JNTU
JNTU B.Tech IV DATA ANALYTICS Previous Question Paper (June - 2024 - R16)
Note: i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. In Part A, Answer all questions. iii) In Part B, Answer any one question from each unit. Each question carries 10 marks and may have a, b as sub questions.
Part A
What are the drawbacks of randomized block design?
Answer
The main drawbacks of Randomized Block Design (RBD) are that it requires the experimental units to be grouped into homogeneous blocks, which can be difficult if the source of variation is unknown or complex to identify. Additionally, if there is missing data within a block, the entire block's analysis becomes statistically complicated, and the design is less flexible than a Completely Randomized Design when dealing with a large number of treatments or varying experimental conditions.
What is the difference between transaction data and reference data?
Answer
Transaction data represents the records of individual business events or activities, such as sales, deposits, or withdrawals. It is characterized by high volume, frequent updates, and a time-sensitive nature, typically containing quantitative values like amounts, dates, and timestamps.
Reference data acts as the supporting information used to classify or categorize transaction data. It consists of relatively static sets of values, such as product codes, customer addresses, currency types, or geographic regions. Unlike transaction data, reference data changes infrequently and provides the necessary context to interpret the business events recorded in the system.
What is the need of analytics?
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Give examples for MNAR and MCAR category.
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What is meant by residual in linear regression?
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List the three main purposes for using linear regression.
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What is unsupervised learning?
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What is a regression tree?
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Give examples for complex relations.
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Illustrate the usage of Chernoff faces.
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Part B
Describe the general approach of designing data architecture at three levels of specifications.
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Differentiate between noise and outlier. Describe techniques to handle them.
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Does repeated imputation improve the quality of estimation? Justify your answer with suitable examples.
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Explain the difference between reporting and analytics. Enumerate various applications of analytics.
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With appropriate data set demonstrate ordinary least square technique for linear regression.
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Compare and contrast linear regression and logistic regression.
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Illustrate the working of CHAID algorithm for constructing decision tree using suitable data.
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What are the various components of time-series data? Explain them with examples.
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Describe the importance of data visualization and illustrate geometric projection visualization.
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Which domains need data visualization applications for complex data? Explain suitable applications and domains.
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