please help.

Suppose IQ scores were obtained for 20 randomly selected sets of twins. The 20 pairs of measurements yield x = 101.35, y = 101.75, r = 0.912, P-value =0.000, and y= 7.5+ 0.93x, where x represents the IQ score of the twin born second. Find the best predicted value of y given that the twin born second has an IQ of 90? Use a significance level of 0.05. 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.)Listed below are amounts of court income and salaries paid to the town justices. All amounts are in thousands of dollars. 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 court incomes and justice salaries? Based on the results, does it appear that justices might profit by levying larger fines? Court Income 64.0 406.0 1568.0 1132.0 271.0 251.0 110.0 150.0 33.0 Justice Salary 30 92 57 45 62 26 27 17 07 800 1600 800 1600 800 1600 800 1600 Court Income Court Income Court Income Court Income 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 less than the significance level 0.05, there is sufficient evidence to support the claim that there is a linear correlation between court incomes and justice salaries for a significance level of a = 0.05. Based on the results, does it appear that justices might profit by levying larger fines? O A. It does not appear that justices might profit by levying larger fines. O B. It does appear that justices might profit by issuing smaller fines. C. It does appear that justices might profit by levying larger fines. O D. It appears that justices profit the same despite the amount of the fines.0 Find the regression equation, let'ting overhead width be the predictor (x) variable Find the best predicted weight of a seal ifthe overhead wtdth measured from a photograph is 2 2 cm Can the prediion be correct? What is wrong wrth predicting the weight In this case') Use a signicance level of 0 05 Overhead Width (cm) 7,1 8,2 9 8 9 4 7 4 8 5 E1 Weight (kn) 141 218 282 238 174 229 5 Click the icon to View the crrtical values at the Pearson correlation coefcient r The regression equation 5;: + x (Round to one decimal place as needed ) The best predicted weight for an overhead Width of 2 2 cm IS kg (Round to one decimal place as needed ) Can the prediction be correct? What is wrong With predicting the weight in this case? 0 A. The predidion cannot be correct because a negative weight does not make sense and because there is not sutcient evidence of a linear correlation, O B. The predidion cannot be correct because there is not sufcient evidence at a linear correlation. The width in this case is beyond the scope of the available sample data. 0 (I. The prediction cannot be correct because a negative weight does not make sense The Width in this case is beyond the scope ot the available sample data 0 D. The predidion can be curred There is nothing wrong with predicting the weight in this case