Part 1 : 07/27/10 21:30:31

Question 1 - CIA 593 III-64 - Forecasting Techniques What coefficient of correlation results from the following data? X Y 1 10 2 8 3 6 4 4 5 2 A. 0 B. 1 C. Cannot be determined from the data given. D. -1 Part 1 : 07/27/10 21:30:31 A. The data represents a negative correlation. As X is increasing, Y is decreasing. B. A perfectly inverse relationship exists, not a direct relationship. C. A linear relationship between X and Y can be determined. D. This data represents a perfect negative correlation. As X is increasing by 1, Y is decreasing by 2. Thus, this is an inverse relationship, and r must be equal to -1. Question 2 - IMA 08 P1-152 - Forecasting Techniques Sales of big-screen televisions have grown steadily during the past five years. A dealer predicted that the demand for February would be 148 televisions. Actual demand in February was 158 televisiions. If the dealer is using exponential smoothing to forecast demand and the smoothing constant is alpha = 0.3, the demand forecast for March, using the exponential smoothing model, will be A. 153 televisions. B. 158 televisions. C. 151 televisions. D. 148 televisions. A. This answer is incorrect. The formula for calculating a forecasted amount using exponential smoothing is: Forecasted Value = (alpha current actual value) + ([1 alpha] current forecasted value) B. This is the actual demand in February, not the forecasted demand for March. The formula for calculating a forecasted amount using exponential smoothing is: Forecasted Value = (alpha current actual value) + ([1 alpha] current forecasted value) C. The formula for calculating a forecasted amount using exponential smoothing is: Forecasted Value = (alpha current actual value) + ([1 alpha] current forecasted value) Using the forecasted and actual amounts given for the month of February, we can calculate the forecasted value for the month of March as follows: Forecasted Value = (0.3 158) + (0.7 148) (c) HOCK international, page 1

Forecasted Value = 151 D. This is the demand forecast for February. It is not the demand forecast for March. The formula for calculating a forecasted amount using exponential smoothing is: Forecasted Value = (alpha current actual value) + ([1 alpha] current forecasted value) Question 3 - CMA 1291 H1 - Forecasting Techniques A distinction between forecasting and planning A. Arises because forecasting covers the short-term and planning does not. B. Is not valid because they are synonyms. C. Is that forecasts are used in planning. D. Is that forecasting is a management activity whereas planning is a technical activity. A. Both planning and forecasting can be used in either a short-term or long-term time frame. B. Though these terms may often be used or thought of as synonyms, they are not. Planning is the process of determining how to achieve the company's goals and objectives, or the questions of what, how, when, where and who for the company's operations. This plan is then communicated to all of the company to serve as a guide for future actions. A forecast is an attempt to determine the future activity levels or environment that the company will be operating in. C. Planning is the process of determining how to achieve the company's goals and objectives, or the questions of what, how, when, where and who for the company's operations. This plan is then communicated to all of the company to serve as a guide for future actions. A forecast is an attempt to determine the future activity levels or environment that the company will be operating in. Forecasts are used in the planning process as a basis for some of the decisions that need to be made. D. Forecasting is more of technical activity than planning because of the mathematical models that may be used in the forecasting process. In any case, planning is certainly a management activity. Question 4 - IMA 08 P1-140 - Forecasting Techniques The results of regressing Y against X are as follows. Coefficient Intercept 5.23 Slope 1.54 When the value of X is 10, the estimated value of Y is A. 53.84 B. 20.63 C. 8.05 D. 6.77 A. This answer results from reversing the constant coefficient and the variable coefficient in the regression model. This is a regression model of the form y = ax + b, and to answer it properly, it is necessary to properly identify the constant coefficient and the variable coefficient and use them, along with the value of x, to solve the equation. B. In this simple regression model, the coefficient means the constant coefficient, or the y intercept, which is the value of y when x is equal to zero. The slope is the variable coefficient, which represents the amount of (c) HOCK international, page 2

increase in y for each unit of increase in x; and the variable coefficient is always next to x in the equation. Therefore, the regression equation to be solved is y = ax + b Where: a = 1.54 x = 10 b = 5.23 Plugging the numbers into the equation, we get: y = (1.54 10) + 5.23 y = 20.63 Part 1 : 07/27/10 21:30:31 C. This answer results from multiplying the intercept by the slope. This is a regression model of the form y = ax + b, and to answer it properly, it is necessary to identify the constant coefficient and the variable coefficient and use them, along with the value of x, to solve the equation. D. This answer results from adding the intercept and the slope together. This is a regression model of the form y = ax + b, and to answer it properly, it is necessary to identify the constant coefficient and the variable coefficient and use them, along with the value of x, to solve the equation. Question 5 - CMA 1291 4-26 - Forecasting Techniques Automite Company is an automobile replacement parts dealer in a large metropolitan community. Automite is preparing its sales forecast for the coming year. Data regarding both Automite's and industry sales of replacement parts as well as both the used and new automobile sales in the community for the last 10 years have been accumulated. If Automite wants to determine if there is a historical trend in the growth of its sales as well as the growth of industry sales of replacement parts, the company would employ A. Simulation techniques. B. Queuing theory. C. Statistical sampling. D. Time series analysis. A. Simulation is a model that uses mathematical expressions and logical relationships to compute the value of the outputs. It is designed to model a real situation or system using controllable or probabilistic inputs. B. Queuing theory is the process of determining the most efficient and effective way to move people or goods through a line in the most economical manner, keeping waiting times to a minimum. C. Statistical sampling is used to calculate the condition of a population based on a sample from that population. D. Time series analysis is used to measure the changes in the quantity of something over a period of time. Time series analysis would be an appropriate technique to use as it involves past experience. Question 6 - CIA 1187 IV-10 - Forecasting Techniques Management has prepared a graph showing the total costs of operating branch warehouses throughout the country. The cost line crosses the vertical axis at $200,000. The total cost of operating one branch is $350,000. The total cost of operating ten branches is $1,700,000. For purposes of preparing a flexible budget based on the number of branch warehouses in operation, what formula should be used to determine budgeted costs at various levels of activity? A. Y = $200,000 + $150,000X B. Y = $350,000 + $150,000X (c) HOCK international, page 3

C. Y = $200,000 + $170,000X D. Y = $350,000 + $200,000X A. In the formula, the constant amount represents the fixed costs. In this situation, since the total cost line intersects the vertical axis at $200,000, that is the fixed cost of operations. This leaves the company with $1,500,000 of variable costs when operating 10 branches. This gives a per branch variable cost of $150,000. Therefore, the number of branches needs to be multiplied by $150,000 and added to the fixed cost to calculate the total cost. B. The calculation of the fixed costs in this answer is incorrect. See the correct answer for a complete explanation. C. The calculation of the variable costs in this answer is incorrect. See the correct answer for a complete explanation. D. The calculation of fixed and variable costs is incorrect in this answer. See the correct answer for a complete explanation. Question 7 - IMA 08 P1-136 - Forecasting Techniques For cost estimation simple regression differs from multiple regression in that simple regression uses only A. one dependent variable, while multiple regression uses more than one dependent variable. B. dependent variables, while multiple regression can use both dependent and independent variables. C. one independent variable, while multiple regression uses more than one independent variable. D. one dependent variable, while multiple regression uses all available data to estimate the cost function. A. Both simple regression analysis and multiple regression analysis are used to estimate or forecast one dependent variable. B. Both simple regression analysis and multiple regression analysis use both dependent and independent variables. C. Simple regression analysis uses one independent variable to estimate or forecast the dependent variable, whereas multiple regression analysis uses more than one independent variable to estimate or forecast the dependent variable. D. Both simple regression analysis and multiple regression analysis are used to estimate or forecast one dependent variable. Question 8 - CMA 1291 4-27 - Forecasting Techniques Automite Company is an automobile replacement parts dealer in a large metropolitan community. Automite is preparing its sales forecast for the coming year. Data regarding both Automite's and industry sales of replacement parts as well as both the used and new automobile sales in the community for the last 10 years have been accumulated. If Automite wants to determine if its sales of replacement parts are patterned after the industry sales of replacement parts or to the sales of used and new automobiles, the company would employ A. Simulation techniques. B. Correlation and regression analysis. C. Statistical sampling. D. Time series analysis. A. Simulation is a model uses mathematical expressions and logical relationships to compute the value of the outputs. It is designed to model a real situation or system using controllable or probabilistic inputs. B. The correlation is a numerical measure that measures both the direction (positive or negative) and the strength of the linear association between the dependent and independent variables. (c) HOCK international, page 4

C. Statistical sampling is used to calculate the condition of a population based on a sample from that population. D. Time series analysis is used to measure the changes in the quantity of something over a period of time. Question 9 - CIA 1194 II-46 - Forecasting Techniques In regression analysis, which of the following correlation coefficients represents the strongest relationship between the independent and dependent variables? A. -.02 B..75 C. -.89 D. 1.03 A. The correlation coefficient representing the strongest relationship between the independent and dependent variable is the one that is closest to either +1 or 1. This is the weakest correlation among the answer choices. B. This is not the strongest correlation. The strongest relationship between the independent and dependent variables is represented by a correlation coefficient that is closest to either +1 or 1. See the correct answer for a complete explanation. C. The coefficient of correlation is a numerical measure that measures both the direction (positive or negative) and the strength of the linear association between the dependent and independent variables. The coefficient of correlation lies between -1.0 and +1.0. When the correlation coefficient is positive (between 0 and +1), it means the dependent and independent variables move in the same direction. When the correlation coefficient is negative (between 0 and 1), it means they move in opposite directions, i.e., when the independent variable goes up, the dependent variable goes down. When the coefficient of correlation is 0, it means either that there is no correlation between the two variables, or that the relationship between them is not linear. To identify the strongest correlation we need to determine the coefficient of correlation that is closest to either +1 or 1. In this case it is.89. D. The coefficient of correlation lies between -1.0 and +1.0. Therefore, the coefficient of correlation could not be 1.03. Question 10 - IMA 08 P1-139 - Forecasting Techniques In order to analyze sales as a function of advertising expenses, the sales manager of Smith Company developed a simple regression model. The model included the following equation, which was based on 32 monthly observations of sales and advertising expenses with a related coefficient of determination of.90. S = $10,000 + $2.50A S = Sales A = Advertising expenses If Smith Company's advertising expenses in one month amounted to $1,000, the related point estimate of sales would be A. $11,250. B. $2,500. C. $12,250. D. $12,500. A. This is ($2.50 100) plus advertising expenses plus $10,000; but that is not the regression model for estimating sales. (c) HOCK international, page 5

B. This is $2.50 multiplied by the advertising expense; but that is not the regression model for estimating sales. C. This is ($2.50 100) plus (2 advertising expenses) plus $10,000; but that is not the regression model for estimating sales. D. Using the model given, we can simply plug the amount given for advertising expense into it to determine the sales estimate, as follows: S = $10,000 + $2.50A S = $10,000 + ($2.50 $1,000) S = $12,500 Part 1 : 07/27/10 21:30:31 Question 11 - CMA 1285 5-27 - Forecasting Techniques The correlation coefficient that indicates the weakest linear association between two variables is A. -0.11 B. 0.35 C. 0.12 D. -0.73 A. The coefficient of correlation is a numerical measure that measures both the direction (positive or negative) and the strength of the linear association between the dependent and independent variables. The coefficient of correlation lies between -1.0 and +1.0. When the correlation coefficient is positive (between 0 and +1), it means the dependent and independent variables move in the same direction. When the correlation coefficient is negative (between 0 and 1), it means they move in opposite directions, i.e., when the independent variable goes up, the dependent variable goes down. When the coefficient of correlation is zero, it means either that there is no correlation between the two variables, or that the relationship between them is not linear. To identify the weakest correlation we need to determine the coefficient of correlation that is closest to 0. In this case it is.11. B. The weakest correlation coefficient is the one that is closest to zero. This answer is incorrect. See the correct answer for a complete explanation. C. The weakest correlation coefficient is the one that is closest to zero. This answer is incorrect. See the correct answer for a complete explanation. D. The weakest correlation coefficient is the one that is closest to zero. This answer is incorrect. See the correct answer for a complete explanation. Question 12 - CMA 1289 5-14 - Forecasting Techniques Correlation is a term frequently used in conjunction with regression analysis, and is measured by the value of the coefficient of correlation, r. The best explanation of the value r is that it A. Is a measure of the relative relationship between two variables. B. Interprets variances in terms of the independent variable. C. Ranges in size from negative infinity to positive infinity. D. Is always positive. (c) HOCK international, page 6

A. The coefficient of correlation is a numerical measure that measures both the direction (positive or negative) and the strength of the linear association between the dependent and independent variables. B. The coefficient of correlation relates two variables to each other. It does not interpret variances. C. The value of coefficient of correlation lies between +1.0 and 1.0. D. The coefficient of correlation can be either positive and negative. Question 13 - CMA 1290 4-27 - Forecasting Techniques In the standard regression equation y = a + bx, the letter b is best described as a(n) A. Dependent variable. B. Independent variable. C. Variable coefficient. D. Constant coefficient. A. The dependent variable is represented by y in the equation given. B. The independent variable is represented by x in the equation given. C. In the standard regression as represented here, the b in the equation represents the variable coefficient. It represents the amount of increase in y for each unit of increase in x, or the slope of the line. D. The constant coefficient is represented by a in the equation given. It represents the y intercept, because this is the value of y when x = 0. Question 14 - IMA 08 P1-138 - Forecasting Techniques Dawson Manufacturing developed the following multiple regression equation, utilizing many years of data, and uses it to model, or estimate, the cost of its product. Cost = FC + (a L) + (b M) Where: FC = fixed costs L = Labor rate per hour M = Material cost per pound Which one of the following changes would have the greatest impact on invalidating the results of this model? A. A significant change in labor productivity. B. Renegotiation of the union contract calling for much higher wage rates. C. A large drop in material costs as a result of purchasing the material from a foreign source. D. A significant reduction in factory overheads, which are a component of fixed costs. A. A significant change in labor productivity would cause the cost of the product to increase because more labor hours would be required to produce the product, but this increase would not be reflected in the cost of the product that would be calculated by the model. This would have the greatest impact on invalidating the results of the model. B. Renegotiation of the union contract calling for much higher wage rates would increase the L in the model, which would increase the cost of the product. This would be consistent with the model and would not serve to invalidate its results. C. A large drop in material costs as a result of purchasing material from a foreign source would reduce M in the (c) HOCK international, page 7

model, which would decrease the cost of the product. This would be consistent with the model and would not serve to invalidate its results. D. A significant reduction in factory overheads would reduce FC in the model, which would decrease the cost of the product. This would be consistent with the model and would not serve to invalidate its results. Question 15 - IMA 08 P1-137 - Forecasting Techniques A company has accumulated data for the last 24 months in order to determine if there is an independent variable that could be used to estimate shipping costs. Three possible independent variables being considered are packages shipped, miles shipped, and pounds shipped. The quantitative technique that should be used to determine whether any of these independent variables might provide a good estimate for shipping costs is A. variable costing. B. linear regression. C. linear programming. D. flexible budgeting. A. Variable costing is not used to estimate or forecast a dependent variable using historical data on an independent variable. B. Historical data on each of the independent variables being considered packages shipped, miles shipped, and pounds shipped should be regressed separately in a simple regression against the historical data on shipping costs, the dependent variable. From each separate regression, the resulting coefficient of correlation should be evaluated to determine whether there is a strong correlation between the independent variable and historical shipping costs. The coefficient of correlation is a numerical measure between +1 and 1 that results from the regression analysis and indicates the strength of the relationship between the independent and dependent variables. A coefficient of correlation that is close to +1 indicates a strong positive linear relationship between the independent and dependent variable, while a coefficient of correlation that is close to 1 indicates a strong inverse, or negative, linear relationship between the independent and dependent variable. C. Linear programming is not used to estimate or forecast a dependent variable using historical data on an independent variable. D. Flexible budgeting is not used to estimate or forecast a dependent variable using historical data on an independent variable. Question 16 - CMA 1290 4-28 - Forecasting Techniques The letter x in the standard regression equation is best described as a(n) A. Independent variable. B. Coefficient of determination. C. Constant coefficient. D. Dependent variable. A. In the standard regression equation x represents the independent varialbe. B. The coefficient of determination is not a part of the standard regression equation at all. The coefficient of determination is the percentage of the total amount of change in the dependent variable that can be explained by changes in the independent variable. C. The constant coefficient is represented by a letter that stands by itself, i.e., a letter without an x term next to it. It represents the y intercept, because this is the value of y when x = 0. (c) HOCK international, page 8

D. In the standard regression equation, y represents the dependent variable. (c) HOCK international, page 9