# Mathematics question help

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

(Points : 3)

strong negative correlation

virtually no correlation

strong positive correlation

moderate negative correlation

weak negative correlation

Question 2. 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 independent variable is ______. (Points : 3)

batch size

unit variable cost

fixed cost

total cost

total variable cost

Question 3. 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 ______. (Points : 3)

batch size

unit variable cost

fixed cost

total cost

total variable cost

Question 4. 4. If x and y in a regression model are totally unrelated, _______. (Points : 3)

the correlation coefficient would be -1

the coefficient of determination would be 0

the coefficient of determination would be 1

the SSE would be 0

the MSE would be 0sQuestion 5. 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 10,000 miles, the predicted cost is ____________. (Points : 3)

2090

3850

7400

6950

5150Question 6. 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 ______. (Points : 3)

a response variable

an independent variable

a quantitative variable

a dependent variable

a constant

Question 7. 7. A multiple regression analysis produced the following tables: Predictor

Coefficients

Standard Error

t Statistic

p-valueIntercept

The regression equation for this analysis is ____________. (Points : 3)

7. A multiple regression analysis produced the following tables:

Predictor | Coefficients | Standard Error | t Statistic | 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 |

These results indicate that ____________. (Points : 3)

none of the predictor variables are significant at the 5% level

each predictor variable is significant at the 5% level

x1 is the only predictor variable significant at the 5% level

x2 is the only predictor variable significant at the 5% level

the intercept is not significant at the 5% level

Question 9. 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 _______. (Points : 3)

heated area

number of bedrooms

market value

central heating

residential houses

Question 10. 10. In regression analysis, outliers may be identified by examining the ________. (Points : 3)

coefficient of determination

coefficient of correlation

p-values for the partial coefficients

residuals

R-squared value

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