About This Chapter
Statistics - Chapter Summary and Learning Objectives
Statistics is the branch of math that deals with the collection and analysis of data. The lessons in this chapter will teach you how to gather samples, calculate averages and create tables. After completing this chapter, you should be able to:
- Understand the conditions that make a sample random
- Calculate mean, median, mode and range
- Interpret standard deviations
- Find the minimums, maximums and outliers in a data set
- Create histograms
|Descriptive & Inferential Statistics: Definition, Differences & Examples||Learn the differences between descriptive and inferential statistics|
|What's the Difference between Populations & Samples? - Lesson & Quiz||Learn the difference between a sample and a population|
|What is Random Sampling? - Definition, Conditions & Measures||Look at the conditions that need to be met for a sample to be random|
|Simple Random Samples: Definition & Examples||Learn how to recognize simple random samples|
|Stratified Random Samples: Definition, Characteristics & Examples||Learn how to recognize stratified random samples|
|Cluster Random Samples: Definition, Selection & Examples||Learn how to recognize cluster random samples|
|Systematic Random Samples: Definition, Formula & Advantages||Learn how to recognize systematic random samples|
|How to Calculate Mean, Median, Mode & Range||Learn how to find the mean, median, mode and range of a data set|
|Calculating the Mean, Median, Mode & Range: Practice Problems, Lesson & Quiz||Practice finding the mean, median, mode and range of a data set|
|Population & Sample Variance: Definition, Formula & Examples||Explore the differences between a population and sample variance|
|Standard Deviation and Shifts in the Mean||Learn how to interpret standard deviation and look at ways a shift in the mean can impact it|
|Maximums, Minimums & Outliers in a Data Set: Lesson & Quiz||Learn how to determine minimums, maximums and outliers in data sets|
|Quartiles & the Interquartile Range: Definition, Formulate & Examples||Learn how to determine quartiles and the interquartile range|
|Finding Percentiles in a Data Set: Formula, Examples & Quiz||Calculate percentiles in a data set|
|Frequency & Relative Frequency Tables: Definition & Examples||Calculate frequency and make relative frequency tables|
|Cumulative Frequency Tables: Definition, Uses & Examples||Learn how to make cumulative frequency tables|
|How to Calculate Percent Increase with Relative & Cumulative Frequency Tables||Use relative and cumulative frequency tables to calculate percent increase|
|Creating & Interpreting Histograms: Process & Examples||Learn how to create and interpret histograms|
1. Descriptive & Inferential Statistics: Definition, Differences & Examples
Descriptive and inferential statistics each give different insights into the nature of the data gathered. One alone cannot give the whole picture. Together, they provide a powerful tool for both description and prediction.
2. Difference between Populations & Samples in Statistics
Before you start collecting any information, it is important to understand the differences between population and samples. This lesson will show you how!
3. 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.
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. 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.
6. 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.
7. 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.
8. How to Calculate Mean, Median, Mode & Range
Measures of central tendency can provide valuable information about a set of data. In this lesson, explore how to calculate the mean, median, mode and range of any given data set.
9. 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.
10. Population & Sample Variance: Definition, Formula & Examples
Population and sample variance can help you describe and analyze data beyond the mean of the data set. In this lesson, learn the differences between population and sample variance.
11. Calculating the Standard Deviation
In this lesson, we will examine the meaning and process of calculating the standard deviation of a data set. Standard deviation can help to determine if the data set is a normal distribution.
12. 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.
13. Quartiles & the Interquartile Range: Definition, Formulate & Examples
Quartiles and the interquartile range can be used to group and analyze data sets. In this lesson, learn the definition and steps for finding the quartiles and interquartile range for a given data set.
14. Finding Percentiles in a Data Set: Formula & Examples
Percentiles are often used in academics to compare student scores. Finding percentiles in a data set can be a useful way to organize and compare numbers in a data set.
15. Frequency & Relative Frequency Tables: Definition & Examples
Frequency and relative frequency tables are a good way to visualize information. This is especially useful for information that is grouped into categories where you are looking for popularity or mode.
16. Cumulative Frequency Tables: Definition, Uses & Examples
Cumulative frequency tables can help you analyze and understand large amounts of information. In this lesson, you practice creating and interpreting cumulative frequency tables.
17. How to Calculate Percent Increase with Relative & Cumulative Frequency Tables
In statistics, one way to describe and analyze data is by using frequency tables. This lesson will discuss relative and cumulative frequencies and how to calculate percent increase using these two methods.
18. Creating & Interpreting Histograms: Process & Examples
Creating histograms can help you easily identify and interpret data. This lesson will give you several examples to better understand histograms and how to create them.
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