Question 3A. The information below shows the age and the number of sick days taken for 6 employeesat a biscuit factory.Age(x)182639485358Number of sick days(Y)16129562Table 3.Using the information above:i. Determine the product-moment coefficient (r)ii. Calculate the coefficient of determination and interpret your answeriii. Determine the equation of the regression lineiv. Use the equation of the regression line to estimate the number of sick days thatwould be taken by an employee who is 47.

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Answered: A. The information below shows the age…

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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The table shows the amounts of crude oil (in thousands of barrels per day) produced by a certain country and the amounts of crude oil (in thousands of barrels per day) imported by the same country for seven years. The equation of the regression line is y =-1.364x+17,178.20. Complete parts (a) and (b) below. Produced, x Imported, y 5,744 5,554 5,439 5,188 5,136 5,067 D 9,132 9,677 10,017 10,193 10,149 10,075 5,756 9,328 (a) Find the coefficient of determination and interpret the result. (Round to three decimal places as needed.)
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. I 11.5 8.2 6.8 3.4 13.6 11.3 9.7 6.8 6.4 2.7 2.9 2.2 0.6 6 6.1 4.6 Y = thousands of automatic weapons y = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 9.4 thousand automatic weapons? Answer Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 3 thousand automatic weapons? Answer= Round to 3 decimal places. Hint: Textbook Pages C Help -
The ages (in years) of five men and their systolic pressures. (You can use technology) Age, x 16 25 39 45 49 Systolic blood pressure, y 109 122 143 132 199 a. calculate the correlation coefficient, r b. calculate the critical r c. Is there a significant linear relationship between x and y? d. find the equation of regression line e Interpret the slope and the y-intercept f. Predict the systolic blood pressure for a man who is 40 years old.
An educator wants to see how the number of absences for a student in her class affects the student's final grade by formulating the equation of the regression line. The data obtained from a sample are shown. Required: Slope of the regression line Only the final answer should be rounded-off to TWO decimal places. 12 Number of absences (x) 10 2 0 7 4 Final Grade (y) 65 68 70 94 77 96
a. Using the pairs of values for all 10 points, find the equation of the regression line. b. After removing the point with coordinates (2,3), use the pairs of values for the remaining 9 points and find the equation of the regression line. c. Compare the results from parts (a) and (b).
Using the weights (Ib) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58 9-0.00749x, where x represents weight Complete parts (a) through (d). Click the icon to view the car data. 東 b. What are the specific values of the slope and y-intercept of the regression line? O A. The slope is 58.9 and the y-intercept is 0.007499. B. The slope is -0.00749 and the y-intercept is 58.9. O C. The slope is 58.9 and the y-intercept is -0.00749. O D. The slope is 0.00749 and the y-intercept is 58.9. c. What is the predictor variable? O A. The predictor variable is highway fuel consumption, which is represented by x. B. The predictor variable is weight, which is represented by x. O C. The predictor variable is weight, which is represented by y O D. The predictor variable is highway fuel consumption, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel…
Use the data in the table below to complete parts (a) through (d). 39 33 40 47 42 50 59 56 51 24 22 27 32 30 31 31 27 29 Click the icon to view details on how to construct and interpret residual plots. (a) Find the equation of the regression line. (Round to three decimal places as needed.) (b) Construct a scatter plot of the data and draw the regression line. Plot the x-values on the horizontal axis and the y-values on the vertical axis. Choose the correct graph below. OA. В. Oc. OD. 34 70, (c) Construct a residual plot. Plot the x-values on the horizontal axis and the residuals on the vertical axis. Choose the correct graph below. O A. Ов. Oc. OD. (d) Determine if there are any patterns in the residual plot and explain what they suggest about the relationship between the variables. The residual plot a pattern because the residuals about 0. This implies the regression line a good representation of the relationship between the variables.
Consider the following table, find the value of the INTERCEPT (b) of the regression line. X Values Y Values 23 8 22 25 4 24 7 21
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.3 8.5 y 13.4 7 3.6 2.6 2.2 2.6 0.8 11 10.2 7.4 6 5.7 6.3 4.9 * = thousands of automatic weapons y = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 10.1 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 9.9 thousand automatic weapons? Answer = Round to 3 decimal places.
3. Find the equation of the regression line for the data and graph the scatter plot for the data No of ads (x) Sales (y) 2 5 8 8 10 12 2 4 7 9 10 Find yl at x = 47 ads
1. In a manufacturing process the assembly line speed (feet per minute) was thought to affect the number of defective parts found during the impaction process. To test this theory, managers devised a situation in which the same batch of parts was inspected visually at a variety of line speeds. They collected the following data. Line Speed (x) Number of Defective Parts Found (y) 20 21 20 19 40 15 30 16 60 14 40 17 Use Microsoft Excel to generate regression output for this problem and use the information in the output to answer the following questions.

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