About This Chapter
CSET Mathematics Subtest II: Statistics - Chapter Summary
If you're getting ready to take the CSET Mathematics Subtest II, use this chapter's video lessons to prepare for questions covering statistics. Instructors show you how to work with the following:
- Mean, median, mode and range
- Random sample types
- Continuous probability distributions
- Discrete probability distributions
- Simple linear regressions
- Correlation and causation
- The method of least squares
- Chi-square tests
Whether you need help sorting out the differences between simple, stratified and cluster random samples or just want to find out what's on the math subtest II, this chapter's video lessons and transcripts are organized so that you can skip ahead or go back to whichever topics interest you. There are also self-assessment quizzes you can use to find out how you stand in terms of your preparedness.
CSET Mathematics Subtest II: Statistics - Chapter Objectives
The CSET Mathematics Subtest II is one of three exams used to determine the academic preparedness of candidates for math teaching credentials in California. It contains 30 multiple-choice questions in addition to four constructed-response questions requiring you to briefly formulate your own answer.
You can use the lessons in this chapter to prepare for the one constructed-response and eight multiple-choice questions focusing on probability and statistics. It's on this part of the exam where you'll be asked to find measures of central tendency, the standard deviation, range and variance for both discrete and continuous probability distributions. You'll also need an ability to discern which sampling method should be used to complete your assigned task, as well as an aptitude for working with linear regression and correlation.
1. The Mean vs the Median: Differences & Uses
Most people can find the mean and the median of a data set, but do you know when to use the mean and when to use the median to describe the information?
2. Calculating the Mean, Median, Mode & Range: Practice Problems
Calculating the mean, median, mode, and range of a data set is a fundamental part of learning statistics. Use this video to practice your skills and then test your knowledge with a short quiz.
3. Developing Continuous Probability Distributions Theoretically & Finding Expected Values
What is an expected value? How can you tell how many time you should expect a coin to land on heads out of several flips? This lesson will show you the answers to both questions!
4. Developing Discrete Probability Distributions Empirically & Finding Expected Values
In this lesson, we will look at creating a discrete probability distribution given a set of discrete data. We will also look at determining the expected value of the distribution.
5. Developing Discrete Probability Distributions Theoretically & Finding Expected Values
In this lesson, we will look at generating a theoretical probability distribution for a discrete random variable and introduce the concept of expected value.
6. What is Random Sampling? - Definition, Conditions & Measures
Random sampling is used in many research scenarios. In this lesson, you will learn how to use random sampling and find out the benefits and risks of using random samples.
7. Simple Random Samples: Definition & Examples
Simple random sampling is a common method used to collect data in many different fields. From psychology to economics, simple random sampling can be the most feasible way to get information. Learn all about it in this lesson!
8. Stratified Random Samples: Definition, Characteristics & Examples
Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.
9. Cluster Random Samples: Definition, Selection & Examples
Cluster random sampling is one of many ways you can collect data. Sometimes it can be confusing knowing which way is best. This lesson explains cluster random sampling, how to use it, and the differences between cluster and stratified sampling.
10. Systematic Random Samples: Definition, Formula & Advantages
Systematic random sampling is a great way to randomly collect data on a population without the hassle of putting names in a bag or using a random number generator. In this lesson, learn all about how and when to use systematic random sampling.
11. Understanding the Law of Large Numbers
The law of large numbers is a concept that is often misunderstood in statistics. In this lesson, you will learn the real meaning of the law of large numbers and how it is employed.
12. Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples
Want proof that all of this normal distribution talk actually makes sense? Then you've come to the right place. In this lesson, we look at sampling distributions and the idea of the central limit theorem, a basic component of statistics.
13. Find the Mean & Standard Error of the Sampling Distribution
Have you ever had a situation where one grade destroyed your average? Wouldn't you like a way of proving that your work was actually pretty good with that one exception? The standard error gives you such a chance.
14. Finding Probabilities About Means Using the Central Limit Theorem
The central limit theorem provides us with a very powerful approach for solving problems involving large amount of data. In this lesson, we'll explore how this is done as well as conditions that make this theorem valid.
15. Simple Linear Regression: Definition, Formula & Examples
Simple linear regression is a great way to make observations and interpret data. In this lesson, you will learn to find the regression line of a set of data using a ruler and a graphing calculator.
16. Problem Solving Using Linear Regression: Steps & Examples
Linear regression can be a powerful tool for predicting and interpreting information. Learn to use two common formulas for linear regression in this lesson.
17. Correlation vs. Causation: Differences & Definition
When conducting experiments and analyzing data, many people often confuse the concepts of correlation and causation. In this lesson, you will learn the differences between the two and how to identify one over the other.
18. What is a Chi-Square Test? - Definition & Example
This lesson explores what a chi-square test is and when it is appropriate to use it. Using a simple example, we will work on understanding the formula and how to calculate the p-value.
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Other chapters within the CSET Math Subtest II (212): Practice & Study Guide course