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20 Cards in this Set
- Front
- Back
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In the multiple regression model, the t-stat for testing that the slope is significantly different from zero is calculated: |
by dividing the estimate by its standard error. |
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in a two regression model, if you exclude one of the relevant variables then |
you are no longer controlling for the influence of the other variable |
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Question 2 is a long BS question about housing value. Its just plug and chug |
sorry boutcha |
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imperfect multicollinearity: |
implies that it will be difficult to estimate precisely one or more of the partial effects using the data at hand |
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Lots of regressions. See prob 4 |
:( |
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question 5, more regressions |
:) |
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question 6 -- more of the same |
regressssions |
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in the multiple regression model, the t- statistic for testing that the slope is significantly different from zero is calculated |
by dividing the estimate by its standard error |
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Imperfect multicollinearity |
means the two or more of the regressors are highly correlated |
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question 9... nope. |
regreeeesssssions |
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The Homoskedasticity - only F - statistic and the hereroskedasticity - robust F - statistic typically are : |
Different |
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If the estimates of the coefficients of interest change substantially across specifications: |
then this often provides evidence that the original specification had omitted variable bias. |
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Suppose you run a regression of test scores against parking lot area per pupil. Is the R^2 likely to be high or low? |
High, because parking lot area is correlated with student teacher ratio, which whether the school is in a suburb or a city, and possibly with district income |
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A recent study found that the death rate for people who sleep 6 -7 hrs per night is lower than death rate for people who sleep 8+ hrs. the 1.1 million observations used for this study came from a random survey of Americans aged 30-102. Each survey respondent was tracked 4 yrs. The death rate for people sleeping 7 hrs was calculated as the ratio of the number of deaths over the span of the study among people sleeping 7 hrs to the # of survey respondents who slept 7 hrs. This calculation was then repeated for people sleeping 6 hrs, and so on. Based on this summary, would you recommend that Americans who sleep 9 hrs per night consider reducing their sleep to 6 or 7 hrs if they want to prolong their lives? Why or why not? Explain.
Which of the following variables are likely useful to add to the regression to control for important omitted variables? |
indicator for chronic illness drug or alcohol use Type of employment |
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Q 15 got that fat regression |
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Q 16 got that fat regression |
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The critical value of F 4∞ at the 5% significance level is: |
2.37 |
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If you had a two-regressor regression model, then omitting one variable that is relevant: |
Can result in a negative value for the coefficient of the included variable, even though the coefficient will have a significant positive effect on Y if the omitted variable were included |
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The following OLS assumption is more likely violated by omitted variable bias: |
E(U_i | X_i) = 0 |
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The adjusted r^2, or R^2 bar is given by: |
1 - (n-1)/(n-k-1)(SSR/TSS) |
