1. According to the following graphic, X and Y have _________.

strong negative correlation
virtually no correlation
strong positive correlation
moderate negative correlation
weak negative correlation

2. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a function of batch size (the number of boards produced in one lot or batch). The dependent variable is ______.

3. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The intercept of this model is the ______.

4. If x and y in a regression model are totally unrelated, _______.

5. A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed:
y= 1,550 + 0.36x. If a car is driven 15,000 miles, the predicted cost is ____________.

6. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, “shift” is ______.

7. A multiple regression analysis produced the following tables.
The regression equation for this analysis is ____________.

8. A multiple regression analysis produced the following tables.
These results indicate that ____________.

9. A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The response variable in this model is _______.

10. In regression analysis, outliers may be identified by examining the ________.