# Arranging and Collecting Data Class 9 Questions and Answers

Objective Type Questions

1. A school named ABC has recorded the total marks of every student in the class. This is an example of:

a) Qualitative data

b) Quantitative data

c) Both qualitative and quantitative data

d) None of the above

2. A food delivery app has asked for your feedback on the quality of the food. You have written two paragraphs to describe the food. This is an example of:

a) Qualitative data

b) Quantitative data

c) Both qualitative and quantitative data

d) None of the above

3. You need to predict what the temperature will be for next Friday. Which algorithm will you use?

a) Clustering

b) Regression

c) Anomaly detection

d) Binary classification

4. You need to predict if your car tyre will last for the next 1000 km. Which algorithm will you use?

a) Clustering

b) Regression

c) Anomaly detection

d) Binary classification

5. Which of the following options are the benefits of Big data processing?

a) Business can utilize outside intelligence while making decisions

b) Improved customer service

c) Better optimal efficiency

d) All of the above

### Arranging and Collecting Data Class 9 Questions and Answers

6. The analysis of large amounts of data to see what patterns or other useful information can be found is known as-

a) Data Analysis

b) Information Analytics

c) Big data Analytics

d) Data Analytics

7. Big data analysis does the following except

a) Collects data

c) Organizes data

d) Analyzes data

8. Primary data for the research process can be collected through

a) Experiment

b) Survey

c) Both a and b

d) None of the above

Answer- c) Both a and b

9. The advantage of secondary data are low cost, speed, availability, and flexibility

a) True

b) False

10. The method of getting primary data by watch people is called

a) Survey

b) Informative

c) Observational

d) Experimental

## Standard Questions

1. What is the difference between multivariate and univariate data? Give some examples.

The primary distinction between multivariate and univariate data analysis resides in the number of variables being investigated simultaneously. Univariate analyses involve studying one variable at a time, such as measuring student heights. However, multivariate analyses include simultaneous analyses on multiple dimensions such as studying relationships among height, weight and age in one class.

1. What are the common sources of data collection?

Common sources of data collection include surveys, interviews, observations, experiments and existing records or databases. In terms of collecting information from various sources: surveys involve asking questions to gather answers; interviews involve direct dialogues between two people to gain knowledge and gather opinions; observations include monitoring behavior as it occurs and recording it for later evaluation by experts; experiments manipulate variables to test out outcomes while existing records provide historical references for analysis.

### Arranging and Collecting Data Class 9 Questions and Answers

1. What are the primary characteristics of Big Data?

Big Data can be defined by three primary characteristics: Volume (large amounts of data), Velocity (high-speed processing of this data) and Variety (diverse data types such as text, images and videos). Because Big Data frequently exceeds traditional databases’ processing capacities, advanced analytics tools and technologies are often necessary.

1. What are categorical variables? Give some examples.

Categorical variables are non-numeric data representing groups or categories. They can be divided into nominal (no particular order) and ordinal (ordered categories).

Examples of nominal categorical variables include gender (male, female), colors (red, blue and green) as well as education levels (high school, college and graduate).

1. How is Big Data used in social media?

Social media uses big data extensively to enhance user experiences, target ads, and analyze user behavior. Social media platforms gather large quantities of user information such as posts, likes, comments and interactions which is then analyzed to create personalized content recommendations, detect patterns in usage behavior and understand user preferences.

Furthermore, sentiment analysis on posts provides businesses with important market intelligence which assists with decision-making and market intelligence strategies.