chest size could not be used to predict weight because there is not a linear correlation between the two. No. it is easier to measure weight than chest size because the chest is not a flat surface. Yes, it is easier to measure a chest size than a weight because measuring weight would require lifting the bear onto the scale. The chest size could be used to predict weight because there is a linear correlation between the two. Fifty-four wild bears were anesthetized, and then their weights and chest sizes Correlation Results were measured and listed in a data set. Results are shown in the accompanying Correlation coeff, r: 0.965577 display. Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuring Critical r: + 0.2680855 an anesthetized bear, is it easier to measure chest size than weight? If so, does it P-value (two tailed): 0.000 appear that a measured chest size can be used to predict the weight? Use a significance level of o = 0.05. Determine the null and alternative hypotheses. Ho: P H : p (Type integers or decimals. Do not round.)The linear correlation coefficient is r= (Round to three decimal places as needed.)Determine the null and alternative hypotheses. Ho: P Hy: p (Type integers or decimals. Do not round.)The test statistic is t = Round to two decimal places as needed.)\fBecause the P-value of the linear correlation coefficient is the significance level, there sufficient evidence to support the claim that there is a linear correlation between Internet users and scientific award winners.Listed below are numbers of Internet users per 100 people and numbers of scientific award winners per 10 million people for different countries. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Use a significance level of o = 0.01. Internet Users 79.1 78.2 57.8 67.1 77.6 38.8 Award Winners 5.3 9.1 3.2 1.6 10.6 0.1 Construct a scatterplot. Choose the correct graph below. O A. O B. O C. OD. O 12- 12- 12 12- Award Winners Award Winners Award Winners Award Winners 30 90 30 90 30 90 30 90 Internet Users Internet Users Internet Users Internet Users50 0.279 0.361 60 0.254 0.330 70 0.236 0.305 80 0.220 0.286 90 0.207 0.269 100 0.196 0.256 n o = 0.05 o = 0.01Critical Values of the Pearson Correlation Coefficient r Critical Values of the Pearson Correlation Coefficient r n D = 0.05 D = 0.01 NOTE: To test Ho: p = 0 0.950 0.990 against Hy: p #0, reject Ho 5 0.878 0.959 if the absolute value of r is 6 0.811 0.917 greater than the critical 0.754 0.875 value in the table. 0.707 0.834 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482 0.606 18 0.468 0.590 19 0.456 0.575 20 0.444 0.561 25 0.396 0.505 30 0.361 0.463 35 0.335 0.430 40 0.312 0.402 45 0.294 0.378Suppose IQ scores were obtained for 20 randomly selected sets of siblings. The 20 pairs of measurements yield x = 103.81, y = 100.95, r= 0.949, P-value = 0.000, and y = 2.98 + 0.94x, where x represents the IQ score of the younger child. Find the best predicted value of y given that the younger child has an IQ of 110? Use a significance level of 0.05. E Click the icon to view the critical values of the Pearson correlation coefficient r. The best predicted value of y is (Round to two decimal places as needed. )Identify a characteristic of the data that is ignored by the regression line. O A. There is an influential point that strongly affects the graph of the regression line. O B. There is no trend in the data. O C. The data has a pattern that is not a straight line. O D. There is no characteristic of the data that is ignored by the regression line.Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. x 14 12 5 4 9 11 6 7 10 13 8 y 13.01 13.64 7.06 5.01 12.50 13.54 8.84 10.34 13.15 13.46 11.55 V x (Round to two decimal places as needed.)How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O) B. The result is very different from the actual height of 1776 mm. O C. The result is exactly the same as the actual height of 1776 mm. O D. The result does not make sense given the context of the data.Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.2 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.0 278.2 253.2 258.7 279.1 258.1 274.0 261.8 Height 1784.9 1770.7 1676.0 1646.2 1858.9 1709.7 1788.7 1736.7 The regression equation is y = X. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.)