Question

2 Which one of the following statements is true about the second step in the process for regression

forecasting?

• An analyst must determine whether the relationship is statistically significant based on a t-test.
• An analyst must evaluate the model for serial correlation.
• An analyst must evaluate the explanatory power of the model.
• An analyst must evaluate whether the model is logical.

3 A negative serial correlation exists when a _______________ error is followed by a _______________ error.

• negative, positive
• negative, negative
• positive, positive
• positive, negative

4 Which one of the following correctly explains the difference between a trend model and a causal model?

• A trend model uses a form of smoothing analysis to project the past time trend forward while the causal model looks at a change in an independent variable that causes a change in a dependent variable.
• A trend model looks at the past time trend to apply regression analysis while the causal model looks at a change in a dependent variable that causes a change in an independent variable.
• A trend model identifies the factors causing change and places them into a bivariate regression model while the causal model matches the slope of the trend through an independent variable tied to a dependent variable’s change.
• A trend model tracks the past time trend and projects it forward while the causal model looks at a change in an independent variable that causes a change in a dependent variable.

5 Why is the first step in the regression model evaluation so important?

• We desire the explanatory power of the model to be at least 84% of the variation in the dependent variable.
• We want the relationship to be statistically significant at the desired level of confidence.
• We would never want to use a relationship that does not conform to business/economic logic.
• We need to determine if the Durbin-Watson test is within our range of zero to four to rule out serial correlation.

6 Visualization of data allows you to ____________________.

• be as transparent to management as required
• see stark differences that would not be apparent from the descriptive statistics
• better understand if you need more data
• more clearly identify the dependent and independent variables

7 What is heteroscedasticity?

• When the error terms in the population regression have a constant variance across all values of the independent variable.
• When the scatter plot of residuals falls in a horizontal band.
• When the standard errors of the regression coefficients may be underestimated causing the calculated t-ratios to be larger than they should be.
• When the scatter plot of residuals falls in a vertical band.

8 What assumption does the causal model make?

• Changes only occur in the variable to be forecast, but that change is not related to the independent variable.
• No changes occur.
• Changes in the independent variable will cause a change in the variable to be forecast.
• Changes in the dependent variable will cause changes to other dependent variables.

9 Heteroscedasticity is more common with _______________ data than with _______________ data?

• time-series, cross-sectional
• cross-sectional, time-series
• qualitative, quantitative
• cross-sectional, qualitative

10 What is the primary purpose of the third step when you are evaluating a linear regression model?

Looking for a Similar Assignment? Order now and Get 10% Discount! Use Coupon Code "Newclient"