QuestionQuestion 1 2 / 2 pointsAccording to the following graphic X and Y have:1) strong negative correlation2) virtually no correlation3) strong positive correlation4) moderate negative correlation5) weak negative correlationQuestion 2 2 / 2 pointsA 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 size2) unit variable cost3) fixed cost4) total cost5) total variable costQuestion 3 2 / 2 pointsA 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 size2) unit variable cost3) fixed cost4) total cost5) total variable costQuestion 4 2 / 2 pointsIf x and y in a regression model are totally unrelated:1) the correlation coefficient would be -12) the coefficient of determination would be 03) the coefficient of determination would be 14) the SSE would be 05) the MSE would be 0sQuestion 5 2 / 2 pointsA 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 = 1550 + 0.36x.If a car is driven 10000 miles the predicted cost is:1) 20902) 38503) 74004) 69505) 5150Question 6 2 / 2 pointsA 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 variable2) an independent variable3) a quantitative variable4) a dependent variable5) a constantQuestion 7 2 / 2 pointsA multiple regression analysis produced the following tables:Predictor Coefficients Standard Error tStatistic p-valueIntercept 616.6849154.55343.9901080.000947×1 -3.338332.333548-1.430580.170675×2 1.7800750.3356055.304075.83E-05
Source df SS MS F p-valueRegression 2 121783 60891.4814.761170.000286Residual 15 61876.684125.112 Total 17 183659.6 The regression equation for this analysis is:1) y = 616.6849 + 3.33833 x1 + 1.780075 x22) y = 154.5535 – 1.43058 x1 + 5.30407 x23) y = 616.6849 – 3.33833 x1 – 1.780075 x24) y = 154.5535 + 2.333548 x1 + 0.335605 x25) y = 616.6849 – 3.33833 x1 + 1.780075 x2Question 8 2 / 2 pointsA multiple regression analysis produced the following tables:Predictor Coefficients Standard Error tStatistic p-valueIntercept 752.0833336.31582.2362410.042132×1 11.873755.320472.0317110.082493×2 1.9081830.6627422.8792260.01213
Source df SS MS F p-valueRegression 2 203693.3101846.76.7454060.010884Residual 12 181184.115098.67 Total 14 384877.4 These results indicate that:1) none of the predictor variables are significant at the 5% level2) each predictor variable is significant at the 5% level3) x1 is the only predictor variable significant at the 5% level4) x2 is the only predictor variable significant at the 5% level5) the intercept is not significant at the 5% levelQuestion 9 2 / 2 pointsA 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 area2) number of bedrooms3) market value4) central heating5) residential housesQuestion 10 2 / 2 pointsIn regression analysis outliers may be identified by examining the:1) coefficient of determination2) coefficient of correlation3) p-values for the partial coefficients4) residuals5) R-squared value