About This Chapter
Dispersion & Probability Models - Chapter Summary
This chapter can help enhance your understanding of dispersion and probability models with entertaining lessons developed by expert instructors. Review these lessons anytime to learn more about mean, median and mode, the measures of variability, the fundamental counting principle and other concepts. Doing so can ensure you're prepared for the following at the end of this chapter:
- Describing the mode, mean and median
- Discussing the relationship between range, variance and standard deviation
- Utilizing the formula for calculating weight average
- Identifying maximums, minimums and outliers in a data set
- Calculating the probability of combinations, permutations and independent and dependent events
- Defining the fundamental counting principle
The video lessons in this chapter average about 8 minutes each, simplifying the process of fitting your review into your busy schedule. Take advantage of the full transcript below the videos that offer easily identifiable vocabulary words that can deepen your understanding of dispersion and probability models. When ready, test your knowledge of these concepts by taking short lesson quizzes and a comprehensive chapter exam. Also, submit any questions you have about lesson topics to our experts.
1. Mean, Median & Mode: Measures of Central Tendency
By describing the data using central tendency, a researcher and reader can understand what the typical score looks like. In this lesson, we will explore in more detail these measures of central tendency and how they relate to samples and populations.
2. Measures of Variability: Range, Variance & Standard Deviation
Looking specifically at range, variance, and standard deviation, this lesson explores the relationship between these measures and samples, populations, and what it says about your data.
3. Calculating Weighted Average: Method, Formula & Example
This lesson will examine the concept of a weighted average and types of situations when it should be used instead of a standard average. It will also give some real-world examples of a weighted average.
4. Maximums, Minimums & Outliers in a Data Set
When analyzing data sets, the first thing to identify is the maximums, minimums, and outliers. This lesson will help you learn how to identify these important items.
5. Probability of Independent and Dependent Events
Sometimes probabilities need to be calculated when more than one event occurs. These types of compound events are called independent and dependent events. Through this lesson, we will look at some real-world examples of how to calculate these probabilities.
6. How to Calculate the Probability of Combinations
To calculate the probability of a combination, you will need to consider the number of favorable outcomes over the number of total outcomes. Combinations are used to calculate events where order does not matter. In this lesson, we will explore the connection between these two essential topics.
7. How to Calculate the Probability of Permutations
In this lesson, you will learn how to calculate the probability of a permutation by analyzing a real-world example in which the order of the events does matter. We'll also review what a factorial is. We will then go over some examples for practice.
8. Fundamental Counting Principle: Definition & Examples
In this lesson, you will learn about the fundamental counting principle, a method for determining how many ways choices can be made from groups. Several examples will be given.
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