tion 13A least squares regression linea. may be used to predict a value of y if the corresponding value is givenO b. implies a cause-effect relationship between x and yO c. can only be determined if a good linear relationship exists between x and yOd. All of these answers are correct.

The regression shows how 1 dependent variable and 1 or more independent variables are related.

Answered: A least squares regression line a. may…

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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An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y=nothingx+(nothing) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) A. A weightless car will get nothing miles per gallon, on average. It is not appropriate to interpret the slope. B. For every pound added to the weight of the car, gas mileage in the city will decrease by nothing mile(s) per gallon, on…
12) Use computer software to find the best multiple regression equation to explain the variation in the dependent variable, Y, in terms of the independent variables, X1, X2, X3. 9896 29.1 1 421 9680 42.3 2 653 10449 29.8 3 573 10811 26.0 4 CORRELATION COEFFICIENTS 546 10014 34.3 5 499 10293 22.7 6 %3D 60% 0.00 Y/ X2=0.280 Y/ X3 = 0.930 504 9413 24.2 7 %3D 611 9860 31.6 8 %3D 646 9782 25.6 9 789 12139 37.9 10 COEFFICIENTS OF DETERMINATION 773 12166 33.9 11 YI X1 = 0.259 Y/ X2 = 0.079 YI X3 = 0.864 Y/ X1, X3 = 0.880 YI X1, X2, X3 = 0.884 753 9976 37.4 12 %3D 852 10645 27.0 13 %3D 755 9738 31.5 14 %3D 815 9933 39.9 15 %3D 902 10132 25.3 16 986 11145 30.4 17 909 9775 32.7 18 945 9549 35.0 19 866 10077 33.8 20 1178 11550 29.4 21 1230 10600 37.1 22 1207 11280 42.9 23 968 12100 32.2 24 1118 12420 30.5 25 A) Î = 57.8+0.036X, +28.1X3 B) Ý = -21.1+0.36X, +2.62X, +27.6X3 %3D C) Ý = 201.7+0.40X, +22.3X3 D) Y = 308.6+ 29.9X3 %3D
Which of the following four statements is correct?Note: Only one answer is correct per question.1) Let f (x) = 2 + 5x be a linear regression line of a linear regression from Y to X.Then the following applies to the scatter plot: 1) The closer the points in the scatter diagram to the regression line drawnthe stronger the correlation between X and Y. 2) The further the points in the scatter plot are from the regression line drawn away, the stronger the correlation between X and Y. 3) Only if all points lie exactly on the regression line drawn isthe correlation between X and Y is strong. 4) The position of the points in the scatter plot has nothing to do with the strength of the correlation. 2) For the events A = "number of points when rolling the dice is two" and B = "number of points when rolling the diceRolling the dice is straight ":* P (A \ B) is smaller than P (A).* P (A \ B) is the same size as P (A).* P (A \ B) is greater than P (A).* No general statement can be made about…
Biologist Theodore Garland, Jr. studied the relationship between running speeds and morphology of 49 species of cursorial mammals (mammals adapted to or specialized for running). One of the relationships he investigated was maximal sprint speed in kilometers per hour and the ratio of metatarsal-to-femur length. A least-squares regression on the data he collected produces the equation ŷ = 37.67 + 33.18x where x is metatarsal-to-femur ratio and y is predicted maximal sprint speed in kilometers per hour. The standard error of the intercept is 5.69 and the standard error of the slope is 7.94. Construct a 96% confidence interval for the slope of the population regression line. Give your answers precise to at least two decimal places. contact us help 6:42 PM povecy polcy terms of use careers A E O 4») 18 -క90.4 58 12/14/2020 a 17 |耳即 delets prt sc insert 112 19 18 + 16 backspace f5 fA
In multiple regression testing of Ho: B1 = B2 = B3 = ... = BK = %3D %3D O at a = 0.05, a p-value of 0.08, would give an indication that: O the null hypothesis should not be rejected O all three independent variables have a slope of zero O the null hypothesis should be rejected O None of the Choices O there is linear relationship between y and any of the three independent variables I3D
If a regression line for two variables has a small positive slope, then the: variables are positively associated? variables are negatively associated? association of the variables cannot be determined. variables have no association with each other.
It is considered that the number of employees in the enterprise affects the number of production. Data are given below. Which of the following is the simple linear regression equation?
1.)fill in the blanks. Based on the physician's study, the predictor variable, X is_____and the response variable, Y, is________. 2.) As described in the article, the relation between age and peak heart is a______. - positive relation - negative relation - no relation 3.) Provide the regression line, ŷ=a+bx. Show steps/equations used to get answer. 4.) Suppose a 40 year old person is randomely selected. Use your Model to predict their peak heart rate. 5.) Based on your model, as a person ages one year, how much would you expect peak heart rate to change?
2. Years of Work Experience and number of Job Offers of 10 job-seekers were as follows: Work Exp. 4 5 3 7 12 2 5 4 9 No. of Offers 7 1 8 4 13 19 3 11 15 a. Fit the regression equation of No. of Job Offers on Years of Work Experience. b. What will be the predicted number of offers for an applicant with 6 years of experience? c. Verify the relationship between the number of job offers and years of work experience using at least two relevant methods.
3. A Ross MAP team is trying to estimate the revenues of major-league baseball teams during the regular season using a regression model. Currently, the independent variables include stadium capacity, the number of weekend games, the number of night games, and the number of Wins (out of 162 regular season games). One of your team members suggests that the model also should include the number of losses as it provides additional explanatory power. Assume that ties are not possible; so every game results in exactly one team winning and the other team losing. Which of the following statements is the most likely conclusion of the new regression model? (a) R2 will increase, adjusted R2 will decrease, and Serror will decrease. (b) R2 and adjusted R2 will increase, and serror will decrease. (c) R, adjusted R2, and Serror will increase. (d) We cannot trust the regression output as some variables are highly correlated, resulting in multicollinearity. Answer to Question 3:
1. Correlation analysis is used to determine the equation of the regression line b. a specific value of the dependent variable for a given value of the independent variable the strength of the relationship between the dependent and the independent variables d. a. с. None of these alternatives is correct.
I have a doubt when it comes to this reasoning : Imagine I have a variable that is correlated to Y and to X1 in a linear regression model. If I ommit it it will result in Omitted Variable Bias but if I include it, would it result in perfect multicolinearity and therefore for example a solution is to include control variables ? Is this right ? Thanks.

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A least squares regression line a. may be used to predict a value of y if the corresponding Value is given O b. implies a cause-effect relationship between x and y O c. can only be determined if a good linear relationship exists between x and y O d. All of these answers are correct.