3:16KThe accompanying data represent the number of days absent, x, and the final exam score, y, for a sample of collegestudents in a general education course at a large state university. Complete parts (a) through (e) below.Click the icon to view the absence count and final exam score data.Click the icon to view a table of critical values for the correlation coefficient.(a) Find the least-squares regression line treating number of absences as the explanatory variable and the final examscore as the response variable.y = -2.777 x + 88.336(Round to three decimal places as needed.)(b) Interpret the slope and the y-intercept, if appropriate. Choose the correct answer below and fill in any answerboxes in your choice.(Round to three decimal places as needed.)O A. For every additional absence, a student's final exam score dropsto interpret the y-intercept.O B. For every additional absence, a student's final exam score dropsexam score of students who miss no classes isOC. The average final exam score of students who miss no classes isslope.OD. It is not appropriate to interpret the slope or the y-intercept.Vo) 1LTE 4G 24%|||=Opoints, on average. It is not appropriatepoints, on average. The average finalIt is not appropriate to interpret the Critical Values for Correlation Coefficientn3456789101112131415161718192021222324252627282930n0.9970.9500.8780.8110.7540.7070.6660.6320.6020.5760.5530.5320.5140.4970.4820.4680.4560.4440.4330.4230.4130.4040.3960.3880.3810.3740.3670.361Absences and Final Exam ScoresNo. ofabsences, x0 1 2 3 4 5 6 7 8 9Finalexam score, y 88.3 86.782.6 81.6 77.2 73.565.172.4 65.7 65.3-X

From the part a, The regression equation is, y^=-2.777X+88.336 Here, slope=-2.777 Intercept=88.336…

Answered: The accompanying data represent the…

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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In analyzing the the correlation between two variables in the scatter plot, when the variable X increases, the variable Y also increases then the correlation between them is a. no correlation b. negative c. multiple correlation d. positive
Let x be the average number of employees in a group health insurance plan, and let y be the average administrative cost as a percentage of claims. x 3 7 15 32 75 y 40 35 30 26 18 (a) Make a scatter diagram of the data and visualize the line you think best fits the data. Flash Player version 10 or higher is required for this question. You can get Flash Player free from Adobe's website. (b) Would you say the correlation is low, moderate, or strong? positive or negative? strong and negative moderate and negative strong and positive low and negative low and positive moderate and positive (c) Use a calculator to verify that Σx = 132, Σx2 = 6932, Σy = 149, Σy2 = 4725, and Σxy = 2997. Compute r. (Round your answer to three decimal places.)r = As x increases, does the value of r imply that y should tend to increase or decrease? Explain. Given our value of r, we cannot draw any conclusions for the behavior of y as x increases. Given our value of r, y should tend to…
Use a graphing calculator to create a scatter plot and find the line of best fit and the correlation coefficient for the following data. Round to the nearest hundredth. X 2 4 6 8124 10 Part: 0/2 y 2 3 6 7 10 15 19
Assume that you have paired values consisting of heights (in inches) and weights (in lb) from 40 randomly selected men. The linear correlation coefficient r is 0.594. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Choose the correct answer below. O A. The coefficient of determination is 0.353. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O B. The coefficient of determination is 0.647. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors. O C. The coefficient of determination is 0.647. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O D. The coefficient of determination is 0.353. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors.
The data below are the ages and annual pharmacy b ills (in dollars) of 9 randomly selected employees. Calculate the linear correlation coefficient.Age, x: 30, 33, 37, 40, 43, 45, 49, 53, 57Pharmacy bill ($), y: 119, 123, 126, 134, 145, 148, 151, 153, 155A. 0.890B. 0.908C. 0.998D. 0.960
Please explain each step clearly, and no excel formula should be used for solving this problem
In correlation analysis, the relationship between age of persons and his or her blood pressure is called as a. Simple Correlation b. Multiple Correlation c. No Correlation d. None of these
A manager wishes to determine the relationship between the number of years the manager's sales representatives have been with the company and their average monthly sales (in thousands of dollars). Calculate the linear correlation coefficient.Years with company, x: 6,7,14,11,12,19,7,5,15Sales, y: 21,23,68,52,55,51,38,45,110
Compute and interpret the coefficient of multiple correlation. Number 4 answer is missing.
Based on the image attached, it shows a scatterplot of daily coffee consumption vs. IQ level. Based on the given scatter diagram, choose the correct statement. a. It is a no correlation. The amount of an individual consumes daily consumption has no correlation with his/her IQ level. b. It is a positive correlation. The more amount of an individual consumes daily consumption, the lower their IQ level to be. c. It is a negative correlation. The more amount of an individual consumes daily consumption, the lower their IQ level to be. d. It is a negative correlation. The more amount of an individual consumes daily consumption, the higher their IQ level to be. e. It is a positive correlation. The more amount of an individual consumes daily consumption, the higher their IQ level to be.
Use the output below to test to see if there is a significant positive linear relation between height and head circumference in children. Use a 5% level of significance. Correlations: Height, Head Circ Pearson correlation of Height and Head Circ 0.911 P-Value 0.000

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