Econometrics Questions

Important risk factors for high blood pressure reported by the National Institute of Health include weight and ethnicity.High blood pressure is common in adults who are overweight and are African American.a public policy researcher in Atlanta surveyed 150 adult men about 5′10″ in height and in the 55-60 age group. Data were collected on their systolic pressure, weight (in pounds), and race (Black = 1 for African American, 0 otherwise). The resulting regression equation is: Systolic = 80.2085 + 0.3901Weight + 6.9082Black. What is the expected Systolic blood pressure for a 170 pound black male?150157153.44146.52

At a University of California campus, data were collected on the starting salary of business graduates (Salary in $1,000s) along with their cumulative GPA, whether they have an MIS concentration (MIS = 1 if yes, 0 otherwise), and whether they have a statistics minor (Statistics = 1 if yes, 0 otherwise). Use the estimated equation Salary = 44.0073 + 6.6227GPA + 6.6071MIS + 6.7309Statistics. What is the additional salary a graduate would earn with an MIS degree?$6,7309$6,6227Can not determine$6,607

What is the term in regression models when a predictor variable has a different partial effect on the outcome depending on the values of another predictor variable?Predictive analyticsInteraction effectLeast squares analysisPartial effect

An educational researcher is trying to analyze the determinants of the applicant pool for the specialized Master of Science in Accounting (MSA) program. Two important determinants are the marketing expense of the business school and the percentage of the MSA alumni who were employed within three months after graduation. Using the equation Applicantŝ = −49.5490 + 0.3550Marketing + 2.0Employed, answer the following question. If the number employed increased by 30, how many more applicants would there have been?5060Cannot answer without knowing how many total were employed30

We can plot the residuals sequentially over time to look for correlated observations. How are violations indicated?-When positive residuals are shown consistently over time and negative residuals are shown consistently over time-When all the residuals are negative-When positive residuals and negative residuals alternate over a few periods, sometimes positive or negative for a couple of periods.-There is no detection method

A crucial assumption in a linear regression model is that the error term is not correlated with the predictor variables. In general, when does this assumption break down?-When there are too many variables in the model-When important predictor variables are excluded.-The estimated standard errors of the OLS estimators are inappropriate-When the standard errors are distorted downward

If one or more of the relevant predictor variables are excluded, then the resulting OLS estimators are biased. The extent of the bias depends on the degree of the '____________" between the included and the excluded predictor variables.

An important first step before running a regression model is to compile a comprehensive list of potential predictor variables. How can we reduce the list to a smaller list of predictor variables?-The best approach may be to do nothing-We must include all relevant variables-Use the adjusted R2 criterion to reduce the list-We use R to make the necessary correction

The assumption of constant variability of observations often breaks down in studies with cross-sectional data. Consider the model y = β0 + β1x + ɛ, where y is a household's consumption expenditure and x is its disposable income. It may be unreasonable to assume that the variability of consumption is the same across a cross-section of household incomes. This violation is called:Nonlinear PatternsMulticollinearityChanging variabilityCorrelated Observations

We can plot the residuals sequentially over time to look for correlated observations. If there is no violation, then what would you see?-The residuals should show no pattern around the vertical axis.-The residuals should show a normal pattern around the horizontal axis.-The residuals should show no pattern around the horizontal axis.-The residuals should show a normal pattern around the vertical axis.

True or false: Linearity is justified if the residuals are randomly dispersed across the values of a predictor variable.TrueFalse

We can use residual plots to gauge changing variability. The residuals are generally plotted against each predictor variable xj. There is a violation if the variability increases or '______________' over the values of xj.

In the presence of changing variability, the OLS estimators are '_______________________', but their estimated standard errors are inappropriate

When confronted with multicollinearity, the best approach may be to do '_______________________' if the estimated model yields a high R2,

Suppose the competing hypotheses in testing for individual significance are H0: βj = 0 versus HA: βj ≠ 0. What would rejecting the null hypothesis imply?xj explains all the variation in yxj is not significant in explaining yxj is significant in explaining yWe would accept the null hypothesis

What is a good solution when confronted with multicollinearity?Select all that apply Multiple select question.-Add another variable-Obtain more data because the sample correlation may get weaker-Drop one of the collinear variables-Obtain more data because a bigger sample is always better

If residual plots exhibit strong nonlinear patterns, the inferences made by a linear regression model can be quite misleading. In such instances, we should employ nonlinear regression methods based on simple transformations of the '___________________' and the predictor variables.

In the presence of changing variability, the estimated standard errors of the OLS estimators are inappropriate. What does this imply about using standard testing?-We should use F tests only-Standard t or F tests are not valid as they are based on these estimated standard errors.-Use standard t or F tests-We should use standard t tests only

In the presence of correlated observations, the OLS estimators are unbiased, but their estimated standard errors are inappropriate. Which of the following could happen as a result?-The model looks better than it really is with a spuriously high R2-The F test may suggest that the predictor variables are individually and jointly significant when this is not true-All of the answers are correct-The t test may suggest that the predictor variables are individually and jointly significant when this is not true

The detection methods for multicollinearity are mostly informal. Which of the following indicate a potential multicollinearity issue? -Individually insignificant predictor variables-High R2 plus individually insignificant predictor variables-High R2 and significant F statistic coupled with insignificant predictor variables-Significant F statistic coupled with individually insignificant predictor variables