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.
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.
