Using 81 quarterly observations on the growth rate of employment (Y) and thegrowth rate of output (X), the following regression results are obtained by ordinaryleast squares (t = 1, 2,...,81):-0.002+0.105X+0.730Y-1+0.063X-1,(0.001) (0.014)(0.045)(0.016)BG 1.100 [0.363], WH=1.474 [0.175], RESET = 0.081 [0.777],R² = 0.8827, SSR 0.00061,-0.002+0.086(X+X-1)+0.711Y-1,(0.001)(0.011)(0.045)R² = 0.8767,SSR = 0.00065.is the fitted value of the regression; figures in parentheses are the standard errorsof the estimated coefficients; R² is the coefficient of determination; SSR is the sumof squared residuals; BG is the Breusch-Godfrey test for fourth-order autocorre-lation; WH is White's test for heteroskedasticity; RESET is Ramsey's regressionspecification error test; figures in square brackets are the p-values of BG, WH andRESET.Test the null hypothesis that the coefficients on X and X-1 in the first regres-sion are equal against the alternative hypothesis that they are not equal (the5% critical value for the test is 3.9651). State clearly what the distribution ofyour test statistic is under the null hypothesis.

Given that using 81 quarterly observations on the growth rate of employment and the growth rate of…

Answered: Using 81 quarterly observations on 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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The equation used to predict college GPA (range 0-4.0) is y = 0.17 +0.51x, +0.002x,, where x, is high school GPA (range 0-4.0) and x, is college board score (range 200-800). Use the multiple regression equation to predict college GPA for a high school GPA of 3.8 and a college board score of 500. The predicted college GPA for a high school GPA of 3.8 and a college board score of 500 is (Round to the nearest tenth as needed.)
tion of about $1 million in payroll. (E) One extra win corresponds to an addi- tion of about $17 million in payroll. 6. The residual for the least-squares regression line y = 3.422 +5.551x at the data point (9.227, y) is equal to 1.811. Which of the following is the value of y? (A) 56.452 (B) 54.641 (C) 52.830 (D) 11.038 (E) 7.416 ere to search
A particular article presented data on y = tar content (grains/100 ft³) of a gas stream as a function of x₁ = rotor speed (rev/min) and x₂ = gas inlet temperature (°F). The following regression model using X₁, X2, X3 = ×₂² and ×4 = X₁X₂ was suggested. (mean y value) = 86.5 – 0.121x₁ +5.07x2 - 0.0706x3 + 0.001x4 (a) According to this model, what is the mean y value (in grains/100 ft³) if x₁ = 3,400 and x₂ = 55. grains/100 ft³ (b) For this particular model, does it make sense to interpret the value of ₂ as the average change in tar content associated with a 1-degree increase in gas inlet temperature when rotor speed is held constant? Explain. Yes, since there are no other terms involving X2. O Yes, since there are other terms involving X₂. ● No, since there are other terms involving X2. O No, since there are no other terms involving X2.
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x,) and newspaper advertising (x,). The estimated regression equation was ý = 83.3 + 2.24x, + 1.30x2. The computer solution, based on a sample of eight weeks, provided SST 25.2 and SSR = 23.455. %D (a) Compute and interpret R² and R,. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R = 0.653 and R, = 0.595. Do you prefer the multiple regression results? Explain. %3D 2 Multiple regression analysi v ---Select--- ipreferred since both R2 and R, show ---Select--- O…
The flow rate in a device used for air quality measurement depends on the pressure drop x (inches of water) across the device's filter. Suppose that for x values between 5 and 20, these two variables are related according to the simple linear regression model with true regression line y = -0.11 + 0.097x. (a.1) What is the true average flow rate for a pressure drop of 10 in.?(a.2) A drop of 15 in.?(b) What is the true average change in flow rate associated with a 1 inch increase in pressure drop?(c) What is the average change in flow rate when pressure drop decreases by 5 in.?
A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is Y = -0.800+ 0.060x₁ +0.900x₂ + e (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? hr (b) How would you interpret ₁ = 0.060, the coefficient of the predictor x₁? O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e.…
A least squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The line is weight=−5.59+0.1826 length. A newborn was 48 cm long and weighed 3 kg. According to the regression model, what was his residual? What does that say about him?
Can you tell how old a lion is by looking at its nose? A professor at the University of Wisconsin-Madison con-ducted a study of data taken from 32 lions and observed the relationship between age (in years) and proportionof blackness in the lion’s nose. The equation of the leastsquares regression line was yn = 0.8790 + 10.6471x where yn is the predicted age of the lion, measured inyears, and x is the proportion of the lion’s nose that isblack. A lion whose nose was 11% black was known to be1.9 years old. What is the residual for the age of this lion?a) -0.15 years b) 0.15 yearsc) 0.88 years d) 2.05 yearse) 10.65 years(Source: http://www.stat.wisc.edu/~st571-1/15-regression-4.pdf)
A least squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The line is weight = -5.25 +0.1696 length. A newborn was 48 cm long and weighed 3 kg. According to the regression model, what was his residual? What does that say about him?
The table lists the average tuition and fees at private colleges and universities for selected years. Year 1985 1990 1995 2000 2008 5311 25,115 Tuition and Fees (in dollars) 9375 SO 12,418 (a) Find the equation of the least-squares regression line that models the data. y 840.450 (Type the slope as a decimal rounded to three decimal places. Round the y-intercept to the nearest integer.) (b) Graph the data and the regression line in the same viewing window using the parameters given below the graph choices. Choose the correct graph below. OA. O B. o HE RO OC. 16,230 A o ƠN
The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Predict the median income of a region in which 30% of adults 25 years and older have at least a bachelor's degree.
A biologist is interested in predicting the percentage increase in lung volume when inhaling (y) for a certain species of bird from the percentage of carbon dioxide in the atmosphere (x). Data collected from a random sample of 20 birds of this species were used to create the least-squares regression equation ŷ = 400-0.08x. Which of the following best describes the meaning of the slope of the least-squares regression line? (A) The percentage increase in lung volume when inhaling increases by 0.08 percent, on average, for every 1 percent increase in the carbon dioxide in the atmosphere. (B) The percentage of carbon dioxide in the atmosphere increases by 0.08 percent, on average, for every 1 percent increase in lung volume when inhaling. (C) The percentage increase in lung volume when inhaling decreases by 0.08 percent, on average, for every 1 percent increase in the carbon dioxide in the atmosphere. (D) The percentage of carbon dioxide in the atmosphere increases by 0.08 percent, on…

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