**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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A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program per-year tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The least squares equation was found Y; = -13258.594 + 2.422X;, where X; is the program per-year tuition and Y; is the predicted mean starting salary. To perform a residual analysis for these data, the following results are obtained. of regression have been seriously violated. Residual index plot QQ Plot of Residuals Residuals Residuals 20000- 20000 0. -20000 -20000 a) To evaluate whether the assumption of linearity has been violated, which of the following graph shou be examined? A. Predicted Values vs. Residuals B. Residual index plot C. QQ plot of residuals D. Residuals vs. Progrm Per-Year Tuition ($) b) To evaluate whether the assumption of normality has been violated, which of the following graph…

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