Question: Questions 11-15. Below are several regression analyses involving data

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Questions 11-15. Below are several regression analyses involving data for the 50 states in the U.S., measured in 2001. We will consider several models predicting income per capita (INCOME), from the following predictor variables: BA: percentage of state residents with bachelor's degrees (ranging from 15 to 33 percent) COMMUTE: the average commute time between home and work for the state's residents (ranging from 16 to 32 minutes) R2 Adj. R: Residual SD Regression Equation Model .169 .152 3077 1 Predicted INCOME 11345 392 COMMUTE 1.559 .550 2242 2 Predicted INCOME 6665 585 BA 3 Predicted INCOME 3277 536 BA t 193 COMMUTE .596 .579 2168 Detailed output for Model 3: Standard Coefficients Error t Stat P-value 0.1770 1.4 Intercept 3277 2390.9 7.0 6.94E-09 76.1 BA 536 0.0429 2.1 92.6 COMMUTE 193

Explanation / Answer

The intercept in amultiple regression model is the mean for the response variable (here, INCOME), when all exxplanatory variables-BA and COMMUTE in the context takes 0 value. Therefore, option b is the correct interpretation.

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