Ch 7: Sampling: Tutoring Solution

About This Chapter

The Sampling chapter of this Statistics 101 Tutoring Solution is a flexible and affordable path to learning about sampling. These simple and fun video lessons are each about five minutes long and they teach all of the sampling principles and methods required in a typical statistics course.

How it works:

  • Begin your assignment or other statistics work.
  • Identify the sampling concepts that you're stuck on.
  • Find fun videos on the topics you need to understand.
  • Press play, watch and learn!
  • Complete the quizzes to test your understanding.
  • As needed, submit a question to one of our instructors for personalized support.

Who's it for?

This chapter of our statistics tutoring solution will benefit any student who is trying to learn sampling and earn better grades. This resource can help students including those who:

  • Struggle with understanding random sampling, the law of large numbers, the central limit theorem or any other sampling topic
  • Have limited time for studying
  • Want a cost effective way to supplement their statistical learning
  • Prefer learning statistics visually
  • Find themselves failing or close to failing their sampling unit
  • Cope with ADD or ADHD
  • Want to get ahead in statistics
  • Don't have access to their statistics teacher outside of class

Why it works:

  • Engaging Tutors: We make learning sampling simple and fun.
  • Cost Efficient: For less than 20% of the cost of a private tutor, you'll have unlimited access 24/7.
  • Consistent High Quality: Unlike a live statistics tutor, these video lessons are thoroughly reviewed.
  • Convenient: Imagine a tutor as portable as your laptop, tablet or smartphone. Learn sampling on the go!
  • Learn at Your Pace: You can pause and rewatch lessons as often as you'd like, until you master the material.

Learning Objectives

  • Define random sampling.
  • Learn how to select different types of random samples, including simple, stratified, cluster and systematic random samples.
  • Become familiar with the law of large numbers.
  • Explain the central limit theorem.
  • Calculate the mean and standard error of a sampling distribution.
  • Use normal distributions and the central limit theorem to find probabilities about means.

9 Lessons in Chapter 7: Sampling: Tutoring Solution
Test your knowledge with a 30-question chapter practice test
Simple Random Samples: Definition & Examples

1. Simple Random Samples: Definition & Examples

Simple random samples are the chosen part of a population which have an equal chance of being selected. Learn about simple random sampling, explore the definition and examples of simple random samples, and understand when it is best to use simple random sampling.

What is Random Sampling? - Definition, Conditions & Measures

2. What is Random Sampling? - Definition, Conditions & Measures

Random sampling is a method of data collection in which each sample has an equal chance of being chosen. Learn the definition of random sampling, explore how they are used, and discover their conditions and measures.

Stratified Random Samples: Definition, Characteristics & Examples

3. Stratified Random Samples: Definition, Characteristics & Examples

Stratified random samples are taken from sub-groups of a population called strata. Learn more about the definition, characteristics, and examples of stratified random sampling, and understand when and how to use this type of sampling.

Cluster Random Samples: Definition, Selection & Examples

4. Cluster Random Samples: Definition, Selection & Examples

Cluster random samples refer to the participants including all members of a population, which are selected through cluster sampling. Learn about cluster sampling and cluster random sampling, explore the definition, selection, and examples of cluster random samples, and understand how and when to use cluster sampling in research.

Systematic Random Samples: Definition, Formula & Advantages

5. Systematic Random Samples: Definition, Formula & Advantages

In systematic random sampling, the random samples are taken at regular periodic intervals. Learn more about the definition, formula, and advantages of systematic random sampling, and discover how, when, and why this type of sampling is used.

Understanding the Law of Large Numbers

6. Understanding the Law of Large Numbers

Understanding the law of large numbers entails learning different statistical concepts such as the sample size, the sample mean, and the population mean. Learn more about the law of large numbers and how it can be applied in a real-world context.

Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples

7. Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples

A sampling distribution is a way that a set of data looks when plotted on a chart, and the central limit theorem states that the more an experiment is run, the more its data will resemble a normal distribution. Review the definition, formula, and examples for both of these concepts.

Find the Mean & Standard Error of the Sampling Distribution

8. Find the Mean & Standard Error of the Sampling Distribution

The mean is simply the average of a sample, while the standard error is a measure of how accurately that mean reflects the mean of the total population. In this lesson, learn how to find both these values in a sampling distribution.

Finding Probabilities About Means Using the Central Limit Theorem

9. Finding Probabilities About Means Using the Central Limit Theorem

The Central Limit Theorem implies that the mean of a population can be estimated by the sample means. Learn the definition and implications of the theorem and explore how probabilities in opinion polls are determined using the Central Limit Theorem.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
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Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
Not Taken
More Exams
There are even more practice exams available in Sampling: Tutoring Solution.

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