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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation |
| Topic 2: Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Role of data in business decisions - Types of analytics: descriptive, predictive, prescriptive |
| Topic 3: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value |
| Topic 4: Statistical Concepts & Descriptive Analytics | 25% | - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods |
| Topic 5: Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Forecasting & trend analysis - Simple & multiple linear regression |
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NEW QUESTION # 103
What is a disadvantage of using a balanced scorecard?
Answer: A
Explanation:
A key disadvantage of using abalanced scorecardis that itrequires significant time and effort to develop a meaningful and effective template. In data-driven decision making, the value of a balanced scorecard depends on careful selection of performance measures that align with organizational strategy.
Developing a balanced scorecard involves defining strategic objectives, selecting appropriate metrics, setting targets, and ensuring data availability. This process can be resource-intensive, especially in large or complex organizations. However, once implemented, the balanced scorecard offers substantial long-term benefits.
The other options are incorrect because the balanced scorecard explicitly includes both financial and nonfinancial measures and is designed to link operations with strategy. While implementation may involve some cost, expense alone is not typically cited as its primary disadvantage.
Therefore, the correct answer isA.
NEW QUESTION # 104
A firm seeks to sharply reduce sales lead times to enhance sales effectiveness and productivity. Which action should the firm take to accomplish this?
Answer: C
Explanation:
To sharply reduce sales lead times, the firm should deploy a customer relationship management tool. A CRM system helps organize customer information, automate follow-up processes, track sales opportunities, streamline communication, and improve coordination across the sales cycle. By reducing manual delays and improving access to customer data, a CRM can make the sales process faster and more efficient, directly supporting stronger sales effectiveness and productivity. Increasing the research and development budget would not address sales-cycle speed. Preparing a media campaign may generate interest or leads, but it does not necessarily reduce the time required to move prospects through the sales process. Additional quality assurance measures are more relevant to process quality and defect prevention than to reducing lead times in sales operations. Because the objective is to improve the speed and efficiency of handling prospects and customers, the most targeted and practical solution is the deployment of a CRM tool. Therefore, the correct answer is to deploy a customer relationship management tool.
NEW QUESTION # 105
Phone calls for a company are routed randomly to one of eight call centers. Six are based in the United States, and two are based in another country. What is the probability that an incoming call will be routed to a U.S.- based call center?
Answer: C
Explanation:
Probability is calculated as the number of favorable outcomes divided by the total number of possible outcomes, assuming each outcome is equally likely. In this case, there are eight call centers total, and six of them are located in the United States. Since calls are routed randomly, each call center has an equal chance of receiving an incoming call. Therefore, the probability that a call is routed to a U.S.-based call center is 6 out of 8. This fraction simplifies to 3 out of 4, which is equal to 0.75 or 75 percent. The answer choices 25 percent and 33 percent are too small because they do not match the proportion of U.S. call centers. The option 67 percent is closer but still incorrect, as 6 divided by 8 is not 0.67. This is a basic probability problem involving equally likely outcomes. Because six of the eight centers are in the United States, the correct probability is 75 percent.
NEW QUESTION # 106
An analyst used multiple linear regression to show how a big box store's sales (y) are predicted by the big box store's advertising expenditure in dollars (variable x1) and the advertising expenditure in dollars of a specialty store (variable x2) in the same market. The estimated regression is y = 651.57 + 92.30x1 # 26.89x2. How are advertising expenditures and sales related in this scenario?
Answer: C
Explanation:
The regression equation shows how each advertising variable is related to the big box store's sales while holding the other variable constant. The coefficient for x1, the big box store's advertising, is positive 92.30.
This means that when the big box store increases its own advertising expenditure, predicted sales increase.
The coefficient for x2, the specialty store's advertising, is negative 26.89. This means that as the specialty store spends more on advertising, the big box store's predicted sales decrease. Therefore, the relationship described in option A is correct. Options C and D incorrectly reverse the meaning of the positive coefficient on the big box store's own advertising. Option B is directionally true in a general sense, but the clearest direct interpretation from the equation is the negative effect of the specialty store's advertising on big box store sales, which is exactly stated in option A. Multiple regression allows analysts to isolate these effects and interpret how changes in each predictor influence the dependent variable. Thus, the correct answer is that if the specialty store increases its advertising expenditures, it will decrease the big box store's sales.
NEW QUESTION # 107
Which process is considered a statistical process control activity?
Answer: D
Explanation:
Statistical process control involves using statistical methods to monitor, control, and improve production processes. A central purpose of this approach is to determine whether a process is operating within acceptable variation limits. Therefore, determining if the precision of a manufactured product is within a tolerable range is a direct example of statistical process control. This type of activity uses measured data to evaluate consistency, detect abnormal variation, and maintain quality standards. Evaluating consumer complaints may provide useful quality feedback, but it is not the direct statistical monitoring activity described by process control methods. Aligning marketing strategy with manufacturing capabilities is a business planning issue rather than a process control task. Forecasting future consumer demand is a forecasting and analytics activity, not statistical process control. The defining feature of statistical process control is monitoring production behavior using measurable process data and tolerance limits. Therefore, the correct answer is the option that focuses on determining whether the manufactured product's precision remains within an acceptable range.
NEW QUESTION # 108
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