ISYE 6414 Regression
Modules 1-2
1. 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.
2. The fitted values are defined as?: The regression line with parameters replaced with the estimated regression
coefficients.
3. 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.
4. The estimators for the regression coefficients are:: Unbiased regardless of the distribution of the data.
5. The assumption of normality:: Is needed for the sampling distribution of the estimators of the regression
coefficients and hence for inference.
6. The estimated versus predicted regression line for a given x*: have the same expectation.
7. The variability in the prediction comes from: the variability due to a new measurement and due to
estimation.
8. Residual analysis can only be used to assess uncorrelated errors.: False
9. Independence assumption can be assess using the normal probability plot.: False
10.Independence assumption can be assessed using the residuals vs fitted values.: False
11.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.
12.If a departure from normality is detected, we transform the predicting variable to improve upon the
normality assumption.: False
13.If a departure from the independence assumption is detected, we trans- form the response variable to
improve upon the independence assumption.: - False
14.The Box-Cox transformation is commonly used to improve upon the linear- ity assumption.: False
15.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.
16.Goodness of fit assessment is done by: residual analysis
17.R-squared (the coefficient of variation) is interpreted as: the percentage of variability in the response variable
explained by the model.
18.The parameters of ANOVA are: the k sample means and the population vari- ance.
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, ISYE 6414 Regression
Modules 1-2
19.The pooled variance estimator is: the sample variance estimator assuming equal variances.
20.In ANOVA, the mean sum of squares divided by N-1 is: the sample variance estimator assuming equal means
and equal variances.
21.MSE measures: the within-treatment variability.
22.MSSTr measures: the between treatment variability.
23.If we reject the test of equal means, we conclude that at least one pair of means are different.: True
24.If we do not reject the test of equal means, we conclude that means are definitely all equal.: False
25.If we reject the test of equal means, we conclude that all treatment means are not equal.: False
26.In ANOVA, the objective of residual analysis is to: evaluate departures from the model assumptions.
27.In ANOVA, the objective of the pairwise comparison is: To identify the statistically significant different
means
28.For assessing the normality assumption of the ANOVA model, we can only use the quantile-quantile normal
plot of the residuals.: False
29.The constant variance assumption is diagnosed using the histogram?: -
False
30.The estimator sigma^2 is a random variable?: True
31.The regression coefficients are used to measure the linear dependence between two variables?: False
32.The mean sum of square errors in ANOVA measures variability within groups: True
33.Beta 1 is an unbiased estimator for Beta 0.: False
34.Under the normality assumptions, the estimator for B1 is a linear com- bindation of randomly
distributed random variables?: True
35.In simple linear regression models, we loose three degrees of freedom because of the estimation of the
three model parameters, B0, B1, and Sig- ma^2?: False
36.The assumptions to diagnose with a linear regression model are indepen- dence, linearity, constant variance,
and normality?: True
37.The sampling distribution for the variance estimator in ANOVA is chi-squared regardless of
the assumptions of data?: False
38.If the constant variance assumption in ANOVA does not hold, the inference on the equality of the means will
not be reliable.: True
39.A negative value of B1 is consistent with an inverse relationship between x and y.: True
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Modules 1-2
1. 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.
2. The fitted values are defined as?: The regression line with parameters replaced with the estimated regression
coefficients.
3. 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.
4. The estimators for the regression coefficients are:: Unbiased regardless of the distribution of the data.
5. The assumption of normality:: Is needed for the sampling distribution of the estimators of the regression
coefficients and hence for inference.
6. The estimated versus predicted regression line for a given x*: have the same expectation.
7. The variability in the prediction comes from: the variability due to a new measurement and due to
estimation.
8. Residual analysis can only be used to assess uncorrelated errors.: False
9. Independence assumption can be assess using the normal probability plot.: False
10.Independence assumption can be assessed using the residuals vs fitted values.: False
11.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.
12.If a departure from normality is detected, we transform the predicting variable to improve upon the
normality assumption.: False
13.If a departure from the independence assumption is detected, we trans- form the response variable to
improve upon the independence assumption.: - False
14.The Box-Cox transformation is commonly used to improve upon the linear- ity assumption.: False
15.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.
16.Goodness of fit assessment is done by: residual analysis
17.R-squared (the coefficient of variation) is interpreted as: the percentage of variability in the response variable
explained by the model.
18.The parameters of ANOVA are: the k sample means and the population vari- ance.
1/
5
, ISYE 6414 Regression
Modules 1-2
19.The pooled variance estimator is: the sample variance estimator assuming equal variances.
20.In ANOVA, the mean sum of squares divided by N-1 is: the sample variance estimator assuming equal means
and equal variances.
21.MSE measures: the within-treatment variability.
22.MSSTr measures: the between treatment variability.
23.If we reject the test of equal means, we conclude that at least one pair of means are different.: True
24.If we do not reject the test of equal means, we conclude that means are definitely all equal.: False
25.If we reject the test of equal means, we conclude that all treatment means are not equal.: False
26.In ANOVA, the objective of residual analysis is to: evaluate departures from the model assumptions.
27.In ANOVA, the objective of the pairwise comparison is: To identify the statistically significant different
means
28.For assessing the normality assumption of the ANOVA model, we can only use the quantile-quantile normal
plot of the residuals.: False
29.The constant variance assumption is diagnosed using the histogram?: -
False
30.The estimator sigma^2 is a random variable?: True
31.The regression coefficients are used to measure the linear dependence between two variables?: False
32.The mean sum of square errors in ANOVA measures variability within groups: True
33.Beta 1 is an unbiased estimator for Beta 0.: False
34.Under the normality assumptions, the estimator for B1 is a linear com- bindation of randomly
distributed random variables?: True
35.In simple linear regression models, we loose three degrees of freedom because of the estimation of the
three model parameters, B0, B1, and Sig- ma^2?: False
36.The assumptions to diagnose with a linear regression model are indepen- dence, linearity, constant variance,
and normality?: True
37.The sampling distribution for the variance estimator in ANOVA is chi-squared regardless of
the assumptions of data?: False
38.If the constant variance assumption in ANOVA does not hold, the inference on the equality of the means will
not be reliable.: True
39.A negative value of B1 is consistent with an inverse relationship between x and y.: True
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