What is the difference between using robust standard errors for hypothesis testing versus the homoskedastic only case?Robust standard errors ensure we have an unbiased estimation of our OLS slope coefficient.a.b Robust standard errors account for potential heteroskedasticity in the variation of the cross-sectional units.None of theseC.Robust standard errors are usually equal to standard errors in the homoskedastic case.d.Robust standard errors are used in the case that variance is constant in the data. Which of the following is a good indication that nonlinear terms might be necessary as control varlables?A clear, nonlinear relationship in the residuals.a.b.Failure to reject the null that the variable of interest is 0.None of these.C.When the correlation is weak between the varlables.d.e A clear, nonlinear relationship when plotting the data of the regressors.

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Answered: What is the difference between using…

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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Suppose we have two SRSS from two distinct populations and the samples are independent. We measure the same variable for both samples. Suppose both populations of the values of these variables are normally distributed but the means and standard deviations are unknown. For purposes of comparing the two means, we use (a) Two-sample t procedures (b) Matched pairs t procedures (c) z procedures (d) The least-squares regression line (e) None of the above. The answer is
2) Attached to the end is a scatterplot of predicted by residual values fora simple linear regression analysis. Use this plot to comment briefly on the validity of theassumptions of linearity, independence, normality, and homogeneity of variance for this simplelinear regression analysis. 3) If you are planning a research study in which x values are predetermined, and then yvalues are measured in order to perform a simple linear regression analysis, would the standarderror of the slope estimate in the model yi = B0 + B1xi + ei be lower by placing the x valuesrelatively close together, or far apart? Briefly explain. (Hint: look at the formula for thestandard error of the slope)
An engineer has designed a valve that will regulate water pressure on an automobile engine. The valve was tested on 260 engines and the mean pressure was 4 lbs/square inch. Assume the variance is known to be 0.64. If the valve was designed to produce a mean pressure of 4.1 lbs/square inch, is there sufficient evidence at the 0.02 level that the valve does not perform to the specifications? State the null and alternative hypotheses for the above scenario.
5. We don't want predictor variables that are too highly correlated with each other or they are redundant (say ≥±.70) or multicollinear (2±.90), which means the variables are measuring pretty much the same thing so why have them all in the equation. What was the finding for redundancy between SPI Scientist and SPI Practitioner? a. No issue with redundancy since the shared variance between the two variables was only 4% (r2 = .04). b. There was an issue with redundancy between the two variables shared 62% of the variance in the relationship.
8 If the analysis of variance F-test leads to the conclusion that at least one of the model parameters is nonzero, can you conclude that the model is the best predictor for the dependent variable y? Can you conclude that all of the terms in the model are important for predicting y? What is the appropriate conclusion?
A carpenter is making doors that are 2058 millimeters tall. If the doors are too long they must be trimmed, and if they are too short they cannot be used. A sample of 61 doors is made, and it is found that they have a mean of 2044 millimeters with a variance of 729. Is there evidence at the 0.05 level that the doors are either too long or too short? State the null and alternative hypotheses for the above scenario.
The article "Approach to Confidence Interval Estimation for Curve Numbers" (R. McCuen, Journal of Hydrologic Engineering, 2002:43-48) discusses the relationship between rainfall depth and runoff depth at several locations. At one particular location, rainfall depth and runoff depth were recorded for 13 rainstorms. Following is MFNITAB output for a fit of the least-squares line to predict runoff depth from rainfall depth (both measured in inches). The regression equation is Runoff =-0.23 + 0.73 Rainfall Predictor Coef SE Coef тР Constant -0.23429 0.23996 -0.98 0.350 Rainfall 0.72868 0.06353 11.47 0.000 S= 0.40229 R-Sq = 92.3% R-Sq(adj) = 91.6% Analysis of Variance Source Regression 121.290 21.290131.550.000 Residual Епог SS MS F P 11 1.7800.16184 Total 1223.070 a Predict the runoff for a storm with 2.5 in. of rainfall. b. Someone claims that if two storms differ in their rainfall by 1 in, then their runoffs will differ, on the average, by 1 in. as well. Is this a plausible claim?…
Select all statements that are true of the least-squares regression line. ) It assumes a linear relationship between the explanatory and the response variable. R-squared is the portion of sample variance in the explanatory variable that can be explained by variation in the response variable. ) It can be used to predict the mean response of the explanatory variable. | R-squared is the portion of sample variance in the y variable that can be explained by variation in the x variable.

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