# Isye 6414 Units 1 - 3 Review

78 Questions | Total Attempts: 148  Settings Create your own Quiz • 1.
Under the normality assumption, the estimator for β1 is a linear combination of normally distributed random variables.
• A.

True

• B.

False

• 2.
In the regression model, the variable of interest for study is the response variable.
• A.

True

• B.

False

• 3.
The constant variance is diagnos=ted using the quantile-quantile normal plot.
• A.

True

• B.

False

• 4.
β1^ is an unbiased estimator for β0.
• A.

True

• B.

False

• 5.
The estimator σ^2 is a fixed variable.
• A.

True

• B.

False

• 6.
Only the log-transformation of the response variable can be used when the normality assumption does not hold.
• A.

True

• B.

False

• 7.
The only assumptions for a linear regression model are linearity, constant variance, and normality.
• A.

True

• B.

False

• 8.
A negative value of β1 is consistent with a direct relationship between x and Y.
• A.

True

• B.

False

• 9.
In the simple linear regression model, we lose three degrees of freedom because of the estimation of the three model parameters, β0, β1, and σ^2.
• A.

True

• B.

False

• 10.
The regression coefficient is used to measure the linear dependence between two variables.
• A.

True

• B.

False

• 11.
If the constant variance assumption in ANOVA does not hold, the inference on the equality of the means will not be reliable.
• A.

True

• B.

False

• 12.
If one confidence interval in the pairwise comparison includes zero, we conclude that the two means are plausibly equal.
• A.

True

• B.

False

• 13.
The mean sum of square errors in ANOVA measures variability within groups.
• A.

True

• B.

False

• 14.
The linear regression model with a qualitative predicting variable with k levels/classes will have k+1 parameters to estimate.
• A.

True

• B.

False

• 15.
If one confidence interval in the pairwise comparison includes only positive values, we conclude that the difference in means is statistically significantly positive.
• A.

True

• B.

False

• 16.
The number of degrees of freedom of the χ2 (chi-square) distribution for the variance estimator is N−k+1 where k is the number of samples.
• A.

True

• B.

False

• 17.
For assessing the normality assumption of the ANOVA model, we can use the quantile-quantile normal plot and the historgram of the residuals.
• A.

True

• B.

False

• 18.
We can assess the assumption of constant-variance by plotting the residuals against fitted values.
• A.

True

• B.

False

• 19.
The ANOVA is a linear regression model with two qualitative predicting variables.
• A.

True

• B.

False

• 20.
The sampling distribution for the variance estimator in ANOVA is χ2 (chi-square) regardless of the assumption of the data.
• A.

True

• B.

False

• 21.
In a multiple linear regression model with 6 predicting variables but without intercept, there are 7 parameters to estimate.
• A.

True

• B.

False

• 22.
The only objective of multiple linear regression is prediction.
• A.

True

• B.

False

• 23.
We can make causal inference in observational studies.
• A.

True

• B.

False

• 24.
In order to make statistical inference on the regression coefficients, we need to estimate the variance of the error terms.
• A.

True

• B.

False

• 25.
We cannot estimate a multiple linear regression model if the predicting variables are linearly dependent.
• A.

True

• B.

False Back to top