Ch 8: Praxis I Math: Data & Statistics

About This Chapter

This chapter provides an overview of data and statistics, including reading different types of graphs. The lessons correspond with questions you'll find on the Praxis Core Academic Skills for Educators.

Praxis I Math: Data and Statistics - Chapter Summary

This chapter helps you read and understand bar graphs, pie charts, and line graphs in preparation for the Praxis I Math exam. It also covers the topic of probability and how to calculate the different types. At the end of this chapter, you should be able to do the following:

  • Interpret and create scatterplots
  • Calculate standard deviation
  • Find measures of central tendency; i.e. mean, median, and mode
  • Define and measure simple random samples
  • Interpret linear relationships using data
  • Write and evaluate real-life linear models
  • Define and explain the difference between correlation and causation
  • Calculate simple conditional probabilities, the probability of permutations, and the probability of combinations

Use our mobile-friendly platform to access the lessons from any device. The convenient dashboard keeps track of your progress so you can pick up where you left off.

14 Lessons in Chapter 8: Praxis I Math: Data & Statistics
Test your knowledge with a 30-question chapter practice test
Understanding Bar Graphs and Pie Charts

1. Understanding Bar Graphs and Pie Charts

In this lesson, we will examine two of the most widely used types of graphs: bar graphs and pie charts. These two graphs can provide the reader with a comparison of the different data that is displayed.

Reading and Interpreting Line Graphs

2. Reading and Interpreting Line Graphs

Watch this video lesson to find out how useful line graphs can be and how much information you can gain just from looking at one. Learn how you can apply that information in your own life to help you make better decisions.

Creating & Interpreting Scatterplots: Process & Examples

3. Creating & Interpreting Scatterplots: Process & Examples

Scatterplots are a great visual representation of two sets of data. In this lesson, you will learn how to interpret bivariate data to create scatterplots and understand the relationship between the two variables.

Mean, Median & Mode: Measures of Central Tendency

4. 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.

Calculating the Standard Deviation

5. 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.

What are Center, Shape, and Spread?

6. 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.

What is Random Sampling? - Definition, Conditions & Measures

7. 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.

Simple Random Samples: Definition & Examples

8. 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!

Interpreting Linear Relationships Using Data: Practice Problems

9. Interpreting Linear Relationships Using Data: Practice Problems

Understanding linear relationships is an important part of understanding statistics. This lesson will help you review linear relationships and will go through three practice problems to help you retain your knowledge. When you are finished, test out your knowledge with a short quiz!

Writing & Evaluating Real-Life Linear Models: Process & Examples

10. Writing & Evaluating Real-Life Linear Models: Process & Examples

You make decisions about budgeting and other financial issues using linear models without even realizing it. Learn how to write and evaluate linear models.

Correlation vs. Causation: Differences & Definition

11. 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.

How to Calculate Simple Conditional Probabilities

12. How to Calculate Simple Conditional Probabilities

Conditional probability, just like it sounds, is a probability that happens on the condition of a previous event occurring. To calculate conditional probabilities, we must first consider the effects of the previous event on the current event.

How to Calculate the Probability of Permutations

13. 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.

How to Calculate the Probability of Combinations

14. 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.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
Not Taken
Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
Not Taken

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