Fast reactions: In a study of reaction times, the time to respond to a visual stimulus (x) and the time to respond to an auditory stimulus (y)were recorded for each of 8 subjects. Times were measured in thousandths of a second. The results are presented in the following table.Visual Auditory203206235241178201191169211189188193161159176163Send data to ExcelThe least-squares regression line y = b+b;x= 5.3531+0.958x and E (x-x) = 3754.8750 are known for this data. Use the critical valuemethod to test H: B1 = 0 versus H:P > 0. Can you conclude that visual response is useful in predicting auditory response? Use the a = 0.01level of significance.Part 1 of 4Find the critical value(s). Round the answer to three decimal places. If there is more than one critical value, separate them with commas.Critical value(s): 3.143Part: 1/ 4Part 2 of 4Compute the test statistic. Always round t-score values to three decimal places.t =

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Answered: Fast reactions: In a study of reaction…

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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Suppose a doctor measures the height, x, and head circumference, y, of 11 children and obtains the data below. The correlation coefficient is 0.883 and the least squares regression line is y = 0.134x + 13.693. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.4 27.75 25.25 26 25.25 27.25 26.75 26 27.25 27 27.25 26.75 17.1 17.3 17.0 17.4 17.2 17.2 17.3 17.3 17.4 17.3 ..... (a) Compute the coefficient of determination, R?. R? % (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Approximately % of the variation in is explained by the least-squares gression model. (Round to one decimal place as needed.)
Consider the following table of data. x 0 1 2 y 4 1 0 1 3 4 1 4 (a) Calculate the least squares line and the correlation coefficient. (b) Sketch a graph of the data. (c) Comparing your answers to parts (a) and (b), does a cor- relation coefficient of 0 mean that there is no relationship between the x- and y- values? Would some curve other than a line fit the data better? Explain.
A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x= 3.3333, s= 2.5033, y = 4.0667, s, = 1.9449, and r= - 0.9585, determine the least-squares regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a). 3 4 5.8 6.3 5.1 3.4 1.8 2.0 重 (a) Choose the correct graph below. OA. O B. OD. 本y 7- Ay 本y 7- AY 7- 0- 0. 0- 0. 6. There appears to be relationship. (b) ŷ =[]x+ (D (Round to three decimal places as needed.) (c) Choose the correct graph below. O A. О В. OC. O D. Ay 7- Ay 7- 0- 0. 9. 6. O Time Remaining: D TCOA2334403 U 10-8-2014 MAIN TCOA2330401 (2) Q 出
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x= 3.6667, sy = 2.2509, y= 3.6833, s, = 1.8809, and r= - 0.9511, determine the least-squares regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a). 1 4 4 6 6 y 5.0 6.3 4.2 3.4 1.4 1.8 (a) Choose the correct graph below. OA. B. OC. OD. Ay 74 Ay Ay 7- .. .. There appears to be a linear, negative relationship. (b) y=x+ O (Round to three decimal places as needed.) (c) Choose the correct graph below. O A. B. OC. D. Ay 0-
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A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x = 3.6667, sx =2.4221, y = 3.8667, sy = 1.8435, andr= -0.9525, determine the least-squares regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a). D O A. (a) Choose the correct graph below. Ay 6- X y 0 0 2 3 5 6 6 5.8 5.7 5.0 2.8 1.7 2.2 O G B. Ay 6- Q (...) C. 0- ● Q D. Ау 0- X 6
The table below shows the heights (inches) and weights (pounds) of 9 randomly selected children. Child Data Height (inches), x 36 56 58 59 61 63 65 69 73 Weight (pounds), y 69 89 96 74 110 90 123 110 128 (a) Construct a scatter diagram for the data, where the weight of the child is the response variable. (b) Determine the least-squares regression line for the data and graph the line on the scatter diagram from part (a). (use calculator) (show what did you do on the calculator) (c) Interpret the slope and the y–intercept in the context of the problem. (d) Predict the weight of a child that is 57 inches tall. Comparing with the observed value, what is the residual? Is the observed weight above or below average? (e) Determine the correlation coefficient, r. Is there a correlation? Discuss the strength of the correlation and why. (f) Are heights and weights of children linearly related? Why or why not? Be sure to justify your answer by using the appropriate method. (g)…
Name Her Age His Age Barack and Michelle Obama 45 47 George W. and Laura Bush 54 54 Bill and Hillary Clinton 45 46 Ronald and Nancy Reagan 55 64 Gerald and Betty Ford 59 69 Lyndon and Lady Bird Johnson 56 61 John and Jacqueline Kennedy 31 43 Dwight and Mamie Eisenhower 50 55 (c) Remove the outlier and compute the least-squares regression line for predicting the president's age from first lady's age. Round the slope and y -intercept values to at least four decimal places. Regression line equation: =y
50
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.858 and the least squares regression line is y = 0.228x +11.187. Complete parts (a) and (b) below. Height, x 27.5 25.75 26.5 25.5 27.25 26.25 25.75 27.25 27 27.25 27 Head Circumference, y 17.4 17.2 17.2 16.9 17.6 17.1 17.1 17.4 17.4 17.3 17.3 (a) Compute the coefficient of determination, R². R² =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. According to the residual plot, the linear model appears to be
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