Hello, could you help me with these problems:

The accompanying table lists the ages of acting award winners matched by the years in which the awards were won. 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. Should we expect that there would be a correlation? Use a significance level of a = 0.05 Construct a scatterplot. Choose the correct graph below. O A. O B. 7071 Best Actor (years) Best Actor (years) Best Actresses and Best Actors 20+ 207 70 20 70 Best Actress (years) Best Actress (years) Best Actress 27 31 30 59 31 34 45 29 64 22 46 56 O C. O D. Best Actor 44 39 36 46 53 47 63 51 41 56 44 33 Best Actor (years) Best Actor (years) 20-+ 20 Best Actress (years) Best Actress (years) The linear correlation coefficient is r= (Round to three decimal places as needed.) ne the null and alternative hypotheses. Ho : P 11 4 A H1 : P (Type integers or decimals. Do not round.) The test statistic is t=]. (Round to two decimal places as needed.) less than or equal to The P-value is. IS greater than is not (Round to three decimal places as needed.) Because 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 the ages of Best Actresses and Best Actors. Should we expect that there would be a correlation? O A. No, because Best Actors and Best Actresses are not typically the same age. O B. Yes, because Best Actors and Best Actresses typically appear in the same movies, so we should expect the ages to be correlated. O C. No, because Best Actors and Best Actresses typically appear in different movies, so we should not expect the ages to be correlated. O D. Yes, because Best Actors and Best Actresses are typically the same age.Police sometimes measure shoe prints at crime scenes so that they can learn something about criminals. Listed below are shoe print lengths, foot lengths, and heights of males. 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. Based on these results, does it appear that police can use a shoe print length to estimate the height of a male? Use a significance level of a = 0.05. Shoe Print (cm) 29.2 29.2 30.8 31.0 27.3 Foot Length (cm) 25. 25.7 27.2 27.1 25.6 Height (cm) 170.4 173.8 181.1 171.2 167.8 Construct a scatterplot. Choose the correct graph below. OA O B. O c. OD 200- 200 200 200 Height (cm) Height (cm) Height (cm) Height (cm) 160 160- 160 160 25 25 35 25 35 25 35 Shoe Print (cm) Shoe Print (cm) Shoe Print (cm) Shoe Print (cm) The linear correlation coefficient is r =. (Round to three decimal places as needed. Determine the null and alternative hypotheses. Ho: P H1 : P V (Type integers or decimals. Do not round.) The test statistic is t =]. (Round to two decimal places as needed.) less than or equal to The P-value is. is not greater than (Round to three decimal places as needed.) IS Because 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 shoe print lengths and heights of males. Based on these results, does it appear that police can use a shoe print length to estimate the height of a male? O A. No, because shoe print length and height do not appear to be correlated. O B. Yes, because shoe print length and height do not appear to be correlated. O C. Yes, because shoe print length and height appear to be correlated. O D. No, because shoe print length and height appear to be correlated.Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 100 mm Hg. Use a significance level of 0.05. Data table Critical Values of the Pearson Correlation Coefficient r ( = 0.05 ( = 0.01 NOTE: To test Ho: 0.950 0.990 p = 0 against Hy : p #0, 0.878 0.959 6 0.811 0.917 eject Ho if the 7 0.754 0.875 absolute value of r is Right Arm 103 102 95 77 76 8 0.707 0.834 greater than the critica 9 0.666 value in the table. Left Arm 174 0.798 169 148 149 147 10 0.632 0.765 11 0.602 0.735 12 0.576 .708 13 0.553 0.684 14 0.532 0.661 15 The regression equation is y = ] +x. 0.514 .64 16 0.497 0.623 (Round to one decimal place as needed.) 17 0.482 0.606 18 0.468 0.590 Given that the systolic blood pressure in the right arm is 100 mm Hg, 19 0.456 0.575 the best predicted systolic blood pressure in the left arm is | | mm Hg. 20 0.444 0.561 (Round to one decimal place as needed.) 25 0.396 0.505 30 0.361 0.463 35 0.335 0.430 40 0.312 0.402 45 0.294 0.378 50 0.279 361 60 0.254 0.330 70 0.236 0.305 80 0.220 .286 90 0.207 0.269 100 0. 196 0.256