Research was conducted on the amount of training for 5K and the time a contestant took to run the race. The researcher recorded the number of miles during training ( a 1 month period) and the time to complete the 5K. The results are below. Miles Trained 20 18 67 46 66 16 Time (Minutes) 27.5 33 22 18.4 17 32.1 a) Give the correlation coefficient. r = b) Give the equation of the regression line. Round the values to 4 deimal places. This is in the form y = ax + b, so the slope goes in the first box and the y-inercept in the 2nd box. y = x + c) Interpret the y-intercept (Explain it's meaning, do not just give its value.) d) Predict the time in the 5K if someone trained 67 miles. Use the rounded values entered for the slope and intercept of the regression line to compute the predicted y-value. y = e) Give the residual for 67 miles trained. This is the actual y value for the runner who trained for miles minus the predicted value. Residual=

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the valuo of v for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 762 621 508 480 (b) x = 641 feet (d) x = 726 feet 515 491 (a) x= 498 feet (c) x = 810 feet 51 46 44 42 38 37 Find the regression equation. ý=x+ (D (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OA. OB O B. Oc. OD. 60- 604 60+ 800 B00 G 800 Height (feet) 800 Height (feet) 800 Height (feet) Height (feet) (a) Predict the value of y for x = 498. Choose the correct answer below. OA 40 ОВ. 50 O C. 47 O D. not meaningful (b) Predict the value of y for x 641. Choose the correct answer…

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Part two Given the data set below, 3 4 7 7 8. Y 4 7 9 6. 3 Find the scatter plot. 8) Find the equation of the regression line. 9) Find R and R2. 10) What can you say about R and R2. 4. 00

solve a,b and c given in the image provided.

2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?

Please answer A,B, D, F, & G pls and thank you!

The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 48 inches. Is the result close to the actual weight of 308 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. 58 49 58 43 46 57 355 266 332 177 246 348 What is the regression equation? y = + x (Round to one decimal place as needed.) %3D What is the best predicted weight of a bear with a chest size of 48 inches? The best predicted weight for a bear with a chest size of 48 inches is pounds. (Round to one decimal place as needed.) Is the result close to the actual weight of 308 pounds? A. This result is not very close to the actual weight of the bear. B. This result is close to the actual weight of the bear. C. This result is exactly the same as the actual weight of the bear.…