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ISYE 6414 Regression Modules 1-2 Updated 2025 with complete solution
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For assessing the normality assumption of the ANOVA model, we can only use the quantile-quantile normal plot of the residuals. - False In simple linear regression models, we loose three degrees of freedom because of the estimation of the three model parameters, B0, B1, and Sigma^2? - False In e...

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  • September 1, 2024
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  • 2024/2025
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ISYE 6414 Regression Modules 1-2

For assessing the normality assumption of the ANOVA model, we can only use the quantile-quantile
normal plot of the residuals. - False



In simple linear regression models, we loose three degrees of freedom because of the estimation of the
three model parameters, B0, B1, and Sigma^2? - False



In evaluating a simple linear model - there is a direct relationship between the coefficient of
determination and the correlation between the predicting and response variables.



Assuming that the data are normally distributed, under the simple linear model, the estimated variance
has the following sampling distribution: - Chi-squared with n-2 degrees of freedom.



The fitted values are defined as? - The regression line with parameters replaced with the
estimated regression coefficients.



The estimators fo the linear regression model are derived by? - Minimizing the sum of squared
differences between the observed and expected values of the response variable.



The estimators for the regression coefficients are: - Unbiased regardless of the distribution of the
data.

The estimated versus predicted regression line for a given x* - have the same expectation.



The variability in the prediction comes from - the variability due to a new measurement and due
to estimation.



Residual analysis can only be used to assess uncorrelated errors. - False



Independence assumption can be assess using the normal probability plot. - False

, Independence assumption can be assessed using the residuals vs fitted values. - False



We detect departure from the assumption of constant variance - when the residuals vs fitted
values are larger in the ends but smaller in the middle.



If a departure from normality is detected, we transform the predicting variable to improve upon the
normality assumption. - False



If a departure from the independence assumption is detected, we transform the response variable to
improve upon the independence assumption. - False



The Box-Cox transformation is commonly used to improve upon the linearity assumption. - False



Goodness of fit assessment is done by - residual analysis



R-squared (the coefficient of variation) is interpreted as - the percentage of variability in the
response variable explained by the model.



The parameters of ANOVA are - the k sample means and the population variance.



The pooled variance estimator is - the sample variance estimator assuming equal variances.



In ANOVA, the mean sum of squares divided by N-1 is - the sample variance estimator assuming
equal means and equal variances.



MSE measures - the within-treatment variability.



MSSTr measures - the between treatment variability.

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