JNTU
JNTU B.Tech IV DATA ANALYTICS Previous Question Paper (September - 2022 - R16)
Answer any Five Questions. All Questions Carry Equal Marks.
Main
A researcher wants to study the effect of diet on health parameters such as cholesterol and BP. Discuss whether he should consider primary or secondary data, suggest a suitable method for collecting that data, and justify why your suggestion is better than other methods.
Answer
For a study focused on the specific causal relationship between diet and health parameters like cholesterol and BP, primary data is the most suitable choice. While secondary data is cost-effective and readily available, it often lacks the granularity, specific dietary controls, and temporal alignment required to establish a direct link between a particular diet and physiological outcomes.
The most suitable method for collecting this data is a controlled clinical trial or a longitudinal prospective study. In this method, participants are assigned specific dietary regimens under controlled conditions, and their health parameters are measured at regular intervals using standardized medical equipment.
This approach is superior to other methods for the following reasons:
- Data Precision: Unlike secondary data, which may be collected for different purposes or lack specific dietary details, primary data allows the researcher to define exact variables, such as caloric intake, macronutrient composition, and duration of the diet.
- Control of Confounding Variables: Primary collection allows for the exclusion or matching of confounding factors like age, genetics, exercise levels, and pre-existing conditions, which are often uncontrolled in secondary datasets.
- Reliability and Validity: By using consistent measurement protocols for cholesterol and BP across all subjects, the researcher ensures high internal validity, reducing the risk of measurement bias inherent in retrospective secondary sources.
- Timeliness: Secondary data is often outdated. Primary data collection ensures that the findings reflect current dietary trends and modern diagnostic standards.
While surveys (a form of primary data) are easier to conduct, they rely on self-reported dietary habits, which are prone to recall bias. Therefore, a clinical trial or a structured prospective study provides the empirical rigor necessary to draw scientifically valid conclusions regarding health interventions.
Explain the impact of data analytics for business decisions and strategy.
Answer
Data analytics transforms raw data into actionable insights, fundamentally shifting business decision-making from intuition-based to data-driven models. Its impact on business strategy includes:
- Enhanced Operational Efficiency: Analytics identifies bottlenecks in supply chains, production processes, and resource allocation, allowing firms to optimize workflows and reduce costs.
- Customer Personalization: By analyzing consumer behavior, preferences, and purchase history, businesses can deliver targeted marketing campaigns and personalized product recommendations, increasing customer retention and lifetime value.
- Risk Mitigation: Predictive analytics allows organizations to forecast market trends, financial risks, and potential threats, enabling proactive rather than reactive strategy adjustments.
- Competitive Advantage: Real-time data processing provides a faster response to market changes, allowing companies to innovate products and pricing strategies ahead of competitors.
- Strategic Decision Support: Analytics provides quantitative evidence for long-term planning, such as market expansion, product development, or mergers, minimizing the uncertainty associated with high-stakes investments.
How to handle missing values to improve the quality of the data?
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In an organization when is analytics preferred over reporting? Contrast reporting and analytics.
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With suitable examples, explain descriptive analytics and predictive analytics.
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Explain about data modeling techniques.
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A regression between foot length (response variable in cm) and height (explanatory variable in inches) for 33 students resulted in the following regression equation:
i) What is the estimated foot length for a student with a height of 70 inches?
ii) One student in the sample was 73 inches tall with a foot length of 29 cm. What is the residual for this student?
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How do you interpret b1 in single regression and multiple regression?
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Demonstrate visualization of complex data and relations.
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Explain the role of churning faces and stick figures in data analysis.
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Differentiate between CHAID and CART approaches for decision tree construction.
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Illustrate the use of impurity measure in handling the demerits of decision tree.
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Explain Geometric projection visualization techniques.
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