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2. Explain the systematic random sampling approach and provide an example.

2. Explain the systematic random sampling approach and provide an example.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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**Question 2: Explain the systematic random sampling approach and provide an example.** Systematic random sampling is a method used to select a sample from a larger population in a structured but random way. It involves selecting elements from an ordered list at regular intervals, starting from a randomly chosen point. **Steps:** 1. **Number the List**: Start by numbering each member of the population sequentially. 2. **Random Start**: Randomly select a starting point. 3. **Select at Intervals**: Choose every kth member in the list after the starting point. **Example:** Suppose you want to select a sample of 10 students from a list of 100. You randomly select the 5th student to start and then choose every 10th student after that (5th, 15th, 25th, etc.) until you have your desired sample size. This technique ensures that the sample is spread evenly over the population and reduces bias compared to simple random sampling.
Transcribed Image Text:**Question 2: Explain the systematic random sampling approach and provide an example.** Systematic random sampling is a method used to select a sample from a larger population in a structured but random way. It involves selecting elements from an ordered list at regular intervals, starting from a randomly chosen point. **Steps:** 1. **Number the List**: Start by numbering each member of the population sequentially. 2. **Random Start**: Randomly select a starting point. 3. **Select at Intervals**: Choose every kth member in the list after the starting point. **Example:** Suppose you want to select a sample of 10 students from a list of 100. You randomly select the 5th student to start and then choose every 10th student after that (5th, 15th, 25th, etc.) until you have your desired sample size. This technique ensures that the sample is spread evenly over the population and reduces bias compared to simple random sampling.
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