About This Chapter
Who's It For?
Anyone who needs help learning or mastering statistics material will benefit from the lessons in this chapter. There is no faster or easier way to learn statistics. Among those who would benefit are:
- Students who have fallen behind in understanding statistics
- Students who struggle with learning disabilities or learning differences, including autism and ADHD
- Students who prefer multiple ways of learning math (visual or auditory)
- Students who have missed class time and need to catch up
- Students who need an efficient way to learn about statistics
- Students who struggle to understand their teachers
- Students who attend schools without extra math learning resources
How It Works:
- Find videos in our course that cover what you need to learn or review.
- Press play and watch the video lesson.
- Refer to the video transcripts to reinforce your learning.
- Test your understanding of each lesson with short quizzes.
- Verify you're ready by completing the statistics overview chapter exam.
Why It Works:
- Study Efficiently: Skip what you know, review what you don't.
- Retain What You Learn: Engaging animations and real-life examples make topics easy to grasp.
- Be Ready on Test Day: Use the statistics overview chapter exam to be prepared.
- Get Extra Support: Ask our subject-matter experts any statistics question. They're here to help!
- Study With Flexibility: Watch videos on any web-ready device.
Students Will Review:
This chapter helps students review the concepts in a statistics overview unit of a standard contemporary math course. Topics covered include:
- Descriptive and inferential statistics
- Difference between populations and samples
- Types and conditions of random samples
- Calculating mean, median, mode and range
- Standard deviation
- Data sets
- Using statistics to estimate parameters
- Categorical and quantitative data
- Discrete and continuous data
- Compare and contrast experiments and observational studies
- Bias and confounding in statistical analysis
- Standard deviation
- Relative and cumulative frequency tables
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.
19. Degrees of Freedom: Definition, Formula & Example
Degrees of freedom is a mathematical equation used primarily in statistics, but also in mechanics, physics, and chemistry. In this lesson, explore how degrees of freedom can be used in statistics to determine if results are significant.
20. Restriction of Range: Definition & Examples
In this lesson, we'll use real-life examples and charts to learn about restriction of range, a statistical technique in which only part of the data available is used to find the connection between two variables or quantities.
21. Linear Regression Model: Definition, Equation & Example
Ever hear of the linear regression model? If you are confused about what linear regression is, come inside this less for an explanation, how it is used and calculated. Then test your new skills with a short quiz.
22. Misleading Graphs: Definition & Examples
In this lesson, you'll learn the definition of misleading graphs and explore some examples to see how and why they are produced. Following the lesson, you'll have a chance to test your own understanding of misleading graphs.
23. Misleading Statistics: Definition & Examples
In this lesson, you'll learn the definition of misleading statistics, including the different types and how to spot them. You'll also explore some hypothetical examples and have the chance to see how well you absorbed the material with a brief quiz.
24. Poisson Distribution: Definition, Formula & Examples
In this lesson, we'll use a hypothetical road trip and some other real-life examples to show you how to use the Poisson distribution, a formula for calculating the probability of events. Then, test what you learned with the quiz questions.
25. Population Variance: Definition, Formula & Example
Population variance can sometimes seem tricky, but after learning what it is and how to figure it out it's a breeze. This lesson will define population variance, teach you the formula and give you some practice.
26. Sample Variance: Definition, Formula & Example
Do you understand the difference between sample variance and population variance? This lesson will explain when to use sample variance and give an example calculation.
27. What is Data Distribution? - Definition & Types
In this lesson, you will learn the definition of a data distribution. We will look at examples and features of various types of data distributions. You can then take a brief quiz to see what you learned.
28. Application of Statistics in Daily Life
Statistics is a major mathematical component of our world that some depend on and others hardly give a passing glance. Statistics has significant value and is used in areas from government to big business. This lesson gives a brief introduction to this broad field.
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Other chapters within the Contemporary Math: Help and Review course
- Mathematical Reasoning & Problem-Solving: Help and Review
- How to Solve Word Problems: Help and Review
- Probability Overview: Help and Review
- Understanding Discrete Probability Distributions: Help and Review
- The Normal Curve & Continuous Probability Distributions: Help and Review
- The Mathematics of Voting: Help and Review
- The Mathematics of Apportionment: Help and Review
- Graph Theory: Help and Review
- Operations with Basic Algebraic Expressions
- Conics in Algebra
- Algebraic Concepts of Groups & Sets
- Notation, Sequences & Series
- Matrices and Determinants in Algebra
- Fractions, Decimals & Mixed Numbers
- Approaches to Math Word Problems
- Performing Basic Arithmetic
- Operations with Monomials and Polynomials
- Number Line & the Coordinate Graph