About This Chapter
Explorations in Core Math Algebra 2 Chapter 8: Data Analysis and Statistics - Chapter Summary and Learning Objectives
Whether you're conducting research or following the batting average of your favorite baseball players, you're probably working with statistics. They are a part of life that affects practically everything around us in some way. Learn more about them, how to use them and what they mean to you in these short video lessons. The lessons cover the following material:
- How to interpret histograms and other charts
- Definition of mean, mode and median
- Explanation and types of survey research and observational studies
- Difference between normal, sampling and probability distributions
|Mean, Median & Mode: Measures of Central Tendency||Describe and explain mode, mean and median.|
|What are Center, Shape, and Spread?||Define terms used in graphs.|
|What is Categorical Data? - Definition & Examples||Understand the different uses for categorical data.|
|Simple Random Samples: Definition & Examples||Explore applications of random sampling.|
|Convenience Sampling in Statistics: Definition & Limitations||Understand limits of convenience sampling.|
|Systematic Random Samples: Definition, Formula & Advantages||Learn how to collect data using random sampling.|
|Stratified Random Samples: Definition, Characteristics & Examples||Explore the uses of stratified random sampling.|
|Cluster Random Samples: Definition, Selection & Examples||Look at the uses cluster random sampling.|
|Make Estimates and Predictions from Categorical Data||Use categorical data to make estimations.|
|What Is Survey Research? - Definition, Methods & Types||Examine methods of conducting surveys.|
|Experiments vs. Observational Studies: Definition, Differences & Examples||Evaluate and examine ways of collecting data.|
|Hypothesis Testing: Comparing the Null & Alternative Hypothesis||Distinguish between null and alternative hypothesis.|
|Sampling Distribution: Definition, Models & Example||Interpret a sampling distribution.|
|Probability Distribution: Definition, Formula & Example||Explore applications of probability distribution.|
|Normal Distribution of Data: Examples, Definition & Characteristics||Work with histograms and learn to understand normal distribution.|
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. What are Center, Shape, and Spread?
Center, shape, and spread are all words that describe what a particular graph looks like. Watch this video lesson to see how you can identify and explain each.
3. What is Categorical Data? - Definition & Examples
Categorical data is one of two types of data that you can collect when conducting research. This lesson will teach you how to understand and use categorical data.
4. 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!
5. Convenience Sampling in Statistics: Definition & Limitations
Convenience sampling is one of the most common types of sampling in research. This is because of the benefits that convenience sample brings to the researcher. However, there are some limitations. You will learn about both in this lesson.
6. 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.
7. 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.
8. 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.
9. Make Estimates and Predictions from Categorical Data
Categorical data can be estimated but not predicted. Learn why in this video lesson along with how to read and gather information from a bar graph of categorical data.
10. What Is Survey Research? - Definition, Methods & Types
This lesson explores the ways a researcher may employ the types of surveys used in research. We will also go over the strengths and weaknesses of each type of survey.
11. Experiments vs Observational Studies: Definition, Differences & Examples
There are different ways to collect data for research. In this lesson, you will learn about collecting data through observational studies and experiments and the differences between each.
12. Hypothesis Testing: Comparing the Null & Alternative Hypothesis
This lesson explores the process of comparing the null and the alternative hypothesis, as well as how to differentiate between the two after your testing is done.
13. Sampling Distribution: Definition, Models & Example
Researchers frequently use sample data to draw conclusions about a population using sampling distributions. In this lesson, you will learn about the definition of a sampling distribution and review an example with an illustration.
14. Probability Distribution: Definition, Formula & Example
Probability distribution is a way of mapping out the likelihood of all the possible results of a statistical event. In this lesson, we'll look at how that is done and how to make practical applications of this concept.
15. Normal Distribution of Data: Examples, Definition & Characteristics
In this lesson, we'll explore the normal distribution of data. Learn about the characteristics of normal distribution, how to plot histograms, the empirical rule, and more.
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Other chapters within the Explorations in Core Math - Algebra 2: Online Textbook Help course
- Explorations in Core Math Algebra 2 Chapter 1: Foundations for Functions
- Explorations in Core Math Algebra 2 Chapter 2: Quadratic Functions
- Explorations in Core Math Algebra 2 Chapter 3: Polynomial Functions
- Explorations in Core Math Algebra 2 Chapter 4: Exponential and Logarithmic Functions
- Explorations in Core Math Algebra 2 Chapter 5: Rational and Radical Functions
- Explorations in Core Math Algebra 2 Chapter 6: Properties and Attributes of Functions
- Explorations in Core Math Algebra 2 Chapter 7: Probability
- Explorations in Core Math Algebra 2 Chapter 9: Sequences and Series
- Explorations in Core Math Algebra 2 Chapter 10: Trigonometric Functions
- Explorations in Core Math Algebra 2 Chapter 11: Trigonometric Graphs and Identities
- Explorations in Core Math Algebra 2 Chapter 12: Conic Sections