Business Analytics Final
True or False: In a simple linear regression model y=b0+b1x+e b1 represents the slope of the true regression line.
True the parameter B1 represents the slope of the true regression line.
In a simple linear regression model, y=b0 + b1x + E the parameter b1 represents the a. intercept b. slope of the true regression line c. mean value of x. d. error term.
B
A one-way data table summarizes: A. a single input's impact on the output of interest. B. Multiple inputs' impact on a single output of interest. C. values of the input cells that will cause the single output value to equal zero. D. Values of the cells when not all of the model is observable on the screen.
A
In linear programming models of real problems, the occurrence of an unbounded solution means that the A. problem formulation is improper B. mathematical models sufficiently represent the real-world problems. C. constraints have been excessively used in modeling. D. resultant values of the decision variables have no bounds.
A
Suppose for a particular week, the forecasted sales were $4,000. The actual sales were $3,000. What is the value of the mean absolute percentage error? A. 33.3% B. 25% C. -33.3% D. -25%
A
The _________________ value for each less-than-or-equal-to constraint indicates the difference between the left-hand and right-hand values for a constraint. A. slack b. unbounded c. objective function coefficient d. surplus
A
The objective function for a linear optimization problem is: Max 3x + 5y, with constraints x>=0, y>= 0 and x and y are both integers and they are also the only decision variables. This is an example of an A. all-integer linear program B. mixed- integer linear program C. nonlinear program D. binary integer linear program
A
The population parameters that describe the y-intercept and slope of the line relating y and x, respectively, are: A. B0 and B1 B. Y and X C. A and B. D. A AND B.
A
In the simple linear regression model, the ______________ accounts for the variability in the dependent variable that cannot be explained by the linear relationship between the variables. a. constant term b. error term c. model parameter d. residual
B
A(n) _______________ solution satisfies all the constraint expressions simultaneously. A. objective B. extreme c. feasible d. infeasible
C
If the forecasted value of the time series variable for period 2 is 22.5 and the actual value observed for period 2 is 25. What is the forecast error in period 2? A. 3 B. -2.5 C. 2.5 D. 2
C
Navigation in a spreadsheet model can be facilitated by: A. using different spreadsheets for each formula in the model. B. Using long calculations in the cells. C. Using clear labels and proper formatting and alignment D. referencing data by using hyperlinks to the problem statement.
C
The difference between the observed value of the dependent variable and the value predicted using the estimated regression equation is known as the: a. constant term b. error term c. residual d. model parameter
C
The objective function for an optimization problem is: Min 3x-2y, with constraints x>= 0, y>=0 x and y must be integers Suppose that the integer restriction on the variables is removed. If so, this would be familiar two-variable linear program, however it would also be an example of A. the convex hull of the linear program B. a mixed-integer linear program. C. an LP relaxation of the integer linear program. D. A binary integer linear program.
C
Which of the following is true regarding forecast error? A. cannot be negative B. takes a positive value when the forecast is too high C. is associated with measuring forecast accuracy D. Cannot be zero
C
Causal Models: A. use the average of the most recent data values in the time series as the forecast for the next period b. provide evidence of a causal relationship between an independent variable and the variable to be forecast c. occur whenever all the independent variables are previous values of the same time series d. relate a time series to other variable that are believed to explain or cause its behavior.
D
____________ is a constraint requiring that the sum of two or more binary variables be less than or equal to one. Thus, if one of the variables equals one, the others must be equal to zero. however all variables could equal zero A. Conditional Constraint. B. Corequisite Constraint C. K out of n alternatives constraint D. Mutually exclusive constraint
D
The mean absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast. These methods measure forecast accuracy by:
Determining how well a particular forecasting method is able to reproduce the time series data that are already available.
True or False: The population parameters that describe the y-intercept and slop of the line relating y and x, respectively are
False They are B0 and B1
What are MSE?
Mean Squared Error
What is MSE?
Mean Squared Error is the average of the square of the forecasts errors.
What are MAE?
Mean absolute Error.
What are MAPE?
Mean absolute percentage error.
What is MAE?
Mean of Absolute error is a measure of forecast accuracy that avoids the problem of positive and negative forecast errors of offsetting each other. Is the average of the absolute values of the forecast errors .
What is MAPE?
The average of the absolute value of percentage forecast error.
A variable used to model the effect of categorical independent variables in a regression model is known as a dummy variable
True
Forecast error is associated with measuring forecast accuracy
True
The mean of absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast. These methods measure forecast accuracy by determining how well a particular forecasting method is able to reproduce the time series data that are already available.
True
True or False A one-way data table summarizes a single input's impact on the output of interest.
True
True or False Causal models relate a time series to other variables that are believed to explain or cause its behavior.
True
True or False the slack value for each less-than-or-equal-to-constraint indicates the difference between the left-hand and right-hand values for a constraint.
True
True or False: An feasible solution satisfies all the constraint expressions simultaneously
True
True or False: In linear programming models of real problems, the occurrence of an unbounded solution means that the problem formulation is improper.
True
True or False: In the simple linear regression model, the error term accounts for the variability in the dependent variable that cannot be explained by the linear relationship between the variables.
True
True or False: The difference between the observed value of the dependent variable and the value predicted using the estimated regression equation is known as the residual.
True
True or False: The sum of two or more binary variables must be less than or equal to one in a mutually exclusive constraint.
True
True or false Navigation in a spreadsheet model can be facilitated by using clear labels and proper formatting and alignment
True
True or False: The objective function for an optimization problem is: Min 3x-2y, with constraints x>=0, Y>=0. x and y must be integers. Suppose that the integer restriction on the variables is removed. If so, this would be a familiar two-variable linear program; however, it would also be an example of an LP relaxation of the integer linear program.
True. LP relaxation is a linear program after dropping integer requirements of variables in an integer linear program.
A variable used to model the effect of categorical independent variables in a regression model is known as a a. dependent variable b. response c. dummy variable d. predictor
c
