Q12. Identify the correct statement concerning, r the correlation coefficientbetween X and Y and the linear regression, Y = aX+b.A. Both can be used to show cause and effect.B. Both quantify the strength of relationship between X and Y.C. X and Y are interchangeable in both relationships.D. Both can be used to make predictions.

The general form of the regression line is: Y=ax+b Where Y is the predicted value, 'b' is the…

Answered: Q12. Identify the correct statement…

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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2. What is the use of the regression line? C. A student conducted a regression analysis between the math grades of his classmates and the number of times they were absent in the subject. He found that the regression line that will predict the grade (y) if the number of absences (x) is known y = 97.732 - 2.61x. 1. What is the predicted grade of a student who has 5 absences? 2. What is the predicted grade of a student who has 8 absences? 3. What is the predicted grade of a student who has 2 absences? 4. What is the predicted grade of a student who has 1 absence? 5. What is the predicted grade of a student who has 0 absences?
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Q 27. Equation of regression lines obtained in a correlation analysis are as follows: 8Y = 18X + 30.50X = 12Y + 14 Find (a) the %3D mean values of X and Y (b) the coefficient of correlation.
The equation of a regression line, unlike the correlation, depends on the units we use to measure the explanatory and response variables. Here is the data on percent body fat and preferred amount of salt. Preferred amountof salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 21 30 23 30 39 24 31 In calculating the preferred amount of salt, the weight of the salt was in milligrams. (a) Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in milligrams. (Round your answers to one decimal place.) ŷ = + x (b) A mad scientist decides to measure weight in tenths of milligrams. The same data in these units are as follows. Preferred amountof salt x 2 3 4 5 6 8 11 Percent body fat y 21 30 23 30 39 24 31 Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in tenths of milligrams. (Round your intercept to one decimal place and your slope to two…
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The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins Click the icon to view the table of numbers of wins and earned run average. (b) x = 10 wins (c) x = 21 wins (d) x = 15 wins ERA 6- ERA 6- AERA 6- ERA 6- 4- 4- 4- 4- 2- 2- 2- 2- 0+ 6 0- 0- 0- 12 18 24 6. 12 18 24 12 18 24 6 12 18 24 Wins Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice below and, if necessary, fill in the answer box within your choice. A. ŷ= (Round to two decimal places as needed.) B. It is not meaningful to predict this value of y because…
What is the value of r2? Use the square root and your knowledge about the slope of the regression line to find the value of the correlation constant r. Based only on the value of r, do you think this line is useful for predicting life expectancy from the infant mortality rate? A new country called Arendelle has formed and has an infant mortality rate of 42 (deaths per 1000 live births). Use the equation of the regression line to predict the life expectancy for Arendelle. Round your answer to one decimal place. Another new country called Weselton has formed and has an infant mortality rate of 250 (deaths per 1000 live births). Recall some of the cautions we should take when considering correlation. In one or two complete sentences, explain why using the linear regression equation to make a life expectancy prediction is not a good idea for Weselton. If you had to choose exactly one potential influential outlier from the original data set, what would it be? Identify the point as an…
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Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative? A. The slope is negative. As the independent variable increases the dependent variable also tends to increase. B. The slope is negative. As the independent variable increases the dependent variable tends to decrease. C. The slope is positive. As the independent variable increases the dependent variable also tends to increase. D. The slope is positive. As the independent variable increases the dependent variable tends to decrease.
Identify the correct statement concerning, r the correlation coefficie between X and Y and the linear regression, Y = aX+b. A. Both can be used to show cause and effect. B. Both quantify the strength of relationship between X and Y. C. X and Y are interchangeable in both relationships. D. Both can be used to make predictions.
Choose all that apply when describing R2. (Hint: 5 options are correct) (Select all that apply.) Can be inflated by adding more variables. O Can be inflated by removing variables. Referred to as the coefficient of detemination or coefficient of multiple determination. Is used to determine the fit of a model. Represents the percent of variability in y that can be explained by the model. In simple linear regression, it is equal to the correlation coefficient 2. O Only describes the relationship between quantitative variables. O Is only used in multiple linear regression. SSres SSreg SStotal SStotal
z score z score for each for each value of value of Zzły х х -0.278 0.536 -0.149 -0.089 0.696 -0.062 -1.253 -1 -1.652 2.070 -0.714 -0.928 0.663 2.461 1.473 1.671 0.536 0.371 0.199 -0.089 4 -0.278 0.025 Σ: ỹ = 3.857 S, = 3.078 = 5.207 T= 4.286 s, = 3.200

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