Multiple Regression Analysis

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1. If the correlation coefficient between the variables is 0, it means that the two variables aren’t related. – TRUE 2. In a simple regression analysis the error terms are assumed to be independent and normally distributed with zero mean and constant variance. – TRUE 3. The difference between the actual Y-value and the predicted Y-value found using a regression equation is called the residual (ε) – TRUE 4. In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N-k). – FALSE (correct answer N-k-1) 5. From the following scatter plot, we can say that between y & x there is _______. – Negative correlation

6. According to the graph, X & Y have ________. – Virtually no correlation

7. 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 explanatory variable is called the _______. – Coefficient of determination 8. In the regression equation, y = 75.65 + 0.50x, the intercept is ______. – 75.65 9. The assumptions underlying simple regression analysis include ______. – The error terms are independent 10. The proportion of variability of the dependent variable accounted for or explained by the independent variable is called the _______. – coefficient of determination 11. 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 = 1550 + 0.36x -- If a car is driven 30,000 miles, the predicted cost is _______. – 12,350 12. A cost accountant is developing a regression model to predict the total cost as a linear function of batch size (the number of boards produced in ne lot or batch) and production shift (day and evening). The dependent variable in this model is ________. –total cost 13. The...
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