William used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). His estimated regression equation was Crime = 428 + 0.050 Income. We can conclude that:
A: crime seems to create additional income in a city
B: the slope is small so Income has no effect on Crime
C: wealthy individuals tend to commit more crimes, on average
D: by itself the intercept is irrelevant since zero median income is impossible in a large city
ANSWER:
The data relates to large US cities. In such large cities Zero income is not possible , and is a meaningless term , which renders the intercept to be completely meaningless.
Thus the correct answer choice is "SECOND Statement"
William used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual...
For a sample of 20 New England cities, a sociologist studies the crime rate in each city (crimes per 100,000 residents) as a function of its poverty rate (in %) and its median income (in $1,000s). A portion of the regression results is as follows. [You may find it useful to reference the t table.) F 0.05 ANOVA Regression Residual Total M S 229.2 4,464.56 Significance F 0.950 df SS 2 458.3 17 75,897.54 1976,355.9 Intercept Poverty Income Coefficients 754.4596...
For a sample of 20 New England cities, a sociologist studies the crime rate in each city (crimes per 100,000 residents) as a function of its poverty rate (in %) and its median income (in $1,000s). A portion of the regression results is shown in the accompanying table. Use Table 2 and Table 4. ANOVA df SS MS F Significance F Regression 2 2,517.3 1,258.6 7.49E-01 Residual 17 72,837.53 4,284.56 Total 19 75,354.80 Coefficients...