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

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

1. According to the following graphic, X and Y have .strong negative correlationvirtually no correlationstrong positive correlationmoderate negative correlationweak negative correlation2. 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 .?


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