Ch 17 multiple regression1. In a multiple regression analysis, if the model provides a poor fit, this indicates that:a. the sum of squares for error will be large.b. the standard error of estimate will be large.c. the coefficient of determination will be close to zero.d. All of these choices are true.2. Suppose a multiple regression analysis involving 25 data points has -18 and SSE = 36. Then, thenumber of the independent variables must be:a. 6b. 5C. 4d. 33. In a multiple regression model, the mean of the probability distribution of the error variable e isassumed to be:a. 1.0b. 0.0e. k, where k is the number of independent variables included in the model.d. None of these choices.4. The adjusted coefficient of determination is adjusted for the:a. number of regression parameters including the y-intercept.b. number of dependent variables and the sample size.c. number of independent variables and the sample size.d. coefficient of correlation and the significance level.5. In a multiple regression model, the standard deviation of the error variable e is assumed to be:a. constant.b. 0.c. 1.0.d. None of these choices.6. In multiple regression analysis, the ratio MSR/MSE yields the:a. 1-test statistie for testing each individual regression coefficient.b. F-test statistic for testing the validity of the regression equation.c. coefficient of determination.d. adjusted coefficient of determination.7. In a multiple regression analysis involving 6 independent variables, the total variation in y is 900 andSSR - 600. What is the value of SSE?a. 300b. 1.50e. 0.67d. None of these choices.8. In order to test the validity of a multiple regression model involving 5 independent variables and 30observations, the numerator and denominator degrees of freedom for the critical value of Fare,respectively,a. 5 and 30b. 6 and 29e. S and 24d. 6 and 25

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Problem 1P

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A researcher wants to include the variable "skill" in a regression, but cannot find a proper metric to measure it, so they just decide not to include it in the model. You know how to measure skill and run the regression with it included and find that some of the original coefficients change. What would explain this? Oa. The covariance between skill and each of the other regressors is zero. O b. The B for skill is zero. Oc. The covariance between skill and each of the other regressors is not zero. O d. The variance of skill is increasing. What are the forecasts for years 7,-8 and 9 using the following data and a second-order autoregressive model where = -3.06, $1 = 0.86 and $2 = 0.94. Year Data 4. 6. 8. 13 16 19 3. 4. O a. =11.980 8=14.800 9 = 23.561 Ob. 7= 28.320 8 39.155 g 57.234 O c. 7=31.140 f8 = 47.916 Models in which all the error terms have the same variance are said to exhibit... O a. Multicollinearity. O b. Homoscedasticity O C. Heteroscedasticity. O d. Autocorrelation. Clear…
4.
Q2. Adjusted R2 is the modified version of R after adjusting for the number of predictor variables in a multiple regression model. a. TrueB. FalseC. None of the above
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.
Which of the multivariate regression parameters listed below would be best interpreted as: the predicted value on the dependent variable when all of the independent variables in the model are equal to zero. a b1 X1 R2
If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?
Suppose a point in a regression has a negative residual. This means: 1. The regression over-estimated this point 2. The regression under-estimated this point 3. The regression correctly estimated this point
Suppose Wesley is a marine biologist who is interested in the relationship between the age and the size of male Dungeness crabs. Wesley collects data on 1,000 crabs and uses the data to develop the following least-squares regression line where X is the age of the crab in months and Y is the predicted value of Y, the size of the male crab in cm. Y = 8.2052 + 0.5693X What is the value of Ý when a male crab is 21.7865 months old? Provide your answer with precision to two decimal places. Interpret the value of Ý. The value of Ý is the probability that a crab will be 21.7865 months old. the predicted number of crabs out of the 1,000 crabs collected that will be 21.7865 months old. the predicted incremental increase in size for every increase in age by 21.7865 months. the predicted size of a crab when it is 21.7865 months old.
In the section we introduced a descriptive measure of the utility of the multiple linear regression for making predictions. a. Define and interpret this descriptive measure. b. Identify the symbol used for this descriptive measure.
1) Draw the graph of the estimated regression below. Y₁ = Bo + B₁X₁B₂X² + B3X²
Consider the following linear regression prediction equation: Y = 12 + -1x. What is the direction of the relationship between these two variables? a. The relationship between the observed X and the predicted Y is negative. b. The relationship between the observed X and the predicted Y is positive. c. There is no relationship between the observed X and the predicated Y. d. There is a weak relationship between the observed X and the predicted Y with no direction.
Consider the simple linear regression model Wage = Bo + B1*Age + U. The error term U can capture the followings, with the exception of O A. possible measurement error in Wage. O B. the temperature in London Ontario tomorrow. O C. possible model misspecification, such as the nonlinear effect of Age on Wage. O D. other variables that affect the dependent variable, such as previous work experience.

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1. In a multiple regression analysis, if the model provides a poor fit, this indicates that: a. the sum of squares for error will be large. b. the standard error of estimate will be large. e. the coefficient of determination will be close to zero. d. All of these choices are true. Suppose