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| Section | Objectives |
|---|---|
| Topic 1: Decision-Making Frameworks | - Risk Analysis and Assessment - Evidence-Based Decision Making - Cost-Benefit Analysis - Rational Decision Models |
| Topic 2: Business Intelligence and Analytics | - Predictive Analytics Basics - Data Mining Concepts - Key Performance Indicators (KPIs) - Dashboards and Reporting |
| Topic 3: Data Analysis Fundamentals | - Data Quality and Cleaning - Descriptive Statistics - Data Types and Measurement Scales - Data Visualization Techniques |
| Topic 4: Data-Driven Culture and Communication | - Presenting Data Insights - Building Data-Informed Organizations - Ethical Considerations in Data Use - Stakeholder Communication |
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NEW QUESTION # 48
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: D
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 # 49
A company runs a regression analysis to identify the impact of volume on demand, which can be shown as an equation y = 50x + 10. Which volume is needed to produce a demand of 10,510?
Answer: D
Explanation:
To solve this regression problem, substitute the given demand value into the equation and solve for x. The equation is y = 50x + 10, and the desired demand is 10,510. Replacing y with 10,510 gives 10,510 = 50x + 10.
Subtracting 10 from both sides gives 10,500 = 50x. Dividing both sides by 50 gives x = 210. This means a volume of 210 is required to produce a demand of 10,510 under the linear relationship described by the equation. Regression equations are often used in forecasting and planning because they allow analysts to estimate one value from another. In this case, the independent variable is volume and the dependent variable is demand. The correct value must satisfy the equation exactly, and only 210 does so. The other options do not produce the required result when substituted into the equation. Therefore, the correct answer is 210.
NEW QUESTION # 50
What must be analyzed using powerful analytic tools?
Answer: C
Explanation:
Big datamust be analyzed using powerful analytic tools due to its volume, velocity, and variety. In data- driven decision making, traditional tools are often insufficient for processing massive, complex datasets generated from digital platforms, sensors, and transactions.
Big data requires advanced computing power, specialized software, and sophisticated algorithms to extract meaningful insights. Inferential statistics and small datasets can often be handled with conventional statistical tools.
Therefore, the correct answer isC, big data.
NEW QUESTION # 51
Which type of study is also known as a quasi-experimental study?
Answer: B
Explanation:
A **quasi-experimental study** is commonly referred to as an **observational study** in data-driven decision making. Unlike true experiments, quasi-experimental studies do not involve random assignment of subjects to treatment and control groups. Instead, researchers observe outcomes in naturally occurring groups and attempt to draw conclusions about relationships between variables.
In observational studies, the researcher does not control the assignment of treatments. As a result, these studies are more susceptible to bias and confounding variables than randomized experiments. However, they are often necessary when controlled experimentation is impractical, unethical, or too costly. For example, studying the impact of policy changes or economic conditions typically relies on observational data.
Blind studies are a form of experimental design used to reduce bias, hypothesis testing is a statistical process rather than a study type, and content validity refers to measurement quality. None of these represent quasi- experimental designs.
In data-driven decision making, observational (quasi-experimental) studies are valuable for identifying associations and generating insights, but analysts must be cautious not to infer causality without proper controls. Therefore, the correct answer is **A**.
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NEW QUESTION # 52
A clothing company wants to predict sales figures based on the amount spent on advertising.
Which type of regression analysis should this company use?
Answer: D
Explanation:
When predicting a continuous outcome based on a single predictor, data-driven decision making recommends simple linear regression. In this case, sales figures are continuous, and advertising spend is a single explanatory variable.
Linear regression models the relationship between one independent variable and one dependent variable by estimating a straight-line relationship. Time series regression is used when data are indexed over time, logistic regression is used for binary outcomes, and multiple linear regression requires multiple predictors.
Because the company is using only advertising spend to predict sales,linear regressionis the most appropriate method. Therefore, the correct answer isB.
NEW QUESTION # 53
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