# Stats Problems Essay

1793 Words Aug 30th, 2014 8 Pages
1. The regression analysis at the bottom relates average annual per capita beef consumption (in pounds) and the independent variables "annual per capita pork consumption" (in pounds) and "average annual beef price" (in dollars per pound).   The coefficient for beef price, -12, tells us that: 1. For every \$1 increase in beef price, average beef consumption decreases by 12 lbs, not controlling for pork consumption. 2.  For every \$12 drop in beef price, average beef consumption decreases by 1 lbs, not controlling for pork consumption. 3. For every \$1 increase in beef price, average beef consumption decreases by 12 lbs, controlling for pork consumption, i.e. holding pork consumption constant. 4. For every \$12 decrease in beef …show more content…
*  2. Employment level only. *  3. Both independent variables. 4. Neither independent variable. *
4. The regression analysis below relates the value of new car sales (in millions of dollars) to compensation (in billions of dollars) and the employment level in the non-agricultural sector (in thousands) for 44 consecutive quarters.   Which of the two independent variables is statistically significant at the 0.05 level? * 1. Compensation only. *  2. Employment level only. * 3.  Both independent variables.
4. Neither independent variable.

5. The regression analysis below relates the value of new car sales (in millions of dollars) and the independent variables "compensation" (in billions of dollars) and "employment level in the non-agricultural sector" (in thousands) for 44 consecutive quarters. Compare this multiple regression to the simple regressions with compensation and employment level as the respective independent variables.   Which of the following is the likely culprit of the dramatic increase in the p-value for employment level in the multiple regression?
1.  Multi-collinearity.
2.  Heteroskedasticity.
3. Nonlinearity. 4.  None of the above.

6. The regression analysis below relates the value of new car sales (in millions of dollars) and the independent variables "compensation" (in billions of dollars) and "employment level in the non-agricultural sector" (in thousands) for 44

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