Page 184 - Data Science class 11
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8. What steps are required to conduct online surveys?
9. Distinguish between a survey and a questionnaire.
10. Distinguish between open-ended and closed-ended questions by giving examples for each type.
11. Explain the strength and weaknesses of the survey method for research.
12. What is the difference between population and sampling?
13. How many types are the most commonly used sampling techniques? Which one of them is taken as the best sampling
method for qualitative research?
B. Long answer type questions:
1. Discuss the pros and cons of commonly used sampling techniques.
2. Enumerate and differentiate among five Common Types of Sampling Errors.
3. What is sampling bias? Is it reasonable to have a sampling bias? Provide an example to support your answer.
4. Identify the 6 main types of bias in research and their sources.
5. How do you avoid Sampling Bias in survey methods?
6. Given below are instances of biased survey questions. Point out the biasness involved and try rephrasing the questions
so that the subject responding to them can generate meaningful responses. a) How amazing was your experience
with our customer service team? b) What problems did you have with the launch of this new product? c) How do we
compare to our competitors? d) Do you always use product X for your cleaning needs?
7. What is a confidence interval? What are the different factors affecting the values of a confidence interval for a given
population?
8. Explain with an example how sensors are used widely in the field of healthcare to collect data and monitor patients'
health conditions.
9. What are intelligent information-aware devices?
10. Write down how IoT is involved in various sectors of life.
11. Justify the statement, "Data collection via sensors requires the least amount of human involvement."
12. What are the advantages of extracting online (Secondary Data) as compared to offline data?
13. What does XML stand for? For what purpose was XML devised? Is it a programming language?
14. Discuss the major features of the XML concept.
Higher Order Thinking Skills (HOTS)
Please answer the questions below in no less than 200 words.
1. India is a nation where most people are incredibly fond of watching cricket as a sport. In order to better strategise
against the opponent, each team analyses the strengths and weaknesses of the opponent, and a lot of data is analysed
off the field.
The sports industry uses sports analysis to increase revenue, improve player performance and a team's quality of play,
prevent injury, and many more enhancements. Analytics has many on-field applications in a sports environment,
including managing both individual and group performance. Sports analytics is a collection of relevant, historical,
statistics that can provide a competitive advantage to a team or individual. Through the collection and analysis of this
data, sports analytics informs players, coaches, and other staff in order to facilitate decision-making both during and
prior to sporting events. Coaches can use data to optimise exercise programs for their players and develop nutrition
plans to maximise fitness. Analytics is also commonly used in developing tactics and team strategies. Sports analytics is
changing the way your favourite team makes decisions, drafts players, prepares for their defensive strategy, and much
more.
Before the Tokyo Olympics 2021, many sports organisations predicted the name of the team that would get the most
medals. The USA, China, and Japan were among them.
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