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Data Comparison Solved Examples - UGC NET PDF Download

DIRECTIONS for questions 1 to 9: In each of these questions, two quantities are mentioned, at column A and at column B. 

To mark the answers to the questions, use the following instructions.

A: If the quantity in column A is greater. 

B: If the quantity in column B is greater.

C: If the two quantities are equal.

D: If the relationship cannot be determined.

Example 1:

Column AColumn B
The circumference of a circle with radius 2
The sum of the circumferences of two circles, each with radius 1

Ans: C

Sol: Circumference of circle = 2 × 3.14 × r.
So, for r = 2, C = 2 × 3.14 × 2 = (4 × 3.14).

For, r = 1, C = 2 × 3.14 × 1.  
For two circles of this type, C = (2 × 3.14 + 2 × 3.14) ⇒; 4 × 3.14.

We got the same result from both the columns.

Hence, the answer is (C)

Example 2:

Column AColumn B
2y
100

Ans: A

Sol: y = sum of first ten positive integers = (10 × 11) ÷ 2 = 55
A = 2 × 55 = 110.
B = 100
A is greater than B. So, the answer is option (A).

Example 3:

Column AColumn B
The number of ways in which 6 can be expressed as a product of two different single digit positive integers 
The number of ways in which 12 can be expressed as a product of two different single digit positive integers 

Ans: C
Sol:

Column A : 6 can be expressed as 6 × 1 and 2 × 3. (2 ways)
Column B : 12 can be expressed as 12 × 1, 6 × 2 and 3 × 4 (3 ways). Out of these, 12 × 1 contains a two-digit number. As that is not to be considered, the valid number of ways reduces to 2. Thus, in both the cases, the valid number of cases is 2. Hence, the answer is option (C).

Exmaple 4:

Column A
Column B
The maximum number of small doughnuts that can be made with 3 packages of mix
The maximum number of large doughnuts that can be made with 4 packages of mix

Ans: A

Sol: Small doughnuts: 1 pack makes 12, 3 packs makes 12 x 3 = 36;

Large doughnuts: 1 pack makes 8, 4 packs makes 4 x 8 = 32. So, the answer is (A).

Example 5: A = {1, 2, 3} B = {4, 5, 6, 7}

Column A
Column B
The total number of ordered pairs (a, b) that can be formed where a is from set A and b is from set B
The total number of ordered pairs (a, b) that can be formed where a is from set A∪B and b from set A

Ans: B

Sol: n(A ) = 3, n( B) = 4, n(A∪B ) = 7;
Ordered pair from set A to set B is = 3 x 4 = 12,
Number of ordered pairs from set A ∪B to set A is = 7 x 3 = 21. So, the answer is (B)

Example 6: Three times the sum of x and y is 18

Column A
Column B
Twice the sum of x and y
12

Ans: C

Sol: 3 (x + y) = 18.  (x + y) = 6.

2 (x + y) = 12. So, the answer is (C).

Example 7: a + b = 0

Column A
Column B
a.a.ab.b.b

Ans: D
Sol: a + b = 0; a = - b.  
For b = 2, a = -2,
a3 = -8, b3= 8

But for b = -2, a = 2,
a3= 8, b3= -8.
So, the answer is (D).

Example 8:  The total cost of 1 apple and 1 orange is Rs. 1.70

Column A
Column B
The cost of one apple
The cost of one orange

Ans :D

Sol:  Let the cost of 1 apple = Rs. x, cost of 1 orange = Rs. y

Then, x + y = Rs. 1.70 but various combinations of x and y are possible. So, the answer is (D)

Example 9: When Rina was 10 years old, the price of a certain item was Rs. 100.

Column A
Column B
The price of the same item when Rina will be 12 years old
Rs. 100

Ans: D
Sol: No relation between age of Rina and price of the item is mentioned. So, the answer is (D).


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FAQs on Data Comparison Solved Examples - UGC NET

1. What is data comparison?
Ans. Data comparison refers to the process of analyzing and evaluating data from different sources or datasets to identify similarities, differences, patterns, or trends. It involves comparing and contrasting data elements, such as values, attributes, or characteristics, to gain insights and make informed decisions.
2. Why is data comparison important in data analysis?
Ans. Data comparison plays a crucial role in data analysis as it helps in identifying discrepancies, errors, or inconsistencies in the data. By comparing data from various sources or datasets, analysts can validate the accuracy and reliability of the information. It also enables them to detect outliers, understand data variations, and uncover valuable insights that can drive better decision-making.
3. What are the common methods used for data comparison?
Ans. There are several methods employed for data comparison, depending on the nature of the data and the analysis requirements. Some common methods include: - Visual comparison: This involves visually inspecting the data side by side to identify any visible differences or patterns. - Statistical comparison: This method utilizes statistical techniques to quantify the differences between datasets, such as calculating mean, standard deviation, or correlation coefficients. - Textual comparison: It involves comparing textual data, such as documents or text files, to identify similarities or differences using techniques like text matching or natural language processing. - Record-level comparison: This method compares individual records or rows in databases or spreadsheets to identify discrepancies or duplicates.
4. How can data comparison be useful in business decision-making?
Ans. Data comparison is highly valuable in business decision-making processes. By comparing data from different time periods, regions, or market segments, organizations can identify trends, patterns, or changes that impact their operations. It helps in evaluating the performance of products, services, or strategies and facilitates benchmarking against competitors or industry standards. Moreover, data comparison enables businesses to detect anomalies, resolve data quality issues, and optimize processes for enhanced efficiency and profitability.
5. What challenges can arise during data comparison?
Ans. Several challenges may arise during data comparison, including: - Data compatibility: Different data formats, structures, or systems may hinder seamless comparison, requiring data transformation or integration efforts. - Data quality issues: Inaccurate, incomplete, or inconsistent data can lead to misleading comparisons and unreliable insights. Data cleansing and normalization may be necessary to ensure accurate results. - Data volume: Large volumes of data can pose challenges in terms of processing time, storage capacity, and analysis complexity. Efficient data management and analysis techniques are required to handle such volumes. - Data privacy and security: Comparing sensitive or confidential data from multiple sources may raise privacy and security concerns. Adequate measures should be taken to protect data confidentiality and comply with regulations. - Subjectivity in interpretation: Different analysts may interpret data differently, leading to subjective comparisons. Establishing clear analysis criteria and ensuring consistency in interpretation is essential for meaningful comparisons.
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