6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 1/69 Question 1 1.B.3.f tb.lc.anal.015_1805 LOS: 1.B.3.f Lesson Reference: Expected Value Computations and Learning Curve Analysis Difficulty: medium Bloom Code: 2 Which of the following is not a limitation of learning curve analysis? Learning curve analysis assumes that the percentage improvement from learning only fully occurs when production doubles. Learning curve analysis cannot be used when it is difficult to accurately measure the impact of efficiency improvements. Rationale  Learning curve analysis assumes that the percentage improvement from learning only fully occurs when production doubles. Learning curve analysis assumes that the time and cost to perform an activity will decrease by a fixed percentage when production doubles. It does not allow for situations where the decrease occurs at intervals other than doubling. This lack of flexibility is a limitation of learning curve analysis; therefore, this is an incorrect answer. Rationale  Learning curve analysis cannot be used when it is difficult to accurately measure the impact of efficiency improvements. It can be difficult to measure the impact of the learning in a learning curve analysis. Since that is needed to analyze the expected decrease in time and cost, the fact that it is difficult to estimate means it is a limitation of learning curve analysis; therefore, this is an incorrect answer. Rationale  Learning curve analysis assumes all improvements in production efficiency are caused by employee learning. Learning curve analysis assumes that all improvements in efficiency are caused by employee learning. A different labor mix, improved machinery, and better-quality materials could also be the cause of improved efficiency. Because learning curve analysis ignores these factors, it is a limitation of learning curve analysis; therefore, this is an incorrect answer. Rationale  Learning curve analysis can only be used to predict performance that is within the range of data used to develop the analysis. Learning curve analysis is a way to estimate the time and cost to perform an activity under the assumption that people will learn to perform the activity more efficiently the more times they perform the task. Linear regression is a statistical technique where past data is used to develop an equation that can be used to predict something of interest. The data used to develop the regression equation define the “relevant range of activity” over which the equation is valid. Using the equation to predict costs for activity within the relevant range is valid; however, using the equation to predict costs for activity outside the relevant range is not valid. The need to stay within the relevant range limits the usefulness of regression analysis, which means it is a limitation of regression analysis, not learning curve analysis; therefore, this is the correct answer. Learning curve analysis can only be used to predict performance that is within the range of data used to develop the analysis. Correct Learning curve analysis assumes all improvements in production efficiency are caused by employee learning. Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 2/69 Question 2 1.B.3.b tb.reg.anal.008_1805 LOS: 1.B.3.b Lesson Reference: Regression Analysis Difficulty: medium Bloom Code: 4 How does a multiple linear regression equation differ from a simple linear regression equation? More than one dependent variable is predicted by a multiple linear regression equation but only one dependent variable is predicted in a simple linear regression equation. A multiple linear regression is likely to be less accurate than a simple linear regression model. A multiple linear regression is likely to be less difficult to interpret than a simple linear regression model. Rationale  More than one independent variable is used to predict a dependent variable in a multiple linear regression equation but only one independent variable is used to predict a dependent variable in a simple linear regression equation. Linear regression is a statistical technique where past data is used to develop an equation that can be used to predict something of interest. The factor being predicted is the dependent variable and the factor or factors used to predict the dependent variable are the independent variables. In multiple linear regression two or more independent variables are used to predict the dependent variable while only one independent variable is used to predict the dependent variable in simple linear regression. Therefore, this is the correct answer. Rationale  More than one dependent variable is predicted by a multiple linear regression equation but only one dependent variable is predicted in a simple linear regression equation. There is only one dependent variable in both types of regression; therefore, this is an incorrect answer. Rationale  A multiple linear regression is likely to be less accurate than a simple linear regression model. In multiple linear regression two or more independent variables are used to predict the dependent variable while only one independent variable is used to predict the dependent variable in simple linear regression. Because multiple linear regression uses more than one independent variable to predict a dependent variable, it is likely to be more accurate, not less accurate, than a simple linear regression model that uses only one independent variable to predict a dependent variable. Therefore, this is an incorrect answer. Rationale  A multiple linear regression is likely to be less difficult to interpret than a simple linear regression model. In multiple linear regression two or more independent variables are used to predict the dependent variable while only one independent variable is used to predict the dependent variable in simple linear regression. Because multiple linear regression uses more than one independent variable to predict a dependent variable, it is likely to be more difficult, not less difficult, to interpret than a simple linear regression model because the relationship between the multiple independent variables must be taken into consideration (multi-collinearity). Therefore, this is an incorrect answer. More than one independent variable is used to predict a dependent variable in a multiple linear regression equation but only one independent variable is used to predict a dependent variable in a simple linear regression equation. Correct

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 4/69 Question 4 1.B.3.c aq.reg.anal.006_0720 LOS: 1.B.3.c Lesson Reference: Regression Analysis Difficulty: hard Bloom Code: 5 Eight quarters of production data from Pear, Inc., a cell phone manufacturing company, are presented below. Pear, Inc. Quarter Phones Cost 1 2,331 $3,245,874 2 2,657 $3,474,318 3 1,987 $2,883,675 4 2,412 $3,287,621 5 2,583 $3,354,966 6 2,497 $3,428,752 7 2,285 $3,152,347 8 2,645 $3,271,899 The regression analysis results on these data are displayed below. Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept $1,473,119 $356,978 4.13 0.01 $599,625 $2,346,614 Phones $738 $147 5.03 0.00 $379 $1,097 Regression Statistics Multiple R 0.90 R Square 0.81 Adjusted R Square 0.78 Standard Error $87,127 Observations 8 Based on the regression analysis result above, and with approximately 68% confidence, predict the total cost to produce 2,500 phones next quarter. $3,318,119 Between $3,143,865 and $3,492,373 Rationale  $3,318,119 This answer calculates the estimated total cost using the regression equation (total cost equation): Total costs = ($738 × 2,500 phones) + $1,473,119 = $3,318,119. However, this answer does not provide a 68% confidence interval around that estimate. Rationale  Between $2,960,994 and $3,675,244 This answer calculates the estimated total cost using the regression equation (total cost equation) and then uses the standard error for both total fixed costs of $356,978 and variable cost per phone of $147 to develop a 68% confidence interval. However, the standard error for the total cost estimate is $87,127, and this amount should be used to calculate a 68% confidence interval. Rationale  Between $3,230,992 and $3,405,246 This answer calculates the estimated total cost using the regression equation (total cost equation): Total costs = ($738 × 2,500 phones) + $1,473,119 = $3,318,119. Then it calculates the 68% confidence interval, which is one standard error: $3,318,119 ± $87,127 = between $3,230,992 and Between $3,230,992 and $3,405,246 Correct Between $2,960,994 and $3,675,244 Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 5/69 $3,405,246. Rationale  Between $3,143,865 and $3,492,373 This answer calculates the estimated total cost using the regression equation (total cost equation): Total costs = ($738 × 2,500 phones) + $1,473,119 = $3,318,119. But it then calculates a 95% confidence interval instead of a 68% confidence interval by using two standard errors.

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 7/69 Question 6 1.B.3.b aq.reg.anal.009_0720 LOS: 1.B.3.b Lesson Reference: Regression Analysis Difficulty: medium Bloom Code: 3 Eight quarters of production data from Pear, Inc., a cell phone manufacturing company, are presented below. Pear, Inc. Quarter Phones Shut Downs Cost 1 2,331 2 $3,245,874 2 2,657 1 $3,474,318 3 1,987 3 $2,883,675 4 2,412 2 $3,287,621 5 2,583 1 $3,354,966 6 2,497 3 $3,428,752 7 2,285 2 $3,152,347 8 2,645 0 $3,271,899 The regression analysis results on these data are displayed below. Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept $466,096 $309,413 1.51 0.19 − $329,275 $1,261,467 Phones $1,080 $114 9.50 0.00 $788 $1,373 Shut Downs $100,963 $24,675 4.09 0.01 $37,534 $164,391 Regression Statistics Multiple R 0.98 R Square 0.96 Adjusted R Square 0.94 Standard Error $45,769 Observations 8 What does the Multiple R statistic represent in this analysis? The Multiple R statistic of 0.98 indicates how much we understand about total costs in the dataset based on the volume of phone production and number of shutdowns. The 0.98 statistic means that variance (change) in the phone production and number of shutdowns explains 98% of the variance (change) in total costs. The Multiple R statistic of 0.98 indicates how much we understand about total costs in the dataset based on the volume of phone production and number of shutdowns. The 0.98 statistic means that variance (change) in the phone production and number of shutdowns explains 2% of the variance (change) in total costs. Rationale  The Multiple R statistic of 0.98 indicates how much we understand about total costs in the dataset based on the volume of phone production and number of shutdowns. The 0.98 statistic means that variance (change) in the phone production and number of shutdowns explains 98% of the variance (change) in total costs. This is an explanation of the R Square or Adjusted R Square, not the Multiple R. Rationale  The Multiple R statistic of 0.98 indicates how much we understand about total costs in the dataset based on the volume of phone production and number of shutdowns. The 0.98 statistic means that variance (change) in the phone production and number of shutdowns The Multiple R statistic of 0.98 is the correlation of total costs, volume of phone production, and number of shutdowns. There is a 98% correlation between these numbers. Correct The Multiple R statistic of 0.98 is the correlation of total costs, volume of phone production, and number of shutdowns. There is a 2% correlation between these numbers. Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 8/69 explains 2% of the variance (change) in total costs. This explanation appears to be related to the R Square or Adjusted R Square, not the Multiple R. However, if this were an evaluation of R Square or Adjusted R Square, variance (change) in the phone production and number of shutdowns explains does not explain 2% of the variance (change) in total costs; rather, it explains 98% of the variance (change in total costs). Rationale  The Multiple R statistic of 0.98 is the correlation of total costs, volume of phone production, and number of shutdowns. There is a 2% correlation between these numbers. There is a 98% correlation between these numbers, not a 2% correlation. Rationale  The Multiple R statistic of 0.98 is the correlation of total costs, volume of phone production, and number of shutdowns. There is a 98% correlation between these numbers. This is an accurate definition of Multiple R.

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 10/69 Question 8 1.B.3.g tb.lc.anal.021_1805 LOS: 1.B.3.g Lesson Reference: Expected Value Computations and Learning Curve Analysis Difficulty: medium Bloom Code: 4 The Madeline Company prepares the following distribution of sales forecasts for various economic conditions and the probability of those conditions. What is the expected value of sales for Madeline? Economic Condition Sales Forecast Probability Robust Growth $20,000,000 15% Moderate Growth $14,000,000 65% Recession $5,000,000 20% $14,000,000 $5,000,000. Rationale  $13,000,000 The average of the three possible sales levels is $13,000,000. This assumes each condition is equally likely, which is not the case; therefore, this is an incorrect answer. Rationale  $13,100,000 To determine the expected sales, the sales under each possible economic condition is multiplied by the likelihood of each condition occurring. These products are then added together. In this example, the expected sales are ($20,000,000 × 15%) + ($14,000,000 × 65%) + ($5,000,000 × 20%) = $13,100,000. Therefore, this is the correct answer. Rationale  $14,000,000 The most likely of the three possible sales levels is $14,000,000. This does not take into consideration the likelihood of the economic conditions occurring or the possibility that the sales may be $20,000,000 or $5,000,000; therefore, this is an incorrect answer. Rationale  $5,000,000. The minimum level of sales is $5,000,000. This is different from the expected sales; therefore, this is an incorrect answer. $13,100,000 Correct $13,000,000 Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 11/69 Question 9 1.B.3.i tb.lc.anal.029_1805 LOS: 1.B.3.i Lesson Reference: Expected Value Computations and Learning Curve Analysis Difficulty: hard Bloom Code: 4 The Laney Company prepares the following distribution of cash flow forecasts for a possible investment under various economic conditions and the probability of those conditions. If the investment requires an initial investment of $150,000, should Laney make the investment? Economic Condition Cash Inflow Probability Robust Growth $700,000 25% Moderate Growth $300,000 55% Recession − $400,000 20% Yes, because the expected net cash flow from the investment is $50,000. No, because there is a chance that the net cash flow from the investment will be an outflow of $550,000. Rationale  Yes, because the expected net cash flow from the investment is $50,000. The average of the three possible cash inflow amounts is $200,000. If this is used to calculate the investment's net cash flow, it will be calculated as $50,000 ($200,000 − $150,000). The $200,000 figure assumes each condition is equally likely, which is not the case; therefore, this is an incorrect answer. Rationale  Yes, because the expected net cash flow from the investment is $110,000. To determine the expected cash inflow for the investment, the cash inflow under each possible economic condition is multiplied by the likelihood of each condition occurring. These products are then added together. In this example, the expected cash inflow is ($700,000 × 25%) + ($300,000 × 55%) + ( − $400,000 × 20%) = $260,000. Because the initial investment needed is $150,000, the expected cash inflow from the investment is $110,000 ($260,000 − $150,000). The fact that the expected net cash flow from the investment is positive means the investment should be made; therefore, this is the correct answer. Rationale  No, because there is a chance that the net cash flow from the investment will be an outflow of $550,000. The worst-case scenario for the investment's cash inflow is − $400,000. If this is used to calculate the investment's net cash flow, it will be calculated as an outflow of $550,000 ( − $400,000 − $150,000). All possible outcomes and the probabilities of those outcomes need to be considered when calculating the investment's expected cash inflow; therefore, this is an incorrect answer. Rationale  Yes, because the most likely net cash flow from the investment is $150,000. If the most likely cash inflow of $300,000 is used to calculate the investment's net cash flow, it will be calculated as $150,000 ($300,000 − $150,000). All possible outcomes and the probabilities of those outcomes need to be considered when calculating the investment's expected cash inflow; therefore, this is an incorrect answer. Yes, because the expected net cash flow from the investment is $110,000. Correct Yes, because the most likely net cash flow from the investment is $150,000. Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 13/69 Question 11 1.B.3.i tb.lc.anal.028_1805 LOS: 1.B.3.i Lesson Reference: Expected Value Computations and Learning Curve Analysis Difficulty: medium Bloom Code: 4 The Joseph Company prepares the following distribution of net cash flows for a possible investment under various economic conditions and the probability of those conditions. What is Joseph's expected net cash flow from this investment Economic Condition Net Cash Flow Probability Robust Growth $5,000,000 20% Moderate Growth $2,000,000 70% Recession − $1,300,000 10% $1,900,000 $5,000,000. Rationale  $2,270,000 To determine the expected net cash flow for the investment, the cash flow under each possible economic condition is multiplied by the likelihood of each condition occurring. These products are then added together. In this example, the expected net cash flow is ($5,000,000 × 20%) + ($2,000,000 × 70%) + ( − $1,300,000 × 10%) = $2,270,000. Therefore, this is the correct answer. Rationale  $1,900,000 The average possible cash flow is $1,900,000. This assumes each economic condition is equally likely, which is not the case; therefore, this is an incorrect answer. Rationale  $2,000,000 The most likely possible cash flow is $2,000,000. This does not take into consideration the likelihood of the economic conditions occurring or the possibility that the cash flow may be $5,000,000 or − $1,300,000; therefore, this is an incorrect answer. Rationale  $5,000,000. The maximum level of possible cash flow is $5,000,000. This is different from the expected cash flow from the investment; therefore, this is an incorrect answer. $2,270,000 Correct $2,000,000 Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 14/69 Question 12 1.B.3.c tb.reg.anal.002_1805 LOS: 1.B.3.c Lesson Reference: Regression Analysis Difficulty: medium Bloom Code: 3 Jamie's Jams conducted a regression analysis on its shipping costs for the last year, which resulted in the following equation: $3.25x + $115. If Jamie plans to ship 287 pints of jam next month, what are the shipping costs expected to be? $932.75 $118.25 Rationale  $1,047.75 Regression analysis uses past data to develop an equation that can be used to make predictions about the future. Simple regression involves using one independent variable (for example, sales, production, or some other measure of volume) to predict future costs. Regression analysis produces an “intercept” and a “slope coefficient.” The “intercept” is the estimate of fixed costs and the “slope coefficient” is the estimate of variable cost per unit of volume. Based on the regression analysis performed, Jamie's fixed costs to ship 287 pints are estimated as $115 and variable costs to ship 287 pints are estimated as $932.75 ($3.25 × 287). This results in a total estimated cost to ship 287 pints of $1,047.75 ($932.75 + $115); therefore, this is the correct answer. Rationale  $932.75 Based on the regression analysis performed, Jamie's variable costs to ship 287 pints are estimated as $932.75 ($3.25 × 287). The $932.75 does not take fixed costs into consideration; therefore, this is an incorrect answer. Rationale  $817.75 Based on the regression analysis performed, Jamie's fixed costs to ship 287 pints are estimated as $115 and variable costs to ship 287 pints are estimated as $932.75 ($3.25 × 287). Shipping costs to ship 287 pints would be estimated as $817.75 if the estimated fixed costs are subtracted from the estimated variable costs ($932.75 − $115.00). This is not the correct equation; therefore, this is an incorrect answer. Rationale  $118.25 Based on the regression analysis performed, Jamie's fixed costs to ship 287 pints are estimated as $115. For the total cost to ship 287 pints to be $118.25 the total variable cost would have to be $3.25; however, that is the variable cost to ship one pint, not 287 pints. Therefore, this is an incorrect answer. $1,047.75 Correct $817.75 Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 16/69 Rationale  Total costs = $114(Phones) + $24,675(Shut Downs) + $309,413 This answer mistakes the standard error for the coefficients. The coefficients are used to create the regression equation (total cost equation).

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 17/69 Question 14 1.B.3.e tb.lc.anal.008_1805 LOS: 1.B.3.e Lesson Reference: Expected Value Computations and Learning Curve Analysis Difficulty: hard Bloom Code: 4 A company implements a new process to manufacture its product and uses 100 hours to complete one unit. It expects that the new process will be subject to an 80% learning curve. If the company assumes the learning curve will follow the cumulative average-time learning model, how many total hours will be needed to produce the third and fourth batches? 256 hours 160 hours Rationale  256 hours With an 80% learning curve, the average time to produce two batches is 80 hours per batch (100 × 80%) and the average time to produce four batches is 64 hours per batch (80 × 80%). If each batch takes an average of 64 hours to produce, the total time needed for four batches is 256 hours (64 × 4). The question asks for the time to produce the third and fourth batches only; therefore, this is an incorrect answer. Rationale  96 hours Learning curve analysis is a way to estimate the cost of an activity under the assumption that people will learn to perform the activity more efficiently the more times they perform the task. The time to produce decreases every time production doubles. In the cumulative average-time model, the full expected learning is expected to occur for all units produced (the cumulative units produced). With an 80% learning curve, the average time to produce two batches is 80 hours per batch (100 × 80%) and the average time to produce four batches is 64 hours per batch (80 × 80%). If the average time to produce the first two batches is 80 hours, the total time to complete the first two batches is 160 hours. Similarly, the total time needed to produce four batches is 256 hours (64 × 4). This means 96 hours will be needed to complete the third and fourth batches (256 − 160); therefore, this is the correct answer. Rationale  160 hours With an 80% learning curve, the average time to produce two batches is 80 hours per batch (100 × 80%). If the average time to produce the first two batches is 80 hours, the total time to complete the first two batches is 160 hours. The question asks the time to produce the third and fourth batches, not the first two batches; therefore, this is an incorrect answer. Rationale  64 hours With an 80% learning curve, the average time to produce two batches is 80 hours per batch (100 × 80%) and the average time to produce four batches is 64 hours per batch (80 × 80%). The question asks for the time to produce the third and fourth batches, not the average time to produce four batches; therefore, this is an incorrect answer. 96 hours Correct 64 hours Your Answer

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 19/69 Rationale  Total costs = $738(Phones) + $1,473,119 This is the correct regression equation (total cost equation) for the above information.

6/14/22, 10:26 AM CMA Exam Review - Part 1 - Assessment Review https://app.efficientlearning.com/pv5/v8/5/app/cma/part1_2020.html?#assessmentReview 20/69 Question 16 1.B.3.h tb.lc.anal.026_1805 LOS: 1.B.3.h Lesson Reference: Expected Value Computations and Learning Curve Analysis Difficulty: medium Bloom Code: 2 Which of the following is a limitation of expected value analysis? Expected value analysis assumes that there will be no changes in past performance in the future (for example, efficiency will not improve). Expected value analysis can only be used when performance is expected to be within the relevant range of activity. Expected value analysis cannot be used when it is difficult to accurately measure the impact of efficiency improvements. Rationale  Expected value analysis assumes that there will be no changes in past performance in the future (for example, efficiency will not improve). The need to assume that there will be no changes in past performance in the future is a limitation of linear regression analysis, not expected value analysis; therefore, this is an incorrect answer. Rationale  Expected value analysis can only be used when performance is expected to be within the relevant range of activity. The need to be within the relevant range of activity is a limitation of linear regression analysis, not expected value analysis. Therefore, this is an incorrect answer. Rationale  Expected value measures the “average” outcome of a situation. Expected value is a tool where the expected results of something (for example, sales, income, or cash flow) and the probability of those results are combined to determine the expected (weighed-average) result. The expected value calculated is then used as the basis for making a decision. One way to think of the expected value of a situation is the average result if the situation occurred a number of times; however, in reality the situation only occurs once. The actual result of one trial may be drastically different than the average result of a theoretical number of trials, which means using the average outcome of a situation is a limitation of expected value analysis. Therefore, this is the correct answer. Rationale  Expected value analysis cannot be used when it is difficult to accurately measure the impact of efficiency improvements. The difficulty of accurately measuring the impact of expected efficiency improvements is a limitation of learning curve analysis, not expected value analysis. Therefore, this is an incorrect answer. Expected value measures the “average” outcome of a situation. Correct