Econometrics Questions
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Assessing the regression model on data other than the sample data that was used to generate the model is known as a. approximation. b. cross-validation. c. graphical validation. d. postulation.
Regression analysis involving one dependent variable and more than one independent variable is known as a. simple regression. b. linear regression. c. multiple regression. d. None of these are correct.
__________ is the data set used to build the candidate models. a. Range b. Codomain c. Validation set d. Training set
A variable used to model the effect of categorical independent variables in a regression model is known as a a. dependent variable. b. response. c. dummy variable. d. predictor variable.
__________ refers to the scenario in which the relationship between the dependent variable and one independent variable is different at different values of a second independent variable. a. Interaction b. Multicollinearity c. Autocorrelation d. Covariance
Fitting a model too closely to sample data, resulting in a model that does not accurately reflect the population is termed as a. approximation. b. hypothesizing. c. overfitting. d. postulating.
The degree of correlation among independent variables in a regression model is called a. multicollinearity. b. interaction. c. the coefficient of determination. d. the sum of squared errors (SSE).
The prespecified value of the independent variable at which its relationship with the dependent variable changes in a piecewise linear regression model is referred to as the a. milestone. b. knot. c. tipping point. d. watchpoint.
Which of the following regression models is used to model a nonlinear relationship between the independent and dependent variables by including the independent variable and the square of the independent variable in the model? a. Multiple regression model b. Quadratic regression model c. Simple regression model d. Least squares regression model
__________ refers to the data set used to compare model forecasts and ultimately pick a model for predicting values of the dependent variable. a. Codomain b. Training set c. Validation set d. Range
The ___________ is a measure of the goodness of fit of the estimated regression equation. It can be interpreted as the proportion of the variability in the dependent variable y that is explained by the estimated regression equation. a. residual b. coefficient of determination c. dummy variable d. interaction variable
__________ is used to test the hypothesis that the values of the regression parameters ß1, ß2, ... ßq are all zero. a. An F test b. A t test c. The least squares method d. Extrapolation
The least squares regression line minimizes the sum of the a. differences between actual and predicted y values. b. absolute deviations between actual and predicted y values. c. absolute deviations between actual and predicted x values. d. squared differences between actual and predicted y values.
A variable used to model the effect of categorical independent variables in a regression model which generally takes only the value zero or one is called a. a residual. b. the coefficient of determination. c. a dummy variable. d. interaction.
A normally distributed error term with a mean of zero would a. have values that are symmetric about the variance. b. allow more accurate modeling. c. yield biased regression estimates. d. be a hyperbolic curve.
Prediction of the value of the dependent variable outside the experimental region is called a. interpolation. b. forecasting. c. averaging. d. extrapolation.
__________ refers to the degree of correlation among independent variables in a regression model. a. Multicollinearity b. Tolerance c. Rank d. Confidence level
The graph of the simple linear regression equation is a(n) a. ellipse. b. hyperbola. c. parabola. d. straight line.
What would be the coefficient of determination if the total sum of squares (SST) is 23.29 and the sum of squares due to regression (SSR) is 10.03? a. 2.32 b. 0.43 c. 0.19 d. 0.89
The process of making estimates and drawing conclusions about one or more characteristics of a population through analysis of sample data drawn from the population is known as a. inductive inference. b. deductive inference. c. statistical inference. d. Bayesian inference.