The x values and standardized residuals for some data are displayed in the accompanying table. Construct a standardized residual plot.x 1.50 1.502.002,50 2.50 3.003.50 3.50 4.00e*0.31 1.03 -1.14 -1.22 0.24 0.74 -1.36 1.54 0.07standardizedstandardizedstandardizedstandardizedresidualsresidualsresidualsresiduals22F2111ofofoF-1-1-1-2-2-2-2-X1.52.02.53.03.54.01.52.02.53.03.54.01.52.02.53.03.54.01.52.02.53.03.54.0Comment on its appearance.O The pattern gives no cause for questioning the appropriateness of the linear regression model, though one observation appears unusual.O The pattern gives no cause for questioning the appropriateness of the linear regression model, and no observation appears unusual.O The quadratic pattern gives cause for questioning the appropriateness of the linear regression model, but no observation appears unusual.O The quadratic pattern gives cause for questioning the appropriateness of the linear regression model, and several observations appear unusual.O The pattern gives no cause for questioning the appropriateness of the linear regression model, though several observations appear unusual.

graph: As we can see the points of scatter plot is so scattered.

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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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Consider the regression below of wealth on income (where income is measured in thousands, e.g., 50 is 50 thousand). wealth_i=2+0.1income_i+0.001(income_i)^2+100college_i If the sum of squared residuals following the regression is 10,000, the regression was estimated with 104 observations, the total variance in college is 200, and the R^squared of a regression of college on income and income squared is 0.75, what is the standard error on the college slope coefficient? 0.41 1.39 2.00 2.02 None of these 1.41
8. a quantile regression where cumulative abnormal returns are regressed on earnings surprises, if the coefficient is 0.1 for the 10th percentile, and 0.5 for the 90th percentile, then it means that the dispersion of returns (measured by the difference between 10th percentile and 90th percentile) increases with earnings surprises. True of False, why?
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A method of estimating earth temperature based on local precipitation is to build models based on 100s of years of earth at several different constant green house gas level (So constant global temperature for 100s of years) , and compare the correlation of statistical relationships between predicted precipitation patterns in a region, and observed precipitation patterns for a region in a few year time period.
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