**Regression Analysis of U.S. Farm Income**In an econometric study based on 30 annual observations, researchers examined the relationship between U.S. farm income and four independent variables: grain exports, federal government subsidies, population, and a dummy variable for bad weather years. Using a least squares regression model, the analysis yielded a Durbin-Watson statistic of 1.29, indicating potential issues with autocorrelation in the residuals.Additionally, the regression of the squared residuals (e²_i) on the predicted values (ŷ_i) from the model produced a coefficient of determination (R²) of 0.043, suggesting possible heteroscedasticity.The researchers conducted further statistical tests as follows:(a) **Test for Heteroscedasticity**: Evaluate the variability of residuals to ensure they are consistent across all levels of the independent variables.(b) **Test for Autocorrelated Errors**: Assess whether the residuals from the regression are autocorrelated, which would violate the assumption of independence in ordinary least squares (OLS) regression.In summary, while the initial regression provides insights into the factors affecting U.S. farm income, the diagnostics indicate that further refinement and testing are needed to ensure the reliability and validity of the results.

Given that: Number of observations, n = 30 Durbin Watson statistic, d = 1.29 Coefficient of…

Answered: In a regression based on 30 annual…

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 a sample of cars reviewed by Motor Trend magazine, the mean horsepower (hp) was 150 hp with a standard deviation of 36 hp. The mean weight (lbs) was 2500 lbs with a standard deviation of 720 lbs. Assume the relationship between weight and horsepower is linear and has a correlation of r = +0.55. What is the slope of the linear regression model predicting weight (y-variable) from horsepower (x-variable)? 9 13 15 11
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The following is the result of the multiple linear regression analysis in STATISTICA, where the response Y = lung capacity of a person, xage = age of the person in years, xheight = height of the person in inches, = a categorical variable with 2 levels (0 = non- X smoke smoker, 1 = smoker), and xCaesarean = a categorical variable with 2 levels (0 = normal delivery, 1 = %3D %3D Caesarean-section delivery). b* Std.Err. Std.Err. t(720) p-value N=725 Intercept Age Height Smoke Caesarean of b 0.467772 0.017626 of b* -11.8001 0.1372 0.2790 -0.6407 -25.2263 7.7846 28.6552 -5.0142 0.000000 0.000000 0.000000 0.206427 0.026517 0.026340 0.754765 -0.074205 -0.033054 0.009735 0. 127774 0.092146 0.000001 0.022851 0.014799 0.014492 -0.2102 -2.2808 What is the predicted lung capacity of an 14-year old non-smoker whose height is 71 inches born by normal delivery? (final answer to 4 decimal places)
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