GB513 Unit 4 Quiz Latest 2017 May Question 1 2 / 2 points According to the following graphic, X and Y have: 1) strong negative correlation 2) virtually no correlation 3) strong positive correlation 4) moderate negative correlation 5) weak negative correlation Question 2 2 / 2 points 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 independent variable is: 1) batch size 2) unit variable cost 3) fixed cost 4) total cost 5) total variable cost Question 3 2 / 2 points 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: 1) batch size 2) unit variable cost 3) fixed cost 4) total cost 5) total variable cost Question 4 2 / 2 points If x and y in a regression model are totally unrelated: 1) the correlation coefficient would be -1 2) the coefficient of determination would be 0 3) the coefficient of determination would be 1 4) the SSE would be 0 5) the MSE would be 0s Question 5 2 / 2 points 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 10,000 miles, the predicted cost is: 1) 2090 2) 3850 3) 7400 4) 6950 5) 5150 Question 6 2 / 2 points 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: 1) a response variable 2) an independent variable 3) a quantitative variable 4) a dependent variable 5) a constant Question 7 2 / 2 points A multiple regression analysis produced the following tables: Predictor Coefficients Standard Error tStatistic p-value Intercept 616.6849 154.5534 3.990108 0.000947 x1 -3.33833 2.333548 -1.43058 0.170675 x2 1.780075 0.335605 5.30407 5.83E-05 Source df SS MS F p-value Regression 2 121783 60891.48 14.76117 0.000286 Residual 15 61876.68 4125.112 Total 17 183659.6 The regression equation for this analysis is: 1) y = 616.6849 + 3.33833 x1 + 1.780075 x2 2) y = 154.5535 – 1.43058 x1 + 5.30407 x2 3) y = 616.6849 – 3.33833 x1 – 1.780075 x2 4) y = 154.5535 + 2.333548 x1 + 0.335605 x2 5) y = 616.6849 – 3.33833 x1 + 1.780075 x2 Question 8 2 / 2 points A multiple regression analysis produced the following tables: Predictor Coefficients Standard Error tStatistic p-value Intercept 752.0833 336.3158 2.236241 0.042132 x1 11.87375 5.32047 2.031711 0.082493 x2 1.908183 0.662742 2.879226 0.01213 Source df SS MS F p-value Regression 2 203693.3 101846.7 6.745406 0.010884 Residual 12 181184.1 15098.67 Total 14 384877.4 These results indicate that: 1) none of the predictor variables are significant at the 5% level 2) each predictor variable is significant at the 5% level 3) x1 is the only predictor variable significant at the 5% level 4) x2 is the only predictor variable significant at the 5% level 5) the intercept is not significant at the 5% level Question 9 2 / 2 points 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: 1) heated area 2) number of bedrooms 3) market value 4) central heating 5) residential houses Question 10 2 / 2 points In regression analysis, outliers may be identified by examining the: 1) coefficient of determination 2) coefficient of correlation 3) p-values for the partial coefficients 4) residuals 5) R-squared value