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WorksheetsRegression Analysis Quiz
Total questions: 40
Worksheet time: 20mins
In regression analysis, a model is ................................. if a relevant variable is not omitted from the model.
Normally distributed
Linear
Correctly specified
Homoscedastic
Statistically significant
The ................................. is a statistical test used to determine whether there is a significant relationship between the dependent variable and the independent variables in a regression model.
T-test
F-test
Chi-square test
Z-test
Shapiro-Wilk test
If a regression model includes a dummy variable for gender (Male = 1, Female = 0), the coefficient on the dummy variable represents:
The average value of the dependent variable for females
The slope of the regression line for females
The intercept for males
The difference in the dependent variable between males and females
If we are plotting the values of the residuals chronologically, we are testing for (a) .
In econometrics, the ................................. is a measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variables.
R-squared
Adjusted R-squared
Standard error
P-value
F-value
The ................................. is a method used to estimate the parameters of a regression model by minimizing the sum of the squared differences between the observed and predicted values.
Regression
Ordinary Least Squares
F-test
T-test
R-squared
................................. refers to the situation where the error terms in a regression model are correlated with each other, violating the assumption of independence.
Multicollinearity
Heteroscedasticity
Autocorrelation
Homoscedasticity
Normality
The ................................. is a graphical representation used to check for the presence of heteroscedasticity by plotting the residuals against the fitted values.
Bar graph
Residual plot
Histogram
Box plot
The ................................. is a measure of the extent to which the independent variables in a regression model are linearly related to each other.
Variance inflation factor
Correlation coefficient
Standard error
Standard deviation
R-squared
................................. occurs when the explanatory variables in a regression model are highly correlated, making it difficult to estimate the individual effects of each variable.
Multicollinearity
Heteroscedasticity
Autocorrelation
Homoscedasticity
Non-normality
The ................................. is a test used to determine whether the residuals from a regression model are normally distributed.
Jarque-Bera test
White's test
Breusch-Godfrey test
Durbin-Watson test
Breusch-Pagan test
An estimator is said to be unbiased if:
Its variance is zero
It minimizes the sum of squared residuals
Its expected value equals the true parameter value
It is consistent
Its standard error is inflated
The ................................. is a statistical method used to assess the goodness of fit of a regression model.
Correlation coefficient
R-squared
Adjusted R-squared
P-value
F-statistic
In regression analysis, ................................. occurs when the variance of the residuals is not constant across all levels of the independent variable.
Heteroscedasticity
Multicollinearity
Homoscedasticity
Normality
Autocorrelation
(a) variables are used to represent qualitative data in regression models.
The ................................. is a technique used to detect the presence of multicollinearity in a regression model.
Variance inflation factor
Durbin-Watson test
Shapiro-Wilk test
Breusch-Pagan test
Jarque-Bera test
Which of the following is NOT a consequence of omitted variable bias?
Unbiasedness of OLS estimates
Biased coefficient estimates
Incorrect signs of coefficients
Inflated standard errors
The ................................. is a method used to evaluate the significance of individual predictors in a regression model.
Chi-square test
T-test
Regression analysis
F-test
ANOVA
The ................................. is a diagnostic tool used to assess whether the residuals of a regression model are independent.
Durbin-Watson test
Variance inflation factor
Shapiro-Wilk test
Breusch-Pagan test
Jarque-Bera test
When using dummy variables, the omitted category is called:
Base slope
Benchmark group
Control group
Interaction group
Inference category
In regression analysis, ................................. refers to the situation where the model fails to capture the true relationship between the dependent and independent variables.
Specification error
Multicollinearity
Autocorrelation
Overfitting
Heteroscedasticity
The ................................. is a statistical method used to assess the relationship between a dependent variable and multiple independent variables.
Polynomial regression
Logistic regression
Multiple regression
Simple linear regression
If an estimator is unbiased but has a large variance, it is:
Efficient
Inefficient
BLUE
Consistent
(a) is a condition where past values of a variable influence its current value.
The ................................. is a method used to check for the presence of autocorrelation in the residuals of a regression model.
Durbin-Watson test
Shapiro-Wilk test
Breusch-Pagan test
Variance inflation factor
In regression analysis, the assumption that the error term has a constant variance is called __________________________ .
Serial correlation
Heteroscedasticity
Multicollinearity
Homoscedasticity
Normality of residuals
The __________________________ test is used to detect autocorrelation in the residuals of a regression model.
Breusch-Pagan test
Shapiro-Wilk test
White's test
Breusch-Godfrey test
__________________________ refers to the problem that arises when an explanatory variable is correlated with the error term.
Non-normality of residuals
Omitted variable bias
Autocorrelation
Serial correlation
Multicollinearity
__________________________ is a technique used to transform a non-stationary time series into a stationary one.
OLS
Squaring
Differencing
Log transformation
__________________________ occurs when the functional form of the model does not correctly represent the true relationship between variables.
Multicollinearity
Misspecification
Normality
Log transformation
The __________________________ is a measure of how much the estimated regression coefficients are inflated due to multicollinearity.
Standard error
Variance inflation factor
Serial correlation
AR(1)
Bias
Which of the following assumptions is required for the Ordinary Least Squares (OLS) estimator to be unbiased?
The error term has a non-zero mean
The explanatory variables are correlated with the error term
The error term has a zero mean and is uncorrelated with the explanatory variables
The sample size is infinite
(a) shows the direction and strength of relationship between variables.
Multicollinearity refers to:
Correlation between the dependent variable and error term
Correlation between dependent and independent variables
Correlation among residuals
Correlation between predicted and observed values
Correlation among independent variables
Which of the following indicates heteroscedasticity in a regression model?
Constant variance of error terms
Increasing variance of residuals with fitted values
Residuals are normally distributed
Independent variables are uncorrelated
Stronger correlation between independent variables
Which of the following is a consequence of multicollinearity?
Biased OLS estimates
Overestimated regression coefficients
Increased R²
Reduced residual variance
Inflated standard errors of coefficients
Which of the following is true about heteroscedasticity?
It leads to biased OLS estimates
It violates the assumption of no multicollinearity
It makes the model specification incorrect
It affects the efficiency of OLS estimates
The (a) in a regression model is the difference between the observed value of the dependent variable and the value predicted by the model.
What is the main purpose of using dummy variables in regression analysis?
To reduce multicollinearity
To test for heteroscedasticity
To represent quantitative variables logically
To detect autocorrelation
To represent categorical variables numerically
The “dummy variable trap” refers to:
Perfect multicollinearity caused by including all categories as dummies
Incorrect coding of categorical variables
Using too few dummy variables
Dropping the intercept from the model
