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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution |
| Topic 2: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance - Role of data in business decisions |
| Topic 3: Decision Models & Prescriptive Analytics | 15% | - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis - Quality & process improvement tools |
| Topic 4: Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression - Forecasting & trend analysis |
| Topic 5: Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation - Probability rules, distributions, expected value |
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NEW QUESTION # 107
A retail manager collected the following sales-receipt totals from the store's cashiers:
$25, $22, $48, $42, $32, $28, $24, $54, $34, $41, $48
What is the median of this sales-receipt data?
Answer: B
Explanation:
Themedianis the middle value of a dataset when the data is arranged in ascending order. It is a key descriptive statistic used in data-driven decision making because it is resistant to extreme values.
First, sort the data in ascending order:
22, 24, 25, 28, 32,34, 41, 42, 48, 48, 54
There are 11 values in total, so the median is the 6th value. The 6th value is$34, making it the median.
The median provides insight into the typical transaction size without being influenced by unusually large receipts. Therefore, the correct answer isB.
NEW QUESTION # 108
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: A
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 # 109
What is the formula for calculating a simple index number?
Answer: C
Explanation:
Asimple index numberis used in data-driven decision making to measure relative change in a variable over time, commonly prices, quantities, or values. The standard formula for a simple price index compares the value in the current period to a base period and expresses the change as a percentage.
The correct formula is thecurrent price divided by the base year's price, multiplied by 100. This standardization allows analysts and managers to easily interpret changes relative to a reference point. An index value of 100 indicates no change from the base year, values above 100 indicate an increase, and values below 100 indicate a decrease.
This approach is widely used in economic analysis, inflation tracking, and business trend evaluation because it simplifies comparisons across time. Dividing the base year by the current price would reverse interpretation, and omitting multiplication by 100 would fail to express the index in percentage terms.
Therefore, optionAcorrectly represents the formula for calculating a simple index number.
NEW QUESTION # 110
A financial analyst theorizes that commute times increase as the percentage of land availability for homes in a city decreases. To test this hypothesis, the analyst uses a regression analysis to explore how land availability predicts commute time.
What does land availability represent in this regression?
Answer: C
Explanation:
In regression analysis, theindependent variableis the predictor used to explain or estimate changes in another variable. In data-driven decision making, identifying the correct variable roles is essential for meaningful interpretation.
In this scenario, land availability is used to predict commute time. Commute time is the outcome being explained, making it the dependent or target variable. Land availability influences or explains changes in commute time, which makes it theindependent variable.
Controls are additional variables included to isolate effects, but land availability is the primary predictor of interest. Therefore, optionCis correct.
NEW QUESTION # 111
A researcher notes that people who exercise daily tend to sleep better. The researcher then asks subjects to keep a sleep diary and record the number of hours they sleep each night. Some diaries have few entries, but the subjects claim they are well rested. Which type of error is represented in this research?
Answer: B
Explanation:
This scenario illustrates response bias because the quality and completeness of the recorded responses are questionable. The researcher depends on subjects to accurately document their sleeping hours in diaries, yet some subjects submit few entries while still claiming they are well rested. This creates a mismatch between documented evidence and self-reported perception. Response bias occurs when participants provide answers that do not fully or accurately reflect reality, whether intentionally or unintentionally. In this case, the subjects may be estimating, omitting information, or reporting what they believe sounds acceptable rather than what was consistently recorded. The problem is not mainly lack of blinding, since there is no indication that concealed treatment assignment is relevant. It is also not primarily cause-and-effect confusion, because the issue described concerns the reliability of the reported data rather than drawing a causal conclusion.
Conscious bias is less fitting because the question points to flawed responses rather than deliberate manipulation by the researcher. Therefore, the best answer is response bias, as the data collected from subjects may not accurately represent their actual sleep behavior.
NEW QUESTION # 112
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